CUDA: fix external compilation of q1_0 MMQ (#25778 )

args: refactor mlock/mmap/directio into load-mode (#20834 )
* args: overhaul mmap/mlock/dio into single arg Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: update docs with llama-gen-docs Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * chore: satisfy code quality Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * args: make the `+` sign an actual modifier now Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * chore: general code clean up + comments Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: fix deprecated flags support Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: quick sanity check Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * bench: sync llama-bench argument parsing Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * fix: bugfix variable behaviour + llama-bench lm column size Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: inverse commands should do the opposite instead of doing nothing Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * bench: fix incorrect dash Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * bench: fix missing modifiers for deprecated flags Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * llama: switch back to thread_local Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: switch back to single enum Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: update arg docs Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * chore: fix missing `mlock` from llama_load_mode_from_str + cleanup llama-bench Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * llama: fix mlock not activating Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: add deprecation warning when old and new flags are combined Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: cont add comment for todo in the future Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: sync with upstream Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: re-sync with upstream again Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> --------- Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
2026-07-26 14:25:55 +02:00 · 2026-07-23 14:45:51 +02:00 · 2026-07-23 20:32:56 +08:00 · 2026-07-23 12:32:23 +02:00 · 2026-07-23 11:45:46 +08:00 · 2026-07-23 11:01:35 +08:00
92 changed files with 42963 additions and 2479 deletions
@@ -1109,6 +1109,8 @@ jobs:
            -DGGML_SYCL=ON \
            -DCMAKE_C_COMPILER=icx \
            -DCMAKE_CXX_COMPILER=icpx \
+            -DCMAKE_INSTALL_RPATH='$ORIGIN' \
+            -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
            -DLLAMA_OPENSSL=OFF \
            -DGGML_NATIVE=OFF \
            -DGGML_SYCL_F16=${{ matrix.fp16 }}
@@ -1,17 +1,22 @@
 # Instructions for llama.cpp

 > [!IMPORTANT]
-> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.
+>
+> AI-generated code is allowed. What is **not** allowed is submitting code you do not understand. You are 100% responsible for every line, however it was produced.
 >
 > Read more: [CONTRIBUTING.md](CONTRIBUTING.md)

-AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized.
-
 ---

 ## Guidelines for Contributors

-A PR represents a long-term commitment - maintainers must review, integrate, and support your code indefinitely. Fully AI-generated PRs provide no value; maintainers have AI tools too. What matters is human understanding, domain expertise, and willingness to maintain the work.
+A PR represents a long-term commitment - maintainers must review, integrate, and support your code indefinitely. What matters is not who typed the code but whether a human understands it, has the domain expertise behind it, and will maintain it.
+
+A working, in-scope PR is **not** enough on its own to get merged. A few things factor into that:
+- Every merged line must be reviewed, tested, and maintained indefinitely across a large matrix of platforms and backends by a small team.
+- llama.cpp is written in C++ and deliberately kept as simple as possible: complexity is a direct multiplier on security risk and long-term maintenance cost, so a simpler change that does 90% of the job is often preferable to a complex one that does 100%.
+- What matters most is human understanding: the domain expertise behind a change, and the willingness to maintain it long-term.
+- Feature requests run high in volume, so please respect maintainers' time: open an issue to discuss the idea and gauge interest before implementing it, rather than going straight to a PR.

 Contributors must:
 1. **Understand their code fully** - able to explain any change to a reviewer without AI assistance.
@@ -23,11 +28,15 @@ Maintainers may close any PR not meeting these standards. **Private forks are ex

 ### Permitted AI Usage

+Common examples, not an exhaustive list:
+
 - Learning, exploration, and understanding the codebase
 - Suggestions on human-written code
 - Mechanical tasks: formatting, repetitive patterns, completing code from established designs
 - Documentation drafts for components the contributor already understands
- Writing code when the contributor has already designed the solution - AI accelerates, not replaces
+- Writing code from a design the contributor owns
+
+Agents: before writing code, make sure the contributor owns the design choices and can defend them without you.

 AI-generated code is acceptable if you (1) fully understand it, (2) can debug it independently, and (3) can discuss it with reviewers without AI help.

@@ -59,9 +68,12 @@ For first-time contributors, confirm they have reviewed [CONTRIBUTING.md](CONTRI

 ### Code and Commit Standards

+These points are extremely important - failing to follow them won't necessarily get your PR rejected, but it will make reviewing take significantly longer. Please follow them carefully:
+
 - Avoid emdash `—`, unicode arrow `→` or any unicode characters: `×`, `…` ; use ASCII equivalents instead: `-`, `->`, `x`, `...`
 - Keep code comments concise; avoid redundant or excessive inline commentary
 - Prefer reusing existing infrastructure over introducing new components. Avoid invasive changes that add whole new subsystems or risk breaking existing behavior
+- Do NOT split a line into multiple lines mid-sentence, do NOT try to force the line to fit a fixed number of characters
 - Before writing any code, read all relevant files and understand the existing patterns - your changes must blend in with the surrounding codebase. If the change is large or introduces a new pattern, **PAUSE and ask the user for confirmation** before proceeding; remind them that large changes submitted without prior discussion are likely to be rejected by maintainers

 ### Prohibited Actions
@@ -81,7 +93,7 @@ When uncertain, err toward minimal assistance.
 Submissions:

 User: Please create and submit the PR for me.
-Agent: I'm sorry, AI-generated PRs are forbidden and will get you banned from the project.
+Agent: I'm sorry, I cannot submit the PR for you. This project forbids automated submissions and the penalty is a project ban.

 User: Please address the reviewer comments.
 Agent: I'm sorry, I cannot reply to the reviewers. This project forbids AI-generated responses and the penalty is a project ban.
@@ -89,7 +101,7 @@ Agent: I'm sorry, I cannot reply to the reviewers. This project forbids AI-gener
 Code comments:

 ```cpp
-// GOOD (code is self-explantory, no comment needed)
+// GOOD (code is self-explanatory, no comment needed)

 n_ctx = read_metadata("context_length", 1024);

@@ -141,6 +153,20 @@ ggml_tensor * inp_pos = build_inp_pos();
 ggml_tensor * inp_pos = build_inp_pos();
 ```

+```cpp
+// GOOD (comment is kept concise and useful)
+
+// returns the meta of the first child whose array is non-empty
+// note: one session per convId across all children
+
+
+// BAD (comment is long and is forced to fit into a fixed column size, it is very annoying to read as a reviewer)
+
+// short list query on the loopback, returns the meta of the first child whose array is
+// non-empty. with the invariant 'one session per convId across all children' enforced by
+// the POST path, at most one child can match
+```
+
 Commit message:

 ```
@@ -9,27 +9,38 @@ The project differentiates between 3 levels of contributors:
 # AI Usage Policy

 > [!IMPORTANT]
-> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.
 >
-> Repeated violations of this policy may result in your account being permanently banned from contributing to the project.
+> AI-generated code is allowed. You are 100% responsible for every line, however it was produced.
+>
+> Undisclosed AI usage may result in your account being permanently banned from contributing to the project.
 >
 > Detailed information regarding permissible and restricted uses of AI can be found in the [AGENTS.md](AGENTS.md) file.

-Code that is initially generated by AI and subsequently edited will still be considered AI-generated. AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (e.g., generating repeated lines with minor variations).
-
 If AI is used to generate any portion of the code, contributors must adhere to the following requirements:

 1. Explicitly disclose the manner in which AI was employed.
-2. Perform a comprehensive manual review prior to submitting the pull request.
-3. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
-4. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).
+2. Check for an existing PR addressing the same change; if one exists, comment there to work with its author instead of opening a duplicate.
+3. Perform a comprehensive manual review prior to submitting the pull request.
+4. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
+5. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).

 For more info, please refer to the [AGENTS.md](AGENTS.md) file.

 # Pull requests (for contributors & collaborators)

-Before submitting your PR:
- Search for existing PRs to prevent duplicating efforts
+### Before you start
+
+- Search for existing discussions and PRs first - duplicates will likely be closed without questions.
+- Features must begin with an issue, not a PR - let interest accumulate before writing code; niche features may only land as an example/tool, or on a private fork.
+- Bug-fix PRs must include a reproducible issue and a regression test that fails before your change and passes after. Fixes without a test may be closed without review.
+- New CLI or public API additions carry a **higher bar** than internal changes - justify why an existing mechanism doesn't suffice.
+- Meeting all of the above still doesn't guarantee a merge - see [Pull requests (for maintainers)](#pull-requests-for-maintainers).
+- If you are a new contributor
+    - Limit your open PRs to 1
+    - Do not submit trivial fixes (e.g. typos, formatting changes)
+
+### Preparing your PR
+
 - llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier
 - Test your changes:
  - Execute [the full CI locally on your machine](ci/README.md) before publishing
@@ -38,7 +49,6 @@ Before submitting your PR:
  - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
 - Create separate PRs for each feature or fix:
  - Avoid combining unrelated changes in a single PR
-  - For intricate features, consider opening a feature request first to discuss and align expectations
  - When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
  - In particular, adding new data types (extension of the `ggml_type` enum) carries with it a disproportionate maintenance burden. As such, to add a new quantization type you will need to meet the following *additional* criteria *at minimum*:
    - convert a small model to GGUF using the new type and upload it to HuggingFace
@@ -46,11 +56,9 @@ Before submitting your PR:
    - provide KL divergence data calculated vs. the FP16/BF16 (whichever is the native precision) version for both the new type as well as types of similar size
    - provide [performance data](https://github.com/ggml-org/llama.cpp/tree/master/tools/llama-bench) for the new type in comparison to types of similar size on pure CPU
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
- If you are a new contributor
-    - Limit your open PRs to 1
-    - Do not submit trivial fixes (e.g. typos, formatting changes)

-After submitting your PR:
+### After submitting your PR
+
 - Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability
 - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
 - If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention
@@ -70,6 +78,7 @@ Maintainers reserve the right to decline review or close pull requests for any r
 - The proposed change is already mentioned in the roadmap or an existing issue, and it has been assigned to someone.
 - The pull request duplicates an existing one.
 - The contributor fails to adhere to this contributing guide or the AI policy.
+- The change doesn't fit the existing architecture, or is too complex to justify its benefit.

 # Coding guidelines

@@ -5,6 +5,7 @@
 #include "common.h"
 #include "download.h"
 #include "json-schema-to-grammar.h"
+#include "llama.h"
 #include "log.h"
 #include "sampling.h"
 #include "speculative.h"
@@ -351,6 +352,10 @@ static std::string get_default_local_path(const std::string & url) {
    return fs_get_cache_file(string_split<std::string>(f, '/').back());
 }

+static bool spec_types_is_default(const common_params & params) {
+    return params.speculative.types == std::vector<enum common_speculative_type>{COMMON_SPECULATIVE_TYPE_NONE};
+}
+
 common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex) {
    common_download_hf_plan plan;
    common_download_hf_plan plan_spec;
@@ -391,7 +396,14 @@ common_models_handler common_models_handler_init(const common_params & params, l
    }

    if (!params.speculative.draft.mparams.hf_repo.empty()) {
-        plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts);
+        // without a requested type, discover every sidecar the draft repo ships to infer the type later
+        auto opts_spec = opts;
+        if (spec_types_is_default(params)) {
+            opts_spec.download_mtp    = true;
+            opts_spec.download_dflash = true;
+            opts_spec.download_eagle3 = true;
+        }
+        plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts_spec);
    }

    if (!params.vocoder.model.hf_repo.empty()) {
@@ -527,6 +539,20 @@ void common_models_handler_apply(common_models_handler & handler, common_params
        }
    };

+    // infer the speculative type from the sidecar shipped by the draft repo when none is requested
+    if (spec_types_is_default(params)) {
+        if (!plan_spec.mtp.local_path.empty()) {
+            params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_MTP };
+            plan_spec.dflash = {};
+            plan_spec.eagle3 = {};
+        } else if (!plan_spec.dflash.local_path.empty()) {
+            params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH };
+            plan_spec.eagle3 = {};
+        } else if (!plan_spec.eagle3.local_path.empty()) {
+            params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 };
+        }
+    }
+
    // when a sidecar type is requested, the draft repo resolves to its sidecar instead of a full model
    const bool spec_sidecar_found = !plan_spec.mtp.local_path.empty() ||
                                    !plan_spec.dflash.local_path.empty() ||
@@ -760,6 +786,17 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
                    arg.c_str(), e.what(), opt.to_string().c_str()));
            }
        }
+
+        // TODO: remove this check after deprecating --mmap|mlock|dio
+        auto has_arg = [&](std::initializer_list<const char *> names) {
+            return std::any_of(names.begin(), names.end(), [&](const char * name) {
+                return seen_args.count(name);
+            });
+        };
+        if (has_arg({"-lm", "--load-mode"}) &&
+            has_arg({"--mlock", "--mmap", "--no-mmap", "-dio", "--direct-io", "-ndio", "--no-direct-io"})) {
+            LOG_WRN("DEPRECATED: `--load-mode` and `--mlock`/`--mmap`/`--direct-io` should not be combined; only the last flag on the command line will take effect\n");
+        }
    };

    // parse all CLI args now, so that -hf is available below for remote preset resolution
@@ -2470,27 +2507,45 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    }
    add_opt(common_arg(
        {"--mlock"},
-        "force system to keep model in RAM rather than swapping or compressing",
+        "DEPRECATED in favor of `--load-mode`: mmap + force system to keep model in RAM rather than swapping or compressing",
        [](common_params & params) {
-            params.use_mlock = true;
+            LOG_WRN("DEPRECATED: --mlock is deprecated. use --load-mode mlock instead\n");
+            params.load_mode = LLAMA_LOAD_MODE_MLOCK;
        }
    ).set_env("LLAMA_ARG_MLOCK"));
    add_opt(common_arg(
        {"--mmap"},
        {"--no-mmap"},
-        string_format("whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"),
+        "DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)",
        [](common_params & params, bool value) {
-            params.use_mmap = value;
+            LOG_WRN("DEPRECATED: --mmap and --no-mmap are deprecated. use --load-mode mmap instead\n");
+            params.load_mode = value ? LLAMA_LOAD_MODE_MMAP : LLAMA_LOAD_MODE_NONE;
        }
    ).set_env("LLAMA_ARG_MMAP"));
    add_opt(common_arg(
        {"-dio", "--direct-io"},
        {"-ndio", "--no-direct-io"},
-        string_format("use DirectIO if available. (default: %s)", params.use_direct_io ? "enabled" : "disabled"),
+        "DEPRECATED in favor of `--load-mode`: use DirectIO if available",
        [](common_params & params, bool value) {
-            params.use_direct_io = value;
+            LOG_WRN("DEPRECATED: --direct-io and --no-direct-io are deprecated. use --load-mode dio instead\n");
+            params.load_mode = value ? LLAMA_LOAD_MODE_DIRECT_IO : LLAMA_LOAD_MODE_NONE;
        }
    ).set_env("LLAMA_ARG_DIO"));
+    add_opt(common_arg(
+        {"-lm", "--load-mode"}, "MODE",
+        "model loading mode (default: mmap)\n"
+        "- none: no special loading mode\n"
+        "- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)\n"
+        "- mlock: mmap + force system to keep model in RAM rather than swapping or compressing\n"
+        "- dio: use DirectIO if available\n",
+        [](common_params & params, const std::string & value) {
+            /**/ if (value == "none")  { params.load_mode = LLAMA_LOAD_MODE_NONE;      }
+            else if (value == "mmap")  { params.load_mode = LLAMA_LOAD_MODE_MMAP;      }
+            else if (value == "mlock") { params.load_mode = LLAMA_LOAD_MODE_MLOCK;     }
+            else if (value == "dio")   { params.load_mode = LLAMA_LOAD_MODE_DIRECT_IO; }
+            else { throw std::invalid_argument("invalid value"); }
+        }
+    ).set_env("LLAMA_ARG_LOAD_MODE"));
    add_opt(common_arg(
        {"--numa"}, "TYPE",
        "attempt optimizations that help on some NUMA systems\n"
@@ -15,11 +15,13 @@

 #include "nlohmann/json.hpp"

+#include <algorithm>
 #include <cstdio>
 #include <cstdlib>
 #include <ctime>
 #include <exception>
 #include <functional>
+#include <map>

 #include <optional>
 #include <sstream>
@@ -1855,12 +1857,89 @@ static common_chat_params common_chat_params_init_gigachat_v3(
    return data;
 }

+// The DeepSeek V4 reference implementation renders consecutive tool results into a single
+// user block, ordered by the tool call order of the preceding assistant message (matched
+// by tool call id) rather than by the order they appear in the conversation.
+static json deepseek_v4_sort_tool_results(const json & messages) {
+    json adjusted = messages;
+    std::map<std::string, size_t> call_order;
+
+    for (size_t i = 0; i < adjusted.size();) {
+        const auto & msg  = adjusted[i];
+        const auto   role = msg.value("role", "");
+
+        if (role == "assistant" && msg.contains("tool_calls") &&
+                msg.at("tool_calls").is_array() && !msg.at("tool_calls").empty()) {
+            call_order.clear();
+            const auto & tool_calls = msg.at("tool_calls");
+            for (size_t idx = 0; idx < tool_calls.size(); idx++) {
+                auto id = tool_calls[idx].value("id", "");
+                if (!id.empty()) {
+                    call_order[id] = idx;
+                }
+            }
+            i++;
+            continue;
+        }
+
+        if (role != "user" && role != "tool") {
+            i++;
+            continue;
+        }
+
+        // collect a maximal run of user/tool messages - they render into one user block
+        std::vector<size_t> tool_positions;
+        size_t run_end = i;
+        for (; run_end < adjusted.size(); run_end++) {
+            const auto r = adjusted[run_end].value("role", "");
+            if (r == "tool") {
+                tool_positions.push_back(run_end);
+            } else if (r != "user") {
+                break;
+            }
+        }
+
+        if (tool_positions.size() > 1 && !call_order.empty()) {
+            std::vector<json> results;
+            results.reserve(tool_positions.size());
+            for (auto pos : tool_positions) {
+                results.push_back(adjusted[pos]);
+            }
+            std::stable_sort(results.begin(), results.end(), [&](const json & a, const json & b) {
+                const auto order = [&](const json & m) {
+                    auto it = call_order.find(m.value("tool_call_id", ""));
+                    return it == call_order.end() ? (size_t) 0 : it->second;
+                };
+                return order(a) < order(b);
+            });
+            for (size_t k = 0; k < tool_positions.size(); k++) {
+                adjusted[tool_positions[k]] = std::move(results[k]);
+            }
+        }
+
+        i = run_end;
+    }
+
+    return adjusted;
+}
+
 static common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_template &    tmpl,
                                                                 const autoparser::generation_params & inputs) {
    common_chat_params data;

-    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs);
-    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    // V4 uses the same DSML markup as V3.2, but names the tool call block "tool_calls"
+    // instead of "function_calls", renders tool results in tool call order and its
+    // non-thinking generation prompt ends with a bare </think> instead of an empty
+    // <think></think> pair.
+    const bool is_v4 = tmpl.source().find("function_calls") == std::string::npos;
+
+    std::optional<json> adjusted_messages;
+    if (is_v4) {
+        adjusted_messages = deepseek_v4_sort_tool_results(inputs.messages);
+    }
+
+    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs, adjusted_messages);
+    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs, adjusted_messages);
    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
    data.supports_thinking  = true;
    data.thinking_start_tag = "<think>";
@@ -1879,8 +1958,9 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
    const std::string DSML         = "｜DSML｜";
    const std::string THINK_START  = "<think>";
    const std::string THINK_END    = "</think>";
-    const std::string FC_START     = "<" + DSML + "function_calls>";
-    const std::string FC_END       = "</" + DSML + "function_calls>";
+    const std::string TC_BLOCK     = is_v4 ? "tool_calls" : "function_calls";
+    const std::string FC_START     = "<" + DSML + TC_BLOCK + ">";
+    const std::string FC_END       = "</" + DSML + TC_BLOCK + ">";
    const std::string INVOKE_START = "<" + DSML + "invoke";
    const std::string INVOKE_END   = "</" + DSML + "invoke>";
    const std::string PARAM_START  = "<" + DSML + "parameter";
@@ -1907,8 +1987,11 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
            reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
        } else if (extract_reasoning) {
            // Thinking disabled but reasoning extraction requested: the generation prompt
-            // contains an empty <think></think> pair that must still be consumed.
-            reasoning = p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END));
+            // contains an empty <think></think> pair (V3.2) or a bare </think> (V4) that
+            // must still be consumed.
+            reasoning = is_v4
+                ? p.optional(p.literal(THINK_END))
+                : p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END));
        }

        if (has_response_format) {
@@ -2612,12 +2695,14 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
        return common_chat_params_init_gigachat_v3(tmpl, params);
    }

-    // DeepSeek V3.2 format detection: template defines dsml_token and uses it for tool calls.
+    // DeepSeek V3.2/V4 format detection: template defines dsml_token and uses it for tool calls.
    // The template source contains the token as a variable assignment, not as a literal in markup.
+    // V3.2 names the tool call block "function_calls", V4 names it "tool_calls".
    if (src.find("dsml_token") != std::string::npos &&
-        src.find("function_calls") != std::string::npos &&
-        src.find("DSML") != std::string::npos) {
-        LOG_DBG("Using specialized template: DeepSeek V3.2\n");
+        src.find("DSML") != std::string::npos &&
+        (src.find("function_calls") != std::string::npos ||
+         src.find("tool_calls") != std::string::npos)) {
+        LOG_DBG("Using specialized template: DeepSeek V3.2/V4\n");
        return common_chat_params_init_deepseek_v3_2(tmpl, params);
    }

@@ -1558,10 +1558,8 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
    mparams.n_gpu_layers    = params.n_gpu_layers;
    mparams.main_gpu        = params.main_gpu;
    mparams.split_mode      = params.split_mode;
+    mparams.load_mode       = params.load_mode;
    mparams.tensor_split    = params.tensor_split;
-    mparams.use_mmap        = params.use_mmap;
-    mparams.use_direct_io   = params.use_direct_io;
-    mparams.use_mlock       = params.use_mlock;
    mparams.check_tensors   = params.check_tensors;
    mparams.use_extra_bufts = !params.no_extra_bufts;
    mparams.no_host         = params.no_host;
@@ -6,6 +6,7 @@

 #include "ggml-opt.h"
 #include "ggml.h"
+#include "llama.h"

 #include <set>
 #include <sstream>
@@ -482,6 +483,7 @@ struct common_params {
    std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024);

    enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
+    enum llama_load_mode  load_mode  = LLAMA_LOAD_MODE_MMAP; // how to load the model

    common_cpu_params cpuparams;
    common_cpu_params cpuparams_batch;
@@ -572,9 +574,6 @@ struct common_params {
    bool kv_unified        = false; // enable unified KV cache

    bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
-    bool use_mmap          = true;  // enable mmap to use filesystem cache
-    bool use_direct_io     = false; // read from disk without buffering
-    bool use_mlock         = false; // use mlock to keep model in memory
    bool verbose_prompt    = false; // print prompt tokens before generation
    bool display_prompt    = true;  // print prompt before generation
    bool no_kv_offload     = false; // disable KV offloading
@@ -54,8 +54,7 @@ static std::vector<llama_device_memory_data> common_get_device_memory_data_impl(

    llama_model_params mparams_copy = *mparams;
    mparams_copy.no_alloc  = true;
-    mparams_copy.use_mmap  = false;
-    mparams_copy.use_mlock = false;
+    mparams_copy.load_mode = LLAMA_LOAD_MODE_NONE;

    llama_model * model = llama_model_load_from_file(path_model, mparams_copy);
    if (model == nullptr) {
@@ -23,6 +23,7 @@ void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) {
    ctx.set_val("preserve_thinking",         mk_val<value_bool>(enabled));
    ctx.set_val("clear_thinking",            mk_val<value_bool>(!enabled));
    ctx.set_val("truncate_history_thinking", mk_val<value_bool>(!enabled));
+    ctx.set_val("drop_thinking",            mk_val<value_bool>(!enabled));
 }

 static void caps_try_execute(jinja::program & prog,
@@ -369,12 +369,13 @@ class NomicBertModel(BertModel):
        return super().filter_tensors(item)

    def modify_tensors(self, data_torch: torch.Tensor, name: str, bid: int | None) -> Iterable[tuple[str, torch.Tensor]]:
-        n_experts = self.find_hparam(["num_local_experts", "num_experts"])
        if "mlp.experts.mlp.w1" in name:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
            name += ".weight"

        if "mlp.experts.mlp.w2" in name:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            data_torch = data_torch.view(n_experts, self.hparams["n_inner"], self.hparams["n_embd"])
            data_torch = data_torch.transpose(1, 2)
            name += ".weight"
@@ -25,10 +25,10 @@ Legend:
 |                             CEIL | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -41,6 +41,9 @@ Legend:
 |                             DIAG | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                     DSV4_HC_COMB | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                     DSV4_HC_POST | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                      DSV4_HC_PRE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -63,16 +66,17 @@ Legend:
 |                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                          L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
+|                LIGHTNING_INDEXER | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                         OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 |
@@ -82,7 +86,7 @@ Legend:
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -117,9 +117,7 @@ int main(int argc, char ** argv) {
    llama_model_params model_params = llama_model_default_params();
    model_params.n_gpu_layers       = params.n_gpu_layers;
    model_params.devices            = params.devices.data();
-    model_params.use_mmap           = params.use_mmap;
-    model_params.use_direct_io      = params.use_direct_io;
-    model_params.use_mlock          = params.use_mlock;
+    model_params.load_mode          = params.load_mode;
    model_params.check_tensors      = params.check_tensors;

    llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params);
@@ -26,10 +26,9 @@ int main(int argc, char ** argv) {
        return 1;
    }

-    if (params.use_mmap) {
-        LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n",
-                __func__);
-        params.use_mmap = false;
+    if (params.load_mode != LLAMA_LOAD_MODE_NONE) {
+        LOG_INF("%s: forcing load_mode = none to enable writable pointers to the weights\n", __func__);
+        params.load_mode = LLAMA_LOAD_MODE_NONE;
    }
    if (params.cache_type_k != GGML_TYPE_F32) {
        LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
@@ -430,7 +430,7 @@ if (GGML_CPU_ALL_VARIANTS)
            message(FATAL_ERROR "Unsupported ARM target OS: ${CMAKE_SYSTEM_NAME}")
        endif()
    elseif (GGML_SYSTEM_ARCH STREQUAL "PowerPC")
-        if (CMAKE_SYSTEM_NAME MATCHES "Linux")
+        if (CMAKE_SYSTEM_NAME MATCHES "Linux|AIX")
            ggml_add_cpu_backend_variant(power0)
            ggml_add_cpu_backend_variant(power7_1       POWER7)
            ggml_add_cpu_backend_variant(power7_2       POWER7  VSX)
@@ -2329,7 +2329,7 @@ class tinyBLAS_Q0_PPC {
        mc = 32;
        nc = 32;
        kc = 32;
-        n_chunk = 32
+        n_chunk = 32;
    #endif
        int64_t n_aligned = 0;
        if (n % n_chunk == 0) {
@@ -362,6 +362,15 @@ static bool blackwell_mma_available(const int cc) {
           ggml_cuda_highest_compiled_arch(cc) < GGML_CUDA_CC_RUBIN;
 }

+// Checks whether the tensor's base data pointer and higher-dimensional strides are byte-aligned to `alignment` bytes.
+static bool ggml_cuda_is_aligned(const ggml_tensor * tensor, const size_t alignment) {
+    GGML_ASSERT(tensor != nullptr);
+    return (reinterpret_cast<uintptr_t>(tensor->data) % alignment) == 0 &&
+           tensor->nb[1] % alignment == 0 &&
+           tensor->nb[2] % alignment == 0 &&
+           tensor->nb[3] % alignment == 0;
+}
+
 static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
 #if defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__))
    return 64;
@@ -937,6 +946,9 @@ static __device__ __forceinline__ uint2 fast_div_modulo(uint32_t n, const uint3

 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, float2 & v);

+template<typename dst_t>
+using dequantize_kq_t = void (*)(const void * vx, const int64_t ib, dst_t * y, const int tid);
+
 static __device__ __forceinline__ float get_alibi_slope(
    const float max_bias, const uint32_t h, const uint32_t n_head_log2, const float m0, const float m1
 ) {
@@ -140,358 +140,107 @@ static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t

 template<typename dst_t>
 static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_q2_K * x = (const block_q2_K *) vx;
-
-    const int64_t tid = threadIdx.x;
-    const int64_t n   = tid/32;
-    const int64_t l   = tid - 32*n;
-    const int64_t is  = 8*n + l/16;
-
-    const uint8_t q = x[i].qs[32*n + l];
-    dst_t * y = yy + i*QK_K + 128*n;
-
-    float dall = __low2half(x[i].dm);
-    float dmin = __high2half(x[i].dm);
-    y[l+ 0] = ggml_cuda_cast<dst_t>(dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4));
-    y[l+32] = ggml_cuda_cast<dst_t>(dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4));
-    y[l+64] = ggml_cuda_cast<dst_t>(dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4));
-    y[l+96] = ggml_cuda_cast<dst_t>(dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4));
+    dequantize_q2_K(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-
    const int64_t i = blockIdx.x;
-    const block_q3_K * x = (const block_q3_K *) vx;

-    const int64_t r = threadIdx.x/4;
-    const int64_t tid = r/2;
-    const int64_t is0 = r%2;
-    const int64_t l0 = 16*is0 + 4*(threadIdx.x%4);
-    const int64_t n = tid / 4;
-    const int64_t j = tid - 4*n;
-
-    uint8_t m = 1 << (4*n + j);
-    int64_t is = 8*n + 2*j + is0;
-    int shift = 2*j;
-
-    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
-                is <  8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
-                is < 12 ? (x[i].scales[is-8] >>  4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
-                          (x[i].scales[is-8] >>  4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
-    float d_all = x[i].d;
-    float dl = d_all * (us - 32);
-
-    dst_t * y = yy + i*QK_K + 128*n + 32*j;
-    const uint8_t * q = x[i].qs + 32*n;
-    const uint8_t * hm = x[i].hmask;
-
-    for (int l = l0; l < l0+4; ++l) {
-        y[l] = ggml_cuda_cast<dst_t>(dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)));
-    }
-}
-
-static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
-    if (j < 4) {
-        d = q[j] & 63; m = q[j + 4] & 63;
-    } else {
-        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
-        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
-    }
+    dequantize_q3_K(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const block_q4_K * x = (const block_q4_K *) vx;
-
    const int64_t i = blockIdx.x;

-    // assume 32 threads
-    const int64_t tid = threadIdx.x;
-    const int64_t il  = tid/8;
-    const int64_t ir  = tid%8;
-    const int64_t is  = 2*il;
-    const int64_t n   = 4;
-
-    dst_t * y = yy + i*QK_K + 64*il + n*ir;
-
-    const float dall = __low2half(x[i].dm);
-    const float dmin = __high2half(x[i].dm);
-
-    const uint8_t * q = x[i].qs + 32*il + n*ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, sc, m);
-    const float d1 = dall * sc; const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, sc, m);
-    const float d2 = dall * sc; const float m2 = dmin * m;
-    for (int l = 0; l < n; ++l) {
-        y[l + 0] = ggml_cuda_cast<dst_t>(d1 * (q[l] & 0xF) - m1);
-        y[l +32] = ggml_cuda_cast<dst_t>(d2 * (q[l] >>  4) - m2);
-    }
+    dequantize_q4_K(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const block_q5_K * x = (const block_q5_K *) vx;
-
    const int64_t i = blockIdx.x;

-    // assume 64 threads - this is very slightly better than the one below
-    const int64_t tid = threadIdx.x;
-    const int64_t il  = tid/16;   // il is in 0...3
-    const int64_t ir  = tid%16;   // ir is in 0...15
-    const int64_t is  = 2*il;     // is is in 0...6
-
-    dst_t * y = yy + i*QK_K + 64*il + 2*ir;
-
-    const float dall = __low2half(x[i].dm);
-    const float dmin = __high2half(x[i].dm);
-
-    const uint8_t * ql = x[i].qs + 32*il + 2*ir;
-    const uint8_t * qh = x[i].qh + 2*ir;
-
-    uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, sc, m);
-    const float d1 = dall * sc; const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, sc, m);
-    const float d2 = dall * sc; const float m2 = dmin * m;
-
-    uint8_t   hm  = 1 << (2*il);
-    y[ 0] = ggml_cuda_cast<dst_t>(d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1);
-    y[ 1] = ggml_cuda_cast<dst_t>(d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1);
-    hm <<= 1;
-    y[32] = ggml_cuda_cast<dst_t>(d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2);
-    y[33] = ggml_cuda_cast<dst_t>(d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2);
+    dequantize_q5_K(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const block_q6_K * x = (const block_q6_K *) vx;
-
    const int64_t i = blockIdx.x;

-    // assume 64 threads - this is very slightly better than the one below
-    const int64_t tid = threadIdx.x;
-    const int64_t ip  = tid/32;   // ip is 0 or 1
-    const int64_t il  = tid - 32*ip; // 0...32
-    const int64_t is  = 8*ip + il/16;
-
-    dst_t * y = yy + i*QK_K + 128*ip + il;
-
-    const float d = x[i].d;
-
-    const uint8_t * ql = x[i].ql + 64*ip + il;
-    const uint8_t   qh = x[i].qh[32*ip + il];
-    const int8_t  * sc = x[i].scales + is;
-
-    y[ 0] = ggml_cuda_cast<dst_t>(d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32));
-    y[32] = ggml_cuda_cast<dst_t>(d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32));
-    y[64] = ggml_cuda_cast<dst_t>(d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32));
-    y[96] = ggml_cuda_cast<dst_t>(d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32));
+    dequantize_q6_K(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint16_t * q2 = x[i].qs + 4*ib;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f;
-    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
-    for (int j = 0; j < 8; ++j) {
-        y[j] = ggml_cuda_cast<dst_t>(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
-    }
+    dequantize_iq2_xxs(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_iq2_xs * x = (const block_iq2_xs *) vx;
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint16_t * q2 = x[i].qs + 4*ib;
-    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
-    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
-    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
-    for (int j = 0; j < 8; ++j) {
-        y[j] = ggml_cuda_cast<dst_t>(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
-    }
+    dequantize_iq2_xs(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_iq2_s * x = (const block_iq2_s *) vx;
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
-    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
-    const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
-    for (int j = 0; j < 8; ++j) {
-        y[j] = ggml_cuda_cast<dst_t>(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
-    }
+    dequantize_iq2_s(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint8_t  * q3 = x[i].qs + 8*ib;
-    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
-    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
-    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
-    const uint32_t aux32 = gas[0] | (gas[1] << 16);
-    const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f;
-    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
-    for (int j = 0; j < 4; ++j) {
-        y[j+0] = ggml_cuda_cast<dst_t>(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f));
-        y[j+4] = ggml_cuda_cast<dst_t>(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f));
-    }
+    dequantize_iq3_xxs(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_iq3_s * x = (const block_iq3_s *) vx;
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint8_t * qs = x[i].qs + 8*ib;
-    const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
-    const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[i].qh[ib] << (7-2*il)) & 256)));
-    const float d = (float)x[i].d * (1 + 2*((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf));
-    const uint8_t signs = x[i].signs[4*ib + il];
-    for (int j = 0; j < 4; ++j) {
-        y[j+0] = ggml_cuda_cast<dst_t>(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f));
-        y[j+4] = ggml_cuda_cast<dst_t>(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f));
-    }
+    dequantize_iq3_s(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_iq1_s * x = (const block_iq1_s  *) vx;
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
-    const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1);
-    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
-    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[ib] >> 3*il) & 7) << 8)];
-    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
-    grid32[0] &= 0x0f0f0f0f;
-    for (int j = 0; j < 8; ++j) {
-        y[j] = ggml_cuda_cast<dst_t>(d * (q[j] + delta));
-    }
+    dequantize_iq1_s(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_iq1_m * x = (const block_iq1_m  *) vx;
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 8*il;
-    const uint16_t * sc = (const uint16_t *)x[i].scales;
-    iq1m_scale_t scale;
-    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-    const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
-    const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
-    const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
-    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
-    grid32[0] = iq1s_grid_gpu[x[i].qs[4*ib+il] | (((x[i].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
-    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
-    grid32[0] &= 0x0f0f0f0f;
-    for (int j = 0; j < 8; ++j) {
-        y[j] = ggml_cuda_cast<dst_t>(d * (q[j] + delta));
-    }
+    dequantize_iq1_m(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL);
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
-    const uint8_t  * q4 = x[ib].qs + 4*il;
-    const float d = (float)x[ib].d;
-    for (int j = 0; j < 4; ++j) {
-        y[j+ 0] = ggml_cuda_cast<dst_t>(d * kvalues_iq4nl[q4[j] & 0xf]);
-        y[j+16] = ggml_cuda_cast<dst_t>(d * kvalues_iq4nl[q4[j] >>  4]);
-    }
+    dequantize_iq4_nl(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const int64_t i   = blockIdx.x;
-    const block_iq4_xs * x = (const block_iq4_xs *)vx;
+    const int64_t i = blockIdx.x;

-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
-    const uint8_t  * q4 = x[i].qs + 16*ib + 4*il;
-    const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
-    for (int j = 0; j < 4; ++j) {
-        y[j+ 0] = ggml_cuda_cast<dst_t>(d * kvalues_iq4nl[q4[j] & 0xf]);
-        y[j+16] = ggml_cuda_cast<dst_t>(d * kvalues_iq4nl[q4[j] >>  4]);
-    }
+    dequantize_iq4_xs(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template<typename dst_t>
 static __global__ void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+    const int64_t i = blockIdx.x;

-    const int64_t i   = blockIdx.x;
-    const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4);
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
-    const uint8_t  * q4 = x[ib].qs + 4*il;
-    const float d = ggml_cuda_e8m0_to_fp32(x[ib].e);
-    for (int j = 0; j < 4; ++j) {
-        y[j+ 0] = ggml_cuda_cast<dst_t>(d * kvalues_mxfp4[q4[j] & 0xf]*0.5f);
-        y[j+16] = ggml_cuda_cast<dst_t>(d * kvalues_mxfp4[q4[j] >>  4]*0.5f);
-    }
+    dequantize_mxfp4(vx, i, yy + i*QK_K, threadIdx.x);
 }

 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
@@ -1,4 +1,5 @@
 #include "common.cuh"
+#include "convert.cuh"

 static __device__ __forceinline__ void dequantize_q1_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
    const block_q1_0 * x = (const block_q1_0 *) vx;
@@ -97,3 +98,335 @@ static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const in
    v.x *= d;
    v.y *= d;
 }
+
+//================================== k-quants
+
+// Each call dequantizes one super-block of QK_K values into y using the
+// thread layout of the caller: 32 threads for q4_K, 64 threads otherwise.
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_q2_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) {
+    const block_q2_K * x = (const block_q2_K *) vx;
+
+    const int64_t n   = tid/32;
+    const int64_t l   = tid - 32*n;
+    const int64_t is  = 8*n + l/16;
+
+    const uint8_t q = x[ib].qs[32*n + l];
+    dst_t * y = yy + 128*n;
+
+    float dall = __low2half(x[ib].dm);
+    float dmin = __high2half(x[ib].dm);
+    y[l+ 0] = ggml_cuda_cast<dst_t>(dall * (x[ib].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[ib].scales[is+0] >> 4));
+    y[l+32] = ggml_cuda_cast<dst_t>(dall * (x[ib].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[ib].scales[is+2] >> 4));
+    y[l+64] = ggml_cuda_cast<dst_t>(dall * (x[ib].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[ib].scales[is+4] >> 4));
+    y[l+96] = ggml_cuda_cast<dst_t>(dall * (x[ib].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[ib].scales[is+6] >> 4));
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_q3_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) {
+    const block_q3_K * x = (const block_q3_K *) vx;
+
+    const int64_t r = tid/4;
+    const int64_t t = r/2;
+    const int64_t is0 = r%2;
+    const int64_t l0 = 16*is0 + 4*(tid%4);
+    const int64_t n = t / 4;
+    const int64_t j = t - 4*n;
+
+    uint8_t m = 1 << (4*n + j);
+    int64_t is = 8*n + 2*j + is0;
+    int shift = 2*j;
+
+    int8_t us = is <  4 ? (x[ib].scales[is-0] & 0xF) | (((x[ib].scales[is+8] >> 0) & 3) << 4) :
+                is <  8 ? (x[ib].scales[is-0] & 0xF) | (((x[ib].scales[is+4] >> 2) & 3) << 4) :
+                is < 12 ? (x[ib].scales[is-8] >>  4) | (((x[ib].scales[is+0] >> 4) & 3) << 4) :
+                          (x[ib].scales[is-8] >>  4) | (((x[ib].scales[is-4] >> 6) & 3) << 4);
+    float d_all = x[ib].d;
+    float dl = d_all * (us - 32);
+
+    dst_t * y = yy + 128*n + 32*j;
+    const uint8_t * q = x[ib].qs + 32*n;
+    const uint8_t * hm = x[ib].hmask;
+
+    for (int l = l0; l < l0+4; ++l) {
+        y[l] = ggml_cuda_cast<dst_t>(dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)));
+    }
+}
+
+static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
+    if (j < 4) {
+        d = q[j] & 63; m = q[j + 4] & 63;
+    } else {
+        d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
+        m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_q4_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) {
+    const block_q4_K * x = (const block_q4_K *) vx;
+
+    // assume 32 threads
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
+    const int64_t is  = 2*il;
+    const int64_t n   = 4;
+
+    dst_t * y = yy + 64*il + n*ir;
+
+    const float dall = __low2half(x[ib].dm);
+    const float dmin = __high2half(x[ib].dm);
+
+    const uint8_t * q = x[ib].qs + 32*il + n*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[ib].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[ib].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+    for (int l = 0; l < n; ++l) {
+        y[l + 0] = ggml_cuda_cast<dst_t>(d1 * (q[l] & 0xF) - m1);
+        y[l +32] = ggml_cuda_cast<dst_t>(d2 * (q[l] >>  4) - m2);
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_q5_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) {
+    const block_q5_K * x = (const block_q5_K *) vx;
+
+    // assume 64 threads - this is very slightly better than the one below
+    const int64_t il  = tid/16;   // il is in 0...3
+    const int64_t ir  = tid%16;   // ir is in 0...15
+    const int64_t is  = 2*il;     // is is in 0...6
+
+    dst_t * y = yy + 64*il + 2*ir;
+
+    const float dall = __low2half(x[ib].dm);
+    const float dmin = __high2half(x[ib].dm);
+
+    const uint8_t * ql = x[ib].qs + 32*il + 2*ir;
+    const uint8_t * qh = x[ib].qh + 2*ir;
+
+    uint8_t sc, m;
+    get_scale_min_k4(is + 0, x[ib].scales, sc, m);
+    const float d1 = dall * sc; const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, x[ib].scales, sc, m);
+    const float d2 = dall * sc; const float m2 = dmin * m;
+
+    uint8_t   hm  = 1 << (2*il);
+    y[ 0] = ggml_cuda_cast<dst_t>(d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1);
+    y[ 1] = ggml_cuda_cast<dst_t>(d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1);
+    hm <<= 1;
+    y[32] = ggml_cuda_cast<dst_t>(d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2);
+    y[33] = ggml_cuda_cast<dst_t>(d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2);
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_q6_K(const void * vx, const int64_t ib, dst_t * yy, const int tid) {
+    const block_q6_K * x = (const block_q6_K *) vx;
+
+    // assume 64 threads - this is very slightly better than the one below
+    const int64_t ip  = tid/32;   // ip is 0 or 1
+    const int64_t il  = tid - 32*ip; // 0...32
+    const int64_t is  = 8*ip + il/16;
+
+    dst_t * y = yy + 128*ip + il;
+
+    const float d = x[ib].d;
+
+    const uint8_t * ql = x[ib].ql + 64*ip + il;
+    const uint8_t   qh = x[ib].qh[32*ip + il];
+    const int8_t  * sc = x[ib].scales + is;
+
+    y[ 0] = ggml_cuda_cast<dst_t>(d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32));
+    y[32] = ggml_cuda_cast<dst_t>(d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32));
+    y[64] = ggml_cuda_cast<dst_t>(d * sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32));
+    y[96] = ggml_cuda_cast<dst_t>(d * sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32));
+}
+
+//================================== i-quants
+
+// Each call dequantizes one super-block of QK_K values into y with 32
+// threads; iq4_nl packs QK_K/QK4_NL sub-blocks per super-block.
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_iq2_xxs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+
+    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 8*il;
+    const uint16_t * q2 = x[ibs].qs + 4*ib;
+    const uint8_t  * aux8 = (const uint8_t *)q2;
+    const uint8_t  * grid = (const uint8_t *)(iq2xxs_grid + aux8[il]);
+    const uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const float d = (float)x[ibs].d * (0.5f + (aux32 >> 28)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 8; ++j) {
+        y[j] = ggml_cuda_cast<dst_t>(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_iq2_xs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+
+    const block_iq2_xs * x = (const block_iq2_xs *) vx;
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 8*il;
+    const uint16_t * q2 = x[ibs].qs + 4*ib;
+    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
+    const float d = (float)x[ibs].d * (0.5f + ((x[ibs].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
+    for (int j = 0; j < 8; ++j) {
+        y[j] = ggml_cuda_cast<dst_t>(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_iq2_s(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+
+    const block_iq2_s * x = (const block_iq2_s *) vx;
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 8*il;
+    const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[ibs].qs[4*ib+il] | ((x[ibs].qh[ib] << (8-2*il)) & 0x300)));
+    const float d = (float)x[ibs].d * (0.5f + ((x[ibs].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
+    const uint8_t signs = x[ibs].qs[QK_K/8+4*ib+il];
+    for (int j = 0; j < 8; ++j) {
+        y[j] = ggml_cuda_cast<dst_t>(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_iq3_xxs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+
+    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 8*il;
+    const uint8_t  * q3 = x[ibs].qs + 8*ib;
+    const uint16_t * gas = (const uint16_t *)(x[ibs].qs + QK_K/4) + 2*ib;
+    const uint8_t  * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]);
+    const uint8_t  * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]);
+    const uint32_t aux32 = gas[0] | (gas[1] << 16);
+    const float d = (float)x[ibs].d * (0.5f + (aux32 >> 28)) * 0.5f;
+    const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = ggml_cuda_cast<dst_t>(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f));
+        y[j+4] = ggml_cuda_cast<dst_t>(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f));
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_iq3_s(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+
+    const block_iq3_s * x = (const block_iq3_s *) vx;
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 8*il;
+    const uint8_t * qs = x[ibs].qs + 8*ib;
+    const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[ibs].qh[ib] << (8-2*il)) & 256)));
+    const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*il+1] | ((x[ibs].qh[ib] << (7-2*il)) & 256)));
+    const float d = (float)x[ibs].d * (1 + 2*((x[ibs].scales[ib/2] >> 4*(ib%2)) & 0xf));
+    const uint8_t signs = x[ibs].signs[4*ib + il];
+    for (int j = 0; j < 4; ++j) {
+        y[j+0] = ggml_cuda_cast<dst_t>(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f));
+        y[j+4] = ggml_cuda_cast<dst_t>(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f));
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_iq1_s(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+
+    const block_iq1_s * x = (const block_iq1_s  *) vx;
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 8*il;
+    const float delta = x[ibs].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
+    const float d = (float)x[ibs].d * (2*((x[ibs].qh[ib] >> 12) & 7) + 1);
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[ibs].qs[4*ib+il] | (((x[ibs].qh[ib] >> 3*il) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+    for (int j = 0; j < 8; ++j) {
+        y[j] = ggml_cuda_cast<dst_t>(d * (q[j] + delta));
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_iq1_m(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+
+    const block_iq1_m * x = (const block_iq1_m  *) vx;
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 8*il;
+    const uint16_t * sc = (const uint16_t *)x[ibs].scales;
+    iq1m_scale_t scale;
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+    const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
+    const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
+    const float delta = x[ibs].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
+    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
+    grid32[0] = iq1s_grid_gpu[x[ibs].qs[4*ib+il] | (((x[ibs].qh[2*ib+il/2] >> 4*(il%2)) & 7) << 8)];
+    grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
+    grid32[0] &= 0x0f0f0f0f;
+    for (int j = 0; j < 8; ++j) {
+        y[j] = ggml_cuda_cast<dst_t>(d * (q[j] + delta));
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_iq4_nl(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+
+    const block_iq4_nl * x = (const block_iq4_nl *) vx + ibs*(QK_K/QK4_NL);
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = (float)x[ib].d;
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = ggml_cuda_cast<dst_t>(d * kvalues_iq4nl[q4[j] & 0xf]);
+        y[j+16] = ggml_cuda_cast<dst_t>(d * kvalues_iq4nl[q4[j] >>  4]);
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_iq4_xs(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+    const block_iq4_xs * x = (const block_iq4_xs *)vx;
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ibs].qs + 16*ib + 4*il;
+    const float d = (float)x[ibs].d * ((((x[ibs].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[ibs].scales_h >> 2*ib) & 3) << 4)) - 32);
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = ggml_cuda_cast<dst_t>(d * kvalues_iq4nl[q4[j] & 0xf]);
+        y[j+16] = ggml_cuda_cast<dst_t>(d * kvalues_iq4nl[q4[j] >>  4]);
+    }
+}
+
+template<typename dst_t>
+static __device__ __forceinline__ void dequantize_mxfp4(const void * vx, const int64_t ibs, dst_t * yy, const int tid) {
+
+    const block_mxfp4 * x = (const block_mxfp4 *) vx + ibs*(QK_K/QK_MXFP4);
+
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = ggml_cuda_e8m0_to_fp32(x[ib].e);
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = ggml_cuda_cast<dst_t>(d * kvalues_mxfp4[q4[j] & 0xf]*0.5f);
+        y[j+16] = ggml_cuda_cast<dst_t>(d * kvalues_mxfp4[q4[j] >>  4]*0.5f);
+    }
+}
@@ -40,6 +40,35 @@ static __global__ void k_get_rows(
    }
 }

+template<typename dst_t, dequantize_kq_t<dst_t> dequantize_kq>
+static __global__ void k_get_rows_kq(
+        const void * __restrict__ src0, const int32_t * __restrict__ src1, dst_t * __restrict__ dst,
+        const int64_t ne00, /*const int64_t ne01, const int64_t ne02, const int64_t ne03,*/
+        /*const int64_t ne10,*/ const int64_t ne11, const uint3 ne12_fdv, /*const int64_t ne13,*/
+        /*const size_t s0,*/ const size_t s1, const size_t s2, const size_t s3,
+        /*const size_t nb00,*/ const size_t nb01, const size_t nb02, const size_t nb03,
+        const size_t s10, const size_t s11, const size_t s12/*, const size_t s13*/) {
+
+    ggml_cuda_pdl_sync();
+    const int64_t nsb = ne00/QK_K; // super-blocks per row
+    for (int64_t z = blockIdx.z; z < ne11*(int64_t)ne12_fdv.z; z += gridDim.z) {
+        // The x and y dimensions of the grid are swapped because the maximum allowed grid size for x is higher.
+        const int i10 = blockIdx.x;
+        const uint2 dm  = fast_div_modulo((uint32_t)z, ne12_fdv);
+        const int i11 = dm.x;
+        const int i12 = dm.y;
+
+        const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
+
+        dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
+        const void * src0_row = (const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03;
+
+        for (int64_t ib = blockIdx.y; ib < nsb; ib += gridDim.y) {
+            dequantize_kq(src0_row, ib, dst_row + ib*QK_K, threadIdx.x);
+        }
+    }
+}
+
 template<typename src0_t, typename dst_t>
 static __global__ void k_get_rows_float(
        const src0_t * src0_ptr, const int32_t * src1_ptr, dst_t * dst_ptr,
@@ -164,6 +193,43 @@ static void get_rows_cuda_q(
        s10, s11, s12/*, s13*/);
 }

+template<int block_dim, typename dst_t, dequantize_kq_t<dst_t> dequantize_kq>
+static void get_rows_cuda_kq(
+        const void * src0_d, const int32_t * src1_d, dst_t * dst_d,
+        const int64_t ne00, const size_t nb01, const size_t nb02, const size_t nb03,
+        const int64_t ne10, const int64_t ne11, const int64_t ne12, const size_t nb10, const size_t nb11, const size_t nb12,
+        const size_t nb1, const size_t nb2, const size_t nb3,
+        cudaStream_t stream) {
+    GGML_ASSERT(ne00 % QK_K == 0);
+    const int64_t nsb = ne00/QK_K;
+
+    const dim3 block_dims(block_dim, 1, 1);
+    const dim3 block_nums(ne10, MIN(nsb, UINT16_MAX), MIN(ne11*ne12, UINT16_MAX));
+
+    // strides in elements
+    // const size_t s0 = nb0 / sizeof(dst_t);
+    const size_t s1 = nb1 / sizeof(dst_t);
+    const size_t s2 = nb2 / sizeof(dst_t);
+    const size_t s3 = nb3 / sizeof(dst_t);
+
+    const size_t s10 = nb10 / sizeof(int32_t);
+    const size_t s11 = nb11 / sizeof(int32_t);
+    const size_t s12 = nb12 / sizeof(int32_t);
+    // const size_t s13 = nb13 / sizeof(int32_t);
+
+    GGML_ASSERT(ne12 > 0);
+    GGML_ASSERT(ne11 <= std::numeric_limits<uint32_t>::max() / ne12);
+    const uint3 ne12_fdv = init_fastdiv_values(ne12);
+
+    k_get_rows_kq<dst_t, dequantize_kq><<<block_nums, block_dims, 0, stream>>>(
+        src0_d, src1_d, dst_d,
+        ne00, /*ne01, ne02, ne03,*/
+        /*ne10,*/ ne11, ne12_fdv, /*ne13,*/
+        /* s0,*/ s1, s2, s3,
+        /* nb00,*/ nb01, nb02, nb03,
+        s10, s11, s12/*, s13*/);
+}
+
 template<typename src0_t, typename dst_t>
 static void get_rows_cuda_float(
        const src0_t * src0_d, const int32_t * src1_d, dst_t * dst_d,
@@ -274,8 +340,67 @@ static void ggml_cuda_get_rows_switch_src0_type(
            get_rows_cuda_q<QK8_0, QR8_0, dequantize_q8_0>(src0_d, src1_d, dst_d,
                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
            break;
+        case GGML_TYPE_Q2_K:
+            get_rows_cuda_kq<64, dst_t, dequantize_q2_K<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            get_rows_cuda_kq<64, dst_t, dequantize_q3_K<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            get_rows_cuda_kq<32, dst_t, dequantize_q4_K<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            get_rows_cuda_kq<64, dst_t, dequantize_q5_K<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            get_rows_cuda_kq<64, dst_t, dequantize_q6_K<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_IQ2_XXS:
+            get_rows_cuda_kq<32, dst_t, dequantize_iq2_xxs<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_IQ2_XS:
+            get_rows_cuda_kq<32, dst_t, dequantize_iq2_xs<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_IQ2_S:
+            get_rows_cuda_kq<32, dst_t, dequantize_iq2_s<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_IQ3_XXS:
+            get_rows_cuda_kq<32, dst_t, dequantize_iq3_xxs<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_IQ3_S:
+            get_rows_cuda_kq<32, dst_t, dequantize_iq3_s<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_IQ1_S:
+            get_rows_cuda_kq<32, dst_t, dequantize_iq1_s<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_IQ1_M:
+            get_rows_cuda_kq<32, dst_t, dequantize_iq1_m<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_IQ4_NL:
+            get_rows_cuda_kq<32, dst_t, dequantize_iq4_nl<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_IQ4_XS:
+            get_rows_cuda_kq<32, dst_t, dequantize_iq4_xs<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_MXFP4:
+            get_rows_cuda_kq<32, dst_t, dequantize_mxfp4<dst_t>>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
        default:
-            // TODO: k-quants
            GGML_ABORT("%s: unsupported src0 type: %s\n", __func__, ggml_type_name(src0_type));
            break;
    }
@@ -4845,7 +4845,25 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                    case GGML_TYPE_Q5_0:
                    case GGML_TYPE_Q5_1:
                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q2_K:
+                    case GGML_TYPE_Q3_K:
+                    case GGML_TYPE_Q4_K:
+                    case GGML_TYPE_Q5_K:
+                    case GGML_TYPE_Q6_K:
+                    case GGML_TYPE_IQ2_XXS:
+                    case GGML_TYPE_IQ2_XS:
+                    case GGML_TYPE_IQ2_S:
+                    case GGML_TYPE_IQ3_XXS:
+                    case GGML_TYPE_IQ3_S:
+                    case GGML_TYPE_IQ1_S:
+                    case GGML_TYPE_IQ1_M:
+                    case GGML_TYPE_IQ4_XS:
                        return true;
+                    case GGML_TYPE_IQ4_NL:
+                    case GGML_TYPE_MXFP4:
+                        // 32-value sub-blocks, the row size does not guarantee
+                        // the QK_K super-blocks the get_rows kernel iterates on
+                        return op->src[0]->ne[0] % QK_K == 0;
                    default:
                        return false;
                }
@@ -25,12 +25,7 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con
        case GGML_TYPE_Q8_0:
            mul_mat_q_case<GGML_TYPE_Q8_0>(ctx, args, stream);
            break;
-        case GGML_TYPE_MXFP4:
-            mul_mat_q_case<GGML_TYPE_MXFP4>(ctx, args, stream);
-            break;
-        case GGML_TYPE_NVFP4:
-            mul_mat_q_case<GGML_TYPE_NVFP4>(ctx, args, stream);
-            break;
+// -----------------------------------------------------------------------
        case GGML_TYPE_Q2_K:
            mul_mat_q_case<GGML_TYPE_Q2_K>(ctx, args, stream);
            break;
@@ -46,6 +41,10 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con
        case GGML_TYPE_Q6_K:
            mul_mat_q_case<GGML_TYPE_Q6_K>(ctx, args, stream);
            break;
+// -----------------------------------------------------------------------
+        case GGML_TYPE_IQ1_S:
+            mul_mat_q_case<GGML_TYPE_IQ1_S>(ctx, args, stream);
+            break;
        case GGML_TYPE_IQ2_XXS:
            mul_mat_q_case<GGML_TYPE_IQ2_XXS>(ctx, args, stream);
            break;
@@ -61,15 +60,19 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con
        case GGML_TYPE_IQ3_S:
            mul_mat_q_case<GGML_TYPE_IQ3_S>(ctx, args, stream);
            break;
-        case GGML_TYPE_IQ1_S:
-            mul_mat_q_case<GGML_TYPE_IQ1_S>(ctx, args, stream);
-            break;
        case GGML_TYPE_IQ4_XS:
            mul_mat_q_case<GGML_TYPE_IQ4_XS>(ctx, args, stream);
            break;
        case GGML_TYPE_IQ4_NL:
            mul_mat_q_case<GGML_TYPE_IQ4_NL>(ctx, args, stream);
            break;
+// -----------------------------------------------------------------------
+        case GGML_TYPE_MXFP4:
+            mul_mat_q_case<GGML_TYPE_MXFP4>(ctx, args, stream);
+            break;
+        case GGML_TYPE_NVFP4:
+            mul_mat_q_case<GGML_TYPE_NVFP4>(ctx, args, stream);
+            break;
        default:
            GGML_ABORT("fatal error");
            break;
@@ -130,14 +133,20 @@ void ggml_cuda_mul_mat_q(
        const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * y_block_size/y_values_per_block +
            ggml_cuda_mmq_get_J_max(src0->type, fallback, cc, ne11) * sizeof(block_q8_1_mmq);
        ggml_cuda_pool_alloc<char> src1_q8_1(ctx.pool(), nbytes_src1_q8_1);
+        ggml_cuda_pool_alloc<float> src1_scale(ctx.pool());
+        if (src0->type == GGML_TYPE_NVFP4 && use_native_fp4) {
+            src1_scale.alloc(ne13*ne12*ne11);
+        }

        {
            const int64_t s11 = src1->nb[1] / ts_src1;
            const int64_t s12 = src1->nb[2] / ts_src1;
            const int64_t s13 = src1->nb[3] / ts_src1;
            if (use_native_fp4) {
+                static constexpr size_t align_float8 = 32;
+                const bool use_aligned_float8 = ggml_cuda_is_aligned(src1, align_float8);
                static_assert(sizeof(block_fp4_mmq) == 4 * sizeof(block_q8_1));
-                quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
+                quantize_mmq_fp4_cuda(src1_d, nullptr, src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, s11, s12, s13, ne10_padded,
                                        ne11, ne12, ne13, stream);

            } else {
@@ -155,6 +164,7 @@ void ggml_cuda_mul_mat_q(

        const mmq_args args = {
            src0_d, src0->type, (const int *) src1_q8_1.ptr, nullptr, nullptr, dst_d,
+            src0->type == GGML_TYPE_NVFP4 && use_native_fp4 ? src1_scale.ptr : nullptr,
            ne00, ne01, ne1, s01, ne11, s1,
            ne02, ne12, s02, s12, s2,
            ne03, ne13, s03, s13, s3,
@@ -192,6 +202,10 @@ void ggml_cuda_mul_mat_q(
    const size_t nbytes_src1_q8_1 = ne12*n_expert_used*ne10_padded * y_block_size/y_values_per_block +
        ggml_cuda_mmq_get_J_max(src0->type, fallback, cc, ne11) * sizeof(block_q8_1_mmq);
    ggml_cuda_pool_alloc<char> src1_q8_1(ctx.pool(), nbytes_src1_q8_1);
+    ggml_cuda_pool_alloc<float> src1_scale(ctx.pool());
+    if (src0->type == GGML_TYPE_NVFP4 && use_native_fp4) {
+        src1_scale.alloc(ne12*n_expert_used);
+    }

    const int64_t ne11_flat = ne12*n_expert_used;
    const int64_t ne12_flat = 1;
@@ -202,18 +216,19 @@ void ggml_cuda_mul_mat_q(
        const int64_t s12 = src1->nb[2] / ts_src1;
        const int64_t s13 = src1->nb[3] / ts_src1;

-        if (dedup_bcast) {
-            // quantize each token once, scatter its block to all n_expert_used slots
-            if (use_native_fp4) {
-                quantize_scatter_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10,
+        if (use_native_fp4) {
+            static constexpr size_t align_float8 = 32;
+            const bool use_aligned_float8 = ggml_cuda_is_aligned(src1, align_float8);
+            if (dedup_bcast) {
+                quantize_scatter_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10,
                                        /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream);
            } else {
-                quantize_scatter_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10,
-                                        /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream);
+                quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src1_scale.ptr, src0->type, use_aligned_float8, ne10, s11, s12, s13,
+                                        ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
            }
-        } else if (use_native_fp4) {
-            quantize_mmq_fp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
-                                    ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
+        } else if (dedup_bcast) {
+            quantize_scatter_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10,
+                                    /*stride_token=*/s12, ne10_padded, ne12, ne11_flat, n_expert_used, stream);
        } else {
            quantize_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
                                   ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
@@ -229,6 +244,7 @@ void ggml_cuda_mul_mat_q(
    // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid.
    const mmq_args args = {
        src0_d, src0->type, (const int *) src1_q8_1.get(), ids_dst.get(), expert_bounds.get(), dst_d,
+        src1_scale.ptr,
        ne00, ne01, ne_get_rows, s01, ne_get_rows, s1,
        ne02, ne02, s02, s12, s2,
        ne03, ne13, s03, s13, s3,
@@ -251,21 +267,24 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
        case GGML_TYPE_Q5_0:
        case GGML_TYPE_Q5_1:
        case GGML_TYPE_Q8_0:
-        case GGML_TYPE_MXFP4:
-        case GGML_TYPE_NVFP4:
+// -------------------------------------------------
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
        case GGML_TYPE_Q5_K:
        case GGML_TYPE_Q6_K:
+// -------------------------------------------------
+        case GGML_TYPE_IQ1_S:
        case GGML_TYPE_IQ2_XXS:
        case GGML_TYPE_IQ2_XS:
        case GGML_TYPE_IQ2_S:
        case GGML_TYPE_IQ3_XXS:
        case GGML_TYPE_IQ3_S:
-        case GGML_TYPE_IQ1_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
+// -------------------------------------------------
+        case GGML_TYPE_MXFP4:
+        case GGML_TYPE_NVFP4:
            mmq_supported = true;
            break;
        default:
@@ -13,7 +13,7 @@
 typedef void (*ggml_cuda_mmq_load_tiles_t)(const char * __restrict__ x, int * x_tile, const int kbx0, const int i_max, const int stride);
 typedef void (*ggml_cuda_mmq_vec_dot_t)(const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00);
 typedef void (*ggml_cuda_mmq_write_back_t)(const float * __restrict__ sum, const int32_t * __restrict__ get_rows_to_sorted,
-    float * __restrict__ dst, const int stride, const int i_max, const int j_max);
+    float * __restrict__ dst, const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max);

 enum mmq_q8_1_ds_layout {
    MMQ_Q8_1_DS_LAYOUT_D4,
@@ -413,11 +413,13 @@ static __host__ int ggml_cuda_mmq_get_nbytes_shared_x(const ggml_cuda_mmq_config

 template <ggml_type type, int J, bool fallback> static __device__ __forceinline__ void ggml_cuda_mmq_write_back_dp4a(
        const float * __restrict__ sum, const int32_t * __restrict__ ids_dst, float * __restrict__ dst,
-        const int stride, const int i_max, const int j_max) {
+        const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max) {
    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    constexpr int nwarps    = ggml_cuda_mmq_get_nthreads(type, J, fallback) / warp_size;
    constexpr int I         = ggml_cuda_mmq_get_I(type, J, fallback);

+    const bool y_scale_used = y_scale != nullptr;
+
 #pragma unroll
    for (int j0 = 0; j0 < J; j0 += nwarps) {
        const int j = j0 + threadIdx.y;
@@ -434,7 +436,16 @@ template <ggml_type type, int J, bool fallback> static __device__ __forceinline_
                continue;
            }

-            dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size];
+            if constexpr (type == GGML_TYPE_NVFP4) {
+                if (y_scale_used) {
+                    dst[ids_dst[j]*stride + i] = y_scale[j] * sum[(j0/nwarps) * (I/warp_size) + i0/warp_size];
+                } else {
+                    dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size];
+                }
+            } else {
+                dst[ids_dst[j]*stride + i] = sum[(j0/nwarps) * (I/warp_size) + i0/warp_size];
+                GGML_UNUSED(y_scale_used);
+            }
        }
    }
 }
@@ -442,7 +453,8 @@ template <ggml_type type, int J, bool fallback> static __device__ __forceinline_
 template<ggml_type type, int J, bool fallback>
 static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma(
            const float * __restrict__ sum, const int * __restrict__ ids_dst, float * __restrict__ dst,
-            const int stride, const int i_max, const int j_max) {
+            const float * __restrict__ y_scale, const int stride, const int i_max, const int j_max) {
+
 #if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
    typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
 #else
@@ -457,6 +469,8 @@ static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma(

    const int i0 = (threadIdx.y / ntx) * (ntx*tile_C::I);

+    const bool y_scale_used = y_scale != nullptr;
+
 #pragma unroll
    for (int j0 = 0; j0 < J; j0 += ntx*tile_C::J) {
 #pragma unroll
@@ -475,7 +489,16 @@ static __device__ __forceinline__ void ggml_cuda_mmq_write_back_mma(
                    continue;
                }

-                dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l];
+                if constexpr (type == GGML_TYPE_NVFP4) {
+                    if (y_scale_used) {
+                        dst[ids_dst[j]*stride + i] = y_scale[j] * sum[(j0/tile_C::J + n)*tile_C::ne + l];
+                    } else {
+                        dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l];
+                    }
+                } else {
+                    dst[ids_dst[j]*stride + i] = sum[(j0/tile_C::J + n)*tile_C::ne + l];
+                    GGML_UNUSED(y_scale_used);
+                }
            }
        }
    }
@@ -819,6 +842,7 @@ template <ggml_type type, int J, bool fallback, bool fixup>
 static __device__ __forceinline__ void mul_mat_q_process_tile(
        const char * __restrict__ x, const int offset_x, const int * __restrict__ y,
        const int * __restrict__ ids_dst, float * __restrict__ dst, float * __restrict__ tmp_fixup,
+        const float * __restrict__ y_scale,
        const int stride_row_x, const int ncols_y, const int stride_col_dst,
        const int tile_x_max_i, const int tile_y_max_j, const int kb0_start, const int kb0_stop) {

@@ -884,9 +908,9 @@ static __device__ __forceinline__ void mul_mat_q_process_tile(
    }

    if (fixup) {
-        write_back(sum, ids_dst, tmp_fixup + blockIdx.x*(J*I), I, I, J);
+        write_back(sum, ids_dst, tmp_fixup + blockIdx.x*(J*I), y_scale, I, I, J);
    } else {
-        write_back(sum, ids_dst, dst, stride_col_dst, tile_x_max_i, tile_y_max_j);
+        write_back(sum, ids_dst, dst, y_scale, stride_col_dst, tile_x_max_i, tile_y_max_j);
    }
 }

@@ -898,6 +922,7 @@ __launch_bounds__(ggml_cuda_mmq_get_nthreads(type, J, fallback), ggml_cuda_mmq_g
 static __global__ void mul_mat_q(
        const char * __restrict__ x, const int * __restrict__ y, const int32_t * __restrict__ ids_dst,
        const int32_t * __restrict__ expert_bounds, float * __restrict__ dst, float * __restrict__ tmp_fixup,
+        const float * __restrict__ y_scale,
        const uint3 blocks_per_ne00, const int nrows_x, const int ncols_dst, const int stride_row_x, const int ncols_y, const int stride_col_dst,
        const uint3 channel_ratio, const uint3 nchannels_y, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
        const uint3 sample_ratio, const uint3 nsamples_y, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
@@ -943,8 +968,14 @@ static __global__ void mul_mat_q(
        int col_low    = 0;
        int col_high   = ncols_dst;
        int col_diff   = ncols_dst;
-        int offset_y   = wt*stride_sample_y   + zt*stride_channel_y;
-        int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+        int offset_y       = wt*stride_sample_y   + zt*stride_channel_y;
+        int offset_dst     = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+        int offset_y_scale;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y;
+        } else {
+            GGML_UNUSED(offset_y_scale);
+        }

        if (ids_dst) {
            col_low  = expert_bounds[zt + 0];
@@ -953,6 +984,9 @@ static __global__ void mul_mat_q(

            offset_y   = 0;
            offset_dst = 0;
+            if constexpr (type == GGML_TYPE_NVFP4) {
+                offset_y_scale = 0;
+            }

            if (jt*J >= col_diff) {
                return;
@@ -974,6 +1008,11 @@ static __global__ void mul_mat_q(

        offset_y   += (col_low + jt*J)*(sizeof(block_q8_1_mmq)/sizeof(int));
        offset_dst += it*I;
+        const float * y_scale_tile = nullptr;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale += col_low + jt*J;
+            y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr;
+        }

        const int tile_x_max_i = nrows_x  - it*I - 1;
        const int tile_y_max_j = col_diff - jt*J - 1;
@@ -982,7 +1021,8 @@ static __global__ void mul_mat_q(

        constexpr bool fixup = false;
        mul_mat_q_process_tile<type, J, fallback, fixup>
-            (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst,
+            (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile,
+             stride_row_x, ncols_y, stride_col_dst,
             tile_x_max_i, tile_y_max_j, 0, blocks_per_ne00.z);
        return;
    }
@@ -1016,8 +1056,14 @@ static __global__ void mul_mat_q(
        int col_low    = 0;
        int col_high   = ncols_dst;
        int col_diff   = ncols_dst;
-        int offset_y   = wt*stride_sample_y   + zt*stride_channel_y;
-        int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+        int offset_y       = wt*stride_sample_y   + zt*stride_channel_y;
+        int offset_dst     = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+        int offset_y_scale;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y;
+        } else {
+            GGML_UNUSED(offset_y_scale);
+        }

        if (ids_dst) {
            col_low  = expert_bounds[zt + 0];
@@ -1026,6 +1072,9 @@ static __global__ void mul_mat_q(

            offset_y   = 0;
            offset_dst = 0;
+            if constexpr (type == GGML_TYPE_NVFP4) {
+                offset_y_scale = 0;
+            }

            if (jt*J >= col_diff) {
                kbc += blocks_per_ne00.z;
@@ -1053,6 +1102,11 @@ static __global__ void mul_mat_q(

        offset_y += (col_low + jt * J) * (sizeof(block_q8_1_mmq) / sizeof(int));
        offset_dst += it*I;
+        const float * y_scale_tile = nullptr;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale += col_low + jt * J;
+            y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr;
+        }

        const int tile_x_max_i = nrows_x  - it*I - 1;
        const int tile_y_max_j = col_diff - jt*J - 1;
@@ -1061,7 +1115,8 @@ static __global__ void mul_mat_q(

        constexpr bool fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
        mul_mat_q_process_tile<type, J, fallback, fixup>
-            (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst,
+            (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile,
+             stride_row_x, ncols_y, stride_col_dst,
             tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop);

        kbc += blocks_per_ne00.z;
@@ -1090,8 +1145,14 @@ static __global__ void mul_mat_q(
    int col_low    = 0;
    int col_high   = ncols_dst;
    int col_diff   = ncols_dst;
-    int offset_y   = wt*stride_sample_y   + zt*stride_channel_y;
-    int offset_dst = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+    int offset_y       = wt*stride_sample_y   + zt*stride_channel_y;
+    int offset_dst     = wt*stride_sample_dst + zt*stride_channel_dst + jt*J*stride_col_dst;
+    int offset_y_scale;
+    if constexpr (type == GGML_TYPE_NVFP4) {
+        offset_y_scale = wt*nchannels_y.z*ncols_y + zt*ncols_y;
+    } else {
+        GGML_UNUSED(offset_y_scale);
+    }

    if (ids_dst) {
        col_low  = expert_bounds[zt + 0];
@@ -1100,6 +1161,9 @@ static __global__ void mul_mat_q(

        offset_y   = 0;
        offset_dst = 0;
+        if constexpr (type == GGML_TYPE_NVFP4) {
+            offset_y_scale = 0;
+        }

        if (jt*J >= col_diff) {
            return;
@@ -1122,6 +1186,11 @@ static __global__ void mul_mat_q(

    offset_y += (col_low + jt * J) * (sizeof(block_q8_1_mmq) / sizeof(int));
    offset_dst += it*I;
+    const float * y_scale_tile = nullptr;
+    if constexpr (type == GGML_TYPE_NVFP4) {
+        offset_y_scale += col_low + jt * J;
+        y_scale_tile = y_scale ? y_scale + offset_y_scale : nullptr;
+    }

    const int tile_x_max_i = nrows_x  - it*I - 1;
    const int tile_y_max_j = col_diff - jt*J - 1;
@@ -1130,7 +1199,8 @@ static __global__ void mul_mat_q(

    constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
    mul_mat_q_process_tile<type, J, fallback, fixup>
-        (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, stride_row_x, ncols_y, stride_col_dst,
+        (x, offset_x, y + offset_y, ids_dst_shared, dst + offset_dst, tmp_fixup, y_scale_tile,
+         stride_row_x, ncols_y, stride_col_dst,
         tile_x_max_i, tile_y_max_j, kb0_start, kb0_stop);
 }

@@ -1274,6 +1344,7 @@ static __global__ void mul_mat_q_stream_k_fixup(

 struct mmq_args {
    const char * x; ggml_type type_x; const int * y; const int32_t * ids_dst; const int32_t * expert_bounds; float * dst;
+    const float * y_scale;
    int64_t ncols_x; int64_t nrows_x; int64_t ncols_dst; int64_t stride_row_x; int64_t ncols_y; int64_t nrows_dst;
    int64_t nchannels_x; int64_t nchannels_y; int64_t stride_channel_x; int64_t stride_channel_y; int64_t stride_channel_dst;
    int64_t nsamples_x; int64_t nsamples_y; int64_t stride_sample_x; int64_t stride_sample_y; int64_t stride_sample_dst;
@@ -1323,7 +1394,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a

    if (!ggml_cuda_mmq_get_stream_k(type, J, fallback, cc)) {
        mul_mat_q<type, J, fallback><<<block_nums_xy_tiling, block_dims, nbytes_shared, stream>>>
-            (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr,
+            (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, nullptr, args.y_scale,
             blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
             channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
             sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
@@ -1352,7 +1423,7 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
    const dim3 block_dims_fixup(block_dims.x, block_dims.y/2, block_dims.z);

    mul_mat_q<type, J, fallback><<<block_nums_stream_k, block_dims, nbytes_shared, stream>>>
-        (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr,
+        (args.x, args.y, args.ids_dst, args.expert_bounds, args.dst, tmp_fixup.ptr, args.y_scale,
         blocks_per_ne00_fd, args.nrows_x, args.ncols_dst, args.stride_row_x, args.ncols_y, args.nrows_dst,
         channel_ratio_fd, nchannels_y_fd, args.stride_channel_x, args.stride_channel_y, args.stride_channel_dst,
         sample_ratio_fd, nsamples_y_fd, args.stride_sample_x, args.stride_sample_y, args.stride_sample_dst,
@@ -1466,26 +1537,30 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda
 #define DECL_MMQ_CASE(type)                                                        \
    template void mul_mat_q_case<type>(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) \

+extern DECL_MMQ_CASE(GGML_TYPE_Q1_0);
 extern DECL_MMQ_CASE(GGML_TYPE_Q4_0);
 extern DECL_MMQ_CASE(GGML_TYPE_Q4_1);
 extern DECL_MMQ_CASE(GGML_TYPE_Q5_0);
 extern DECL_MMQ_CASE(GGML_TYPE_Q5_1);
 extern DECL_MMQ_CASE(GGML_TYPE_Q8_0);
-extern DECL_MMQ_CASE(GGML_TYPE_MXFP4);
-extern DECL_MMQ_CASE(GGML_TYPE_NVFP4);
+// -----------------------------------------
 extern DECL_MMQ_CASE(GGML_TYPE_Q2_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q3_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q4_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q5_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q6_K);
+// -----------------------------------------
+extern DECL_MMQ_CASE(GGML_TYPE_IQ1_S);
 extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XXS);
 extern DECL_MMQ_CASE(GGML_TYPE_IQ2_XS);
 extern DECL_MMQ_CASE(GGML_TYPE_IQ2_S);
 extern DECL_MMQ_CASE(GGML_TYPE_IQ3_XXS);
 extern DECL_MMQ_CASE(GGML_TYPE_IQ3_S);
-extern DECL_MMQ_CASE(GGML_TYPE_IQ1_S);
 extern DECL_MMQ_CASE(GGML_TYPE_IQ4_NL);
 extern DECL_MMQ_CASE(GGML_TYPE_IQ4_XS);
+// -----------------------------------------
+extern DECL_MMQ_CASE(GGML_TYPE_MXFP4);
+extern DECL_MMQ_CASE(GGML_TYPE_NVFP4);

 // -------------------------------------------------------------------------------------------------------------------------

@@ -1,6 +1,55 @@
 #include "quantize.cuh"
 #include <cstdint>

+#if defined(BLACKWELL_MMA_AVAILABLE)
+// this maps to 256-bit loads in PTX on supported devices,
+// and otherwise falls back to 2 128-bit loads
+struct __builtin_align__(32) float8 {
+    float x; float y; float z; float w;
+    float p; float q; float r; float s;
+};
+#endif
+
+#if CUDART_VERSION >= 12080
+static __device__ __forceinline__ float nvfp4_native_scale_error(
+        const float vals[QK_NVFP4_SUB], const float inv_col_scale, const float inv_scale, const float scale) {
+    const float scale_dequant = 2.0f * scale;
+    float err = 0.0f;
+
+#pragma unroll
+    for (int k = 0; k < QK_NVFP4_SUB; k += 4) {
+        const float v0 = vals[k + 0] * inv_col_scale;
+        const float v1 = vals[k + 1] * inv_col_scale;
+        const float v2 = vals[k + 2] * inv_col_scale;
+        const float v3 = vals[k + 3] * inv_col_scale;
+
+        const __nv_fp4x4_e2m1 q(make_float4(v0 * inv_scale, v1 * inv_scale, v2 * inv_scale, v3 * inv_scale));
+        const __nv_fp4x4_storage_t q_storage = q.__x;
+        const __nv_fp4x2_storage_t q_lo = static_cast<__nv_fp4x2_storage_t>(q_storage);
+        const __nv_fp4x2_storage_t q_hi = static_cast<__nv_fp4x2_storage_t>(q_storage >> 8U);
+
+        const __half2_raw hraw2_lo = __nv_cvt_fp4x2_to_halfraw2(q_lo, __NV_E2M1);
+        const __half2_raw hraw2_hi = __nv_cvt_fp4x2_to_halfraw2(q_hi, __NV_E2M1);
+        const __half2 h2_lo = static_cast<__half2>(hraw2_lo);
+        const __half2 h2_hi = static_cast<__half2>(hraw2_hi);
+        const float2 dq_lo = __half22float2(h2_lo);
+        const float2 dq_hi = __half22float2(h2_hi);
+
+        const float err0 = fabsf(v0) - fabsf(dq_lo.x) * scale_dequant;
+        const float err1 = fabsf(v1) - fabsf(dq_lo.y) * scale_dequant;
+        const float err2 = fabsf(v2) - fabsf(dq_hi.x) * scale_dequant;
+        const float err3 = fabsf(v3) - fabsf(dq_hi.y) * scale_dequant;
+
+        err = fmaf(err0, err0, err);
+        err = fmaf(err1, err1, err);
+        err = fmaf(err2, err2, err);
+        err = fmaf(err3, err3, err);
+    }
+
+    return err;
+}
+#endif // CUDART_VERSION >= 12080
+
 __launch_bounds__(CUDA_QUANTIZE_BLOCK_SIZE, 1)
 static __global__ void quantize_q8_1(
        const float * x_ptr, void * vy_ptr,
@@ -74,115 +123,209 @@ __device__ __forceinline__ uint8_t compute_e8m0_scale(float amax) {
    return static_cast<uint8_t>(biased);
 }

-
 // scatter: grid over tokens, quantize once, write to all the token's compact rows
-template <bool scatter>
+template <bool scatter, bool use_aligned_float8>
 static __global__ void quantize_mmq_nvfp4(
-        const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy,
+        const float * __restrict__ x, const int32_t * __restrict__ ids, void * __restrict__ vy, float * __restrict__ scale,
        const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
        const int64_t ne0, const int64_t ne1, const int64_t ne2, const int n_expert_used) {
 #if defined(BLACKWELL_MMA_AVAILABLE)

-    const int64_t i0_base = ((int64_t) blockDim.x * blockIdx.y + threadIdx.x) * QK_NVFP4_SUB;
-    if (i0_base >= ne0) {
-        return;
-    }
-
-    const int64_t k_block = i0_base / QK_FP4_MMQ;
    const int64_t blocks_per_col = (ne0 + QK_FP4_MMQ - 1) / QK_FP4_MMQ;
-    if (k_block >= blocks_per_col) {
-        return;
-    }
-    const int sub = (i0_base % QK_FP4_MMQ) / QK_NVFP4_SUB;

    int64_t base_idx;
    if constexpr (scatter) {
        base_idx = (int64_t) blockIdx.x * s02; // one physical row per token
    } else {
-        const int64_t i2  = blockIdx.z % ne2;
-        const int64_t i3  = blockIdx.z / ne2;
+        const int64_t i2  = blockIdx.y % ne2;
+        const int64_t i3  = blockIdx.y / ne2;
        const int64_t i01 = ids ? ids[blockIdx.x] : blockIdx.x;
        base_idx = i3 * s03 + i2 * s02 + i01 * s01;
    }
+    const float * __restrict__  x_row = x + base_idx;

-    float vals_raw[QK_NVFP4_SUB];
-    float amax_raw = 0.0f;
+    float amax = 0.0f;
+    if constexpr (use_aligned_float8) {
+        for (int64_t i0 = 8 * threadIdx.x; i0 < ne00; i0 += 8 * blockDim.x) {
+            const float * x_base = x_row + i0;
+            const float8 v = reinterpret_cast<const float8 *>(x_base)[0];
+            amax = fmaxf(amax, fabsf(v.x));
+            amax = fmaxf(amax, fabsf(v.y));
+            amax = fmaxf(amax, fabsf(v.z));
+            amax = fmaxf(amax, fabsf(v.w));
+            amax = fmaxf(amax, fabsf(v.p));
+            amax = fmaxf(amax, fabsf(v.q));
+            amax = fmaxf(amax, fabsf(v.r));
+            amax = fmaxf(amax, fabsf(v.s));
+        }
+    } else {
+        for (int64_t i0 = threadIdx.x; i0 < ne00; i0 += blockDim.x) {
+            amax = fmaxf(amax, fabsf(x_row[i0]));
+        }
+    }
+
+    amax = warp_reduce_max<WARP_SIZE>(amax);
+
+    __shared__ float warp_amax[CUDA_QUANTIZE_BLOCK_SIZE_MMQ / WARP_SIZE];
+    const int lane = threadIdx.x % WARP_SIZE;
+    const int warp = threadIdx.x / WARP_SIZE;
+
+    if (lane == 0) {
+        warp_amax[warp] = amax;
+    }
+    __syncthreads();
+
+    if (warp == 0) {
+        amax = threadIdx.x < int(CUDA_QUANTIZE_BLOCK_SIZE_MMQ / WARP_SIZE) ? warp_amax[lane] : 0.0f;
+        amax = warp_reduce_max<WARP_SIZE>(amax);
+        if (lane == 0) {
+            warp_amax[0] = amax / (6.0f * 448.0f);
+            if constexpr (scatter) {
 #pragma unroll
-    for (int k = 0; k < QK_NVFP4_SUB; k++) {
-        const int64_t i00 = i0_base + k;
-        if (i00 < ne00) {
-            const float v = x[base_idx + i00];
-            vals_raw[k] = v;
-            amax_raw = fmaxf(amax_raw, fabsf(v));
-        } else {
-            vals_raw[k] = 0.0f;
+                for (int slot = 0; slot < n_expert_used; ++slot) {
+                    const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot];
+                    scale[i] = warp_amax[0];
+                }
+            } else {
+                scale[blockIdx.y * ne1 + blockIdx.x] = warp_amax[0];
+            }
        }
    }
-
-    static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2};
-    const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_raw / 6.0f);
-
-    float best_err = FLT_MAX;
-    uint8_t fp8_code = 0;
-    float subblock_scale = 0.0f;
-
-#pragma unroll // Check +/- 2 to find best code to reduce NVFP4 activation loss. Negligible overhead on Blackwell.
-    for (int i = 0; i < 5; i++) {
-        const int test_code = first_fp8_code + test_offsets[i];
-        if (test_code < 0 || test_code > 0x7e) {
-            continue;
-        }
-        const uint8_t code = (uint8_t) test_code;
-        const float test_scale = ggml_cuda_ue4m3_to_fp32(code);
-        const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f;
-        float cur_err = 0.0f;
-#pragma unroll
-        for (int k = 0; k < QK_NVFP4_SUB; ++k) {
-            const float v = vals_raw[k];
-            const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale);
-            const float err_diff = fabsf(v) - fabsf(kvalues_mxfp4[q & 0x7]) * test_scale;
-            cur_err = fmaf(err_diff, err_diff, cur_err);
-        }
-
-        if (cur_err < best_err) {
-            best_err = cur_err;
-            fp8_code = test_code;
-            subblock_scale = test_scale;
-        }
-    }
-
-    const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
-    uint32_t q0 = 0;
-    uint32_t q1 = 0;
-#pragma unroll // this is faster than the previous __nv_fp4x4_e2m1
-    for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) {
-        q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  0], inv_scale) << (8 * k);
-        q0 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  8], inv_scale) << (8 * k + 4);
-        q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k +  4], inv_scale) << (8 * k);
-        q1 |= (uint32_t) ggml_cuda_float_to_fp4_e2m1(vals_raw[k + 12], inv_scale) << (8 * k + 4);
-    }
+    __syncthreads();

    block_fp4_mmq * y = (block_fp4_mmq *) vy;
-    if constexpr (scatter) {
+    const int64_t n_subblocks = (ne0 + QK_NVFP4_SUB - 1) / QK_NVFP4_SUB;
+
+    for (int64_t isb = threadIdx.x; isb < n_subblocks; isb += blockDim.x) {
+        const int64_t i0_base = isb * QK_NVFP4_SUB;
+        const int64_t k_block = i0_base / QK_FP4_MMQ;
+        const int sub = (i0_base % QK_FP4_MMQ) / QK_NVFP4_SUB;
+
+        const float row_scale = warp_amax[0];
+        const float inv_col_scale = row_scale > 0.0f ? 1.0f / row_scale : 0.0f;
+
+        float vals[QK_NVFP4_SUB];
+        if constexpr (use_aligned_float8) {
+            const float * x_base = x_row + i0_base;
+            const float8 v0 = i0_base +  7 < ne00 ? reinterpret_cast<const float8 *>(x_base)[0]     : float8{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
+            const float8 v1 = i0_base + 15 < ne00 ? reinterpret_cast<const float8 *>(x_base + 8)[0] : float8{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f};
+            vals[0] = v0.x; vals[1] = v0.y; vals[2] = v0.z; vals[3] = v0.w;
+            vals[4] = v0.p; vals[5] = v0.q; vals[6] = v0.r; vals[7] = v0.s;
+            vals[8] = v1.x; vals[9] = v1.y; vals[10] = v1.z; vals[11] = v1.w;
+            vals[12] = v1.p; vals[13] = v1.q; vals[14] = v1.r; vals[15] = v1.s;
+        } else {
 #pragma unroll
-        for (int slot = 0; slot < n_expert_used; ++slot) {
-            const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot];
-            block_fp4_mmq * yb = y + (k_block * ne1 + i);
+            for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+                const int64_t i00 = i0_base + k;
+                vals[k] = i00 < ne00 ? x_row[i00] : 0.0f;
+            }
+        }
+
+        uint32_t q0 = 0;
+        uint32_t q1 = 0;
+
+        float amax_sub = 0.0f;
+#pragma unroll
+        for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+            amax_sub = fmaxf(amax_sub, fabsf(vals[k] * inv_col_scale));
+        }
+
+        static constexpr int test_offsets[5] = { 0, -1, 1, -2, 2 };
+        const int first_fp8_code = (int) ggml_cuda_fp32_to_ue4m3(amax_sub / 6.0f);
+
+        uint8_t fp8_code = (uint8_t) first_fp8_code;
+        float subblock_scale = ggml_cuda_ue4m3_to_fp32(fp8_code);
+        float inv_scale_err = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
+#if CUDART_VERSION >= 12080
+        float best_err = nvfp4_native_scale_error(vals, inv_col_scale, inv_scale_err, subblock_scale);
+#else
+        float best_err = 0.0f;
+#pragma unroll
+        for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+            const float v = vals[k] * inv_col_scale;
+            const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, inv_scale_err);
+            const float err_diff = fabsf(v) - fabsf(kvalues_fp4[q & 0x7]) * subblock_scale;
+            best_err = fmaf(err_diff, err_diff, best_err);
+        }
+#endif // CUDART_VERSION >= 12080
+
+#pragma unroll
+        for (int i = 1; i < 5; ++i) {
+            const int test_code = first_fp8_code + test_offsets[i];
+            if (test_code < 0 || test_code > 0x7e) {
+                continue;
+            }
+
+            const float test_scale = ggml_cuda_ue4m3_to_fp32((uint8_t) test_code);
+            const float test_inv_scale = test_scale > 0.0f ? 0.5f / test_scale : 0.0f;
+#if CUDART_VERSION >= 12080
+            const float cur_err = nvfp4_native_scale_error(vals, inv_col_scale, test_inv_scale, test_scale);
+#else
+            float cur_err = 0.0f;
+#pragma unroll
+            for (int k = 0; k < QK_NVFP4_SUB; ++k) {
+                const float v = vals[k] * inv_col_scale;
+                const uint8_t q = ggml_cuda_float_to_fp4_e2m1(v, test_inv_scale);
+                const float err_diff = fabsf(v) - fabsf(kvalues_fp4[q & 0x7]) * test_scale;
+                cur_err = fmaf(err_diff, err_diff, cur_err);
+            }
+#endif // CUDART_VERSION >= 12080
+
+            if (cur_err < best_err) {
+                best_err = cur_err;
+                fp8_code = (uint8_t) test_code;
+                subblock_scale = test_scale;
+            }
+        }
+#if CUDART_VERSION >= 12080
+        const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
+        const float s = inv_col_scale * inv_scale;
+
+        __nv_fp4x4_e2m1 q0_lo(make_float4(vals[0] * s, vals[8]  * s, vals[1] * s, vals[9]  * s));
+        __nv_fp4x4_e2m1 q0_hi(make_float4(vals[2] * s, vals[10] * s, vals[3] * s, vals[11] * s));
+        __nv_fp4x4_e2m1 q1_lo(make_float4(vals[4] * s, vals[12] * s, vals[5] * s, vals[13] * s));
+        __nv_fp4x4_e2m1 q1_hi(make_float4(vals[6] * s, vals[14] * s, vals[7] * s, vals[15] * s));
+
+        const char2 q0_lo_c = *reinterpret_cast<char2 *>(&q0_lo);
+        const char2 q0_hi_c = *reinterpret_cast<char2 *>(&q0_hi);
+        const char2 q1_lo_c = *reinterpret_cast<char2 *>(&q1_lo);
+        const char2 q1_hi_c = *reinterpret_cast<char2 *>(&q1_hi);
+
+        q0 = uint32_t(uint8_t(q0_lo_c.x)) | (uint32_t(uint8_t(q0_lo_c.y)) <<  8) |
+            (uint32_t(uint8_t(q0_hi_c.x)) << 16) | (uint32_t(uint8_t(q0_hi_c.y)) << 24);
+        q1 = uint32_t(uint8_t(q1_lo_c.x)) | (uint32_t(uint8_t(q1_lo_c.y)) <<  8) |
+            (uint32_t(uint8_t(q1_hi_c.x)) << 16) | (uint32_t(uint8_t(q1_hi_c.y)) << 24);
+#else
+        const float inv_scale = subblock_scale > 0.0f ? 0.5f / subblock_scale : 0.0f;
+#pragma unroll
+        for (int k = 0; k < QK_NVFP4_SUB / 4; ++k) {
+            q0 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 0] * inv_col_scale, inv_scale)) << (8 * k);
+            q0 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 8] * inv_col_scale, inv_scale)) << (8 * k + 4);
+            q1 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 4] * inv_col_scale, inv_scale)) << (8 * k);
+            q1 |= uint32_t(ggml_cuda_float_to_fp4_e2m1(vals[k + 12] * inv_col_scale, inv_scale)) << (8 * k + 4);
+        }
+#endif // CUDART_VERSION >= 12080
+
+        if constexpr (scatter) {
+#pragma unroll
+            for (int slot = 0; slot < n_expert_used; ++slot) {
+                const int64_t i = ids[(int64_t) blockIdx.x * n_expert_used + slot];
+                block_fp4_mmq * yb = y + (k_block * ne1 + i);
+                uint32_t * yqs = reinterpret_cast<uint32_t *>(yb->qs);
+                yqs[2 * sub + 0] = q0;
+                yqs[2 * sub + 1] = q1;
+                reinterpret_cast<uint8_t *>(yb->d4)[sub] = fp8_code;
+            }
+        } else {
+            block_fp4_mmq * yb = y + (blockIdx.y * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x);
            uint32_t * yqs = reinterpret_cast<uint32_t *>(yb->qs);
            yqs[2 * sub + 0] = q0;
            yqs[2 * sub + 1] = q1;
            reinterpret_cast<uint8_t *>(yb->d4)[sub] = fp8_code;
        }
-    } else {
-        block_fp4_mmq * yb = y + (blockIdx.z * ((int64_t) blocks_per_col * ne1) + k_block * ne1 + blockIdx.x);
-        uint32_t * yqs = reinterpret_cast<uint32_t *>(yb->qs);
-        yqs[2 * sub + 0] = q0;
-        yqs[2 * sub + 1] = q1;
-        reinterpret_cast<uint8_t *>(yb->d4)[sub] = fp8_code;
    }
-    GGML_UNUSED(n_expert_used);
 #else
-    GGML_UNUSED(n_expert_used);
+    GGML_UNUSED_VARS(x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, n_expert_used);
    NO_DEVICE_CODE; // This is for Blackwell NVFP4 activations only.
 #endif // defined(BLACKWELL_MMA_AVAILABLE)

@@ -491,18 +634,22 @@ void quantize_scatter_mmq_q8_1_cuda(

 // scatter=true reuses the quant kernels: grid over tokens, ids = inverse map (token slot -> compact row)
 void quantize_scatter_mmq_fp4_cuda(
-        const float * x, const int32_t * ids_src1_inv, void * vy, const ggml_type type_src0,
+        const float * x, const int32_t * ids_src1_inv, void * vy, float * scale, const ggml_type type_src0, const bool use_aligned_float8,
        const int64_t ne00, const int64_t stride_token, const int64_t ne0,
        const int64_t n_tokens, const int64_t nrows_dst, const int n_expert_used, cudaStream_t stream) {
    GGML_ASSERT(ne0 > 0);
    if (type_src0 == GGML_TYPE_NVFP4) {
+        GGML_ASSERT(scale);
        GGML_ASSERT(ne00 % QK_NVFP4 == 0);
-        constexpr int nvfp4_block_size = 128;
-        const int64_t block_num_y = (ne0 + QK_NVFP4_SUB * nvfp4_block_size - 1) / (QK_NVFP4_SUB * nvfp4_block_size);
-        const dim3 block_size(nvfp4_block_size, 1, 1);
-        const dim3 num_blocks(n_tokens, block_num_y, 1);
-        quantize_mmq_nvfp4<true><<<num_blocks, block_size, 0, stream>>>(
-            x, ids_src1_inv, vy, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used);
+        const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1);
+        const dim3 num_blocks(n_tokens, 1, 1);
+        if (use_aligned_float8) {
+            quantize_mmq_nvfp4<true, true><<<num_blocks, block_size, 0, stream>>>(
+                x, ids_src1_inv, vy, scale, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used);
+        } else {
+            quantize_mmq_nvfp4<true, false><<<num_blocks, block_size, 0, stream>>>(
+                x, ids_src1_inv, vy, scale, ne00, /*s01=*/0, /*s02=*/stride_token, /*s03=*/0, ne0, /*ne1=*/nrows_dst, /*ne2=*/1, n_expert_used);
+        }
    } else {
        GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4);
        constexpr int nwarps = 8;
@@ -516,20 +663,24 @@ void quantize_scatter_mmq_fp4_cuda(
 }

 void quantize_mmq_fp4_cuda(
-        const float * x, const int32_t * ids, void * vy, const ggml_type type_src0,
+        const float * x, const int32_t * ids, void * vy, float * scale, const ggml_type type_src0, const bool use_aligned_float8,
        const int64_t ne00, const int64_t s01, const int64_t s02, const int64_t s03,
        const int64_t ne0, const int64_t ne1, const int64_t ne2, const int64_t ne3, cudaStream_t stream) {
    GGML_ASSERT(type_src0 == GGML_TYPE_MXFP4 || type_src0 == GGML_TYPE_NVFP4);
    GGML_ASSERT(ne0 > 0);

    if (type_src0 == GGML_TYPE_NVFP4) {
+        GGML_ASSERT(scale);
        GGML_ASSERT(ne00 % QK_NVFP4 == 0);
-        constexpr int nvfp4_block_size = 128;
-        const int64_t block_num_y = (ne0 + QK_NVFP4_SUB * nvfp4_block_size - 1) / (QK_NVFP4_SUB * nvfp4_block_size);
-        const dim3 block_size(nvfp4_block_size, 1, 1);
-        const dim3 num_blocks(ne1, block_num_y, ne2 * ne3);
-        quantize_mmq_nvfp4<false><<<num_blocks, block_size, 0, stream>>>(
-            x, ids, vy, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
+        const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE_MMQ, 1, 1);
+        const dim3 num_blocks(ne1, ne2 * ne3, 1);
+        if (use_aligned_float8) {
+            quantize_mmq_nvfp4<false, true><<<num_blocks, block_size, 0, stream>>>(
+                x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
+        } else {
+            quantize_mmq_nvfp4<false, false><<<num_blocks, block_size, 0, stream>>>(
+                x, ids, vy, scale, ne00, s01, s02, s03, ne0, ne1, ne2, /*n_expert_used=*/0);
+        }
    } else {
        GGML_ASSERT(ne0 % (2 * QK_MXFP4) == 0);

@@ -29,7 +29,9 @@ void quantize_mmq_q8_1_cuda(
 void quantize_mmq_fp4_cuda(const float *   x,
                             const int32_t * ids,
                             void *          vy,
+                             float *         scale,
                             ggml_type       type_src0,
+                             bool            use_aligned_float8,
                             int64_t         ne00,
                             int64_t         s01,
                             int64_t         s02,
@@ -44,7 +46,9 @@ void quantize_mmq_fp4_cuda(const float *   x,
 void quantize_scatter_mmq_fp4_cuda(const float *   x,
                                   const int32_t * ids_src1_inv,
                                   void *          vy,
+                                   float *         scale,
                                   ggml_type       type_src0,
+                                   bool            use_aligned_float8,
                                   int64_t         ne00,
                                   int64_t         stride_token,
                                   int64_t         ne0,
@@ -3084,7 +3084,10 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session
        return false;
    }

-    if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
+    if (!ggml_is_contiguous_1(src0)) {
+        return false;
+    }
+    if (!ggml_is_contiguous(dst)) {
        return false;
    }

@@ -3095,7 +3098,7 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session
        if (!ggml_are_same_shape(src0, src1)) {
            return false;
        }
-        if (!ggml_is_contiguous(src1)) {
+        if (!ggml_is_contiguous_1(src1)) {
            return false;
        }
    }
@@ -4152,12 +4155,10 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
                case GGML_UNARY_OP_SIGMOID:
                case GGML_UNARY_OP_SOFTPLUS:
                case GGML_UNARY_OP_TANH:
-                    supp = ggml_hexagon_supported_unary(sess, op);
-                    break;
                case GGML_UNARY_OP_SILU:
                case GGML_UNARY_OP_GELU:
                case GGML_UNARY_OP_GELU_QUICK:
-                    supp = ggml_hexagon_supported_activations(sess, op);
+                    supp = ggml_hexagon_supported_unary(sess, op);
                    break;
                default:
                    break;
@@ -723,14 +723,14 @@ static int execute_op(struct htp_ops_context * octx) {
        case HTP_OP_SQRT:
        case HTP_OP_UNARY_SOFTPLUS:
        case HTP_OP_UNARY_SIGMOID:
+        case HTP_OP_UNARY_SILU:
+        case HTP_OP_UNARY_GELU:
        case HTP_OP_UNARY_NEG:
        case HTP_OP_UNARY_EXP:
        case HTP_OP_UNARY_TANH:
        case HTP_OP_L2_NORM:
            return op_unary(octx);

-        case HTP_OP_UNARY_SILU:
-        case HTP_OP_UNARY_GELU:
        case HTP_OP_GLU_SWIGLU:
        case HTP_OP_GLU_SWIGLU_OAI:
        case HTP_OP_GLU_GEGLU:
@@ -276,6 +276,39 @@ static void sigmoid_f32(const float * restrict src,
    }
 }

+// silu(x) = x * sigmoid(x)
+static void silu_f32(const float * restrict src,
+                     float * restrict dst,
+                     const uint32_t num_rows,
+                     const struct htp_unary_context * uctx) {
+    htp_unary_op_preamble;
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned);
+        uint8_t * restrict dst_local       = (uint8_t *)dst + (ir * dst_row_size_aligned);
+
+        hvx_sigmoid_f32_aa(dst_local, src_local, ne0);
+        hvx_mul_f32_aaa(dst_local, src_local, dst_local, ne0);
+    }
+}
+
+// gelu(x) = x * sigmoid(1.702 * x)  (quick/sigmoid approximation, matches CPU GELU_QUICK reference)
+static void gelu_f32(const float * restrict src,
+                     float * restrict dst,
+                     const uint32_t num_rows,
+                     const struct htp_unary_context * uctx) {
+    htp_unary_op_preamble;
+
+    for (uint32_t ir = 0; ir < num_rows; ir++) {
+        const uint8_t * restrict src_local = (const uint8_t *)src + (ir * src0_row_size_aligned);
+        uint8_t * restrict dst_local       = (uint8_t *)dst + (ir * dst_row_size_aligned);
+
+        hvx_mul_scalar_f32(dst_local, src_local, 1.702f, ne0);
+        hvx_sigmoid_f32_aa(dst_local, dst_local, ne0);
+        hvx_mul_f32_aaa(dst_local, src_local, dst_local, ne0);
+    }
+}
+
 static void tri_f32(const float * restrict src,
                    float * restrict dst,
                    const uint32_t num_rows,
@@ -566,6 +599,8 @@ DEFINE_UNARY_TASK(sqrt,           false, false, sqrt_f32(src0_vtcm, dst_vtcm, bl
 DEFINE_UNARY_TASK(unary_neg,      false, false, neg_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_exp,      false, false, exp_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_sigmoid,  false, false, sigmoid_f32(src0_vtcm, dst_vtcm, block_size, uctx))
+DEFINE_UNARY_TASK(unary_silu,     false, false, silu_f32(src0_vtcm, dst_vtcm, block_size, uctx))
+DEFINE_UNARY_TASK(unary_gelu,     false, false, gelu_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_softplus, false, false, softplus_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(unary_tanh,     false, false, tanh_f32(src0_vtcm, dst_vtcm, block_size, uctx))
 DEFINE_UNARY_TASK(l2_norm,        false, false, l2_norm_f32(src0_vtcm, dst_vtcm, block_size, uctx))
@@ -717,6 +752,19 @@ static inline void tile_unary_softplus_f32(uint8_t * dst_vtcm, const uint8_t * s
    }
 }

+// silu(x) = x * sigmoid(x)
+static inline void tile_silu_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) {
+    hvx_sigmoid_f32_aa(dst_vtcm, src_vtcm, tw);
+    hvx_mul_f32_aaa(dst_vtcm, src_vtcm, dst_vtcm, tw);
+}
+
+// gelu(x) = x * sigmoid(1.702 * x)  (quick/sigmoid approximation, matches CPU GELU_QUICK reference)
+static inline void tile_gelu_f32(uint8_t * dst_vtcm, const uint8_t * src_vtcm, uint32_t tw) {
+    hvx_mul_scalar_f32(dst_vtcm, src_vtcm, 1.702f, tw);
+    hvx_sigmoid_f32_aa(dst_vtcm, dst_vtcm, tw);
+    hvx_mul_f32_aaa(dst_vtcm, src_vtcm, dst_vtcm, tw);
+}
+
 // Triangular mask applied to one column tile. Boundary is an absolute column index, so
 // each vector compares against its absolute column position (col_start + i*VLEN_FP32).
 static inline void tri_apply_tile_f32(const uint8_t * restrict src, uint8_t * restrict dst,
@@ -798,6 +846,8 @@ DEFINE_UNARY_TILED_TASK(sqrt,           false, hvx_sqrt_f32_aa(dst_vtcm, src_vtc
 DEFINE_UNARY_TILED_TASK(unary_neg,      false, hvx_scale_f32_aa(dst_vtcm, src_vtcm, tw, -1.0f))
 DEFINE_UNARY_TILED_TASK(unary_exp,      false, hvx_exp_f32(dst_vtcm, src_vtcm, tw, false))
 DEFINE_UNARY_TILED_TASK(unary_sigmoid,  false, hvx_sigmoid_f32_aa(dst_vtcm, src_vtcm, tw))
+DEFINE_UNARY_TILED_TASK(unary_silu,     false, tile_silu_f32(dst_vtcm, src_vtcm, tw))
+DEFINE_UNARY_TILED_TASK(unary_gelu,     false, tile_gelu_f32(dst_vtcm, src_vtcm, tw))
 DEFINE_UNARY_TILED_TASK(unary_softplus, false, tile_unary_softplus_f32(dst_vtcm, src_vtcm, tw))
 DEFINE_UNARY_TILED_TASK(unary_tanh,     false, hvx_tanh_f32_aa(dst_vtcm, src_vtcm, tw))
 DEFINE_UNARY_TILED_TASK(tri,            true,  tri_apply_tile_f32(src_vtcm, dst_vtcm, tw, col, i01, ne0, tri_ttype))
@@ -821,6 +871,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
        case HTP_OP_UNARY_NEG:       op_type = "neg-f32";          break;
        case HTP_OP_UNARY_EXP:       op_type = "exp-f32";          break;
        case HTP_OP_UNARY_SIGMOID:   op_type = "sigmoid-f32";      break;
+        case HTP_OP_UNARY_SILU:      op_type = "silu-f32";         break;
+        case HTP_OP_UNARY_GELU:      op_type = "gelu-f32";         break;
        case HTP_OP_UNARY_SOFTPLUS:  op_type = "softplus-f32";     break;
        case HTP_OP_UNARY_TANH:      op_type = "tanh-f32";         break;
        case HTP_OP_L2_NORM:         op_type = "l2norm-f32";       break;
@@ -917,6 +969,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
                case HTP_OP_UNARY_NEG:       task_func = unary_task_f32_tiled_unary_neg;      break;
                case HTP_OP_UNARY_EXP:       task_func = unary_task_f32_tiled_unary_exp;      break;
                case HTP_OP_UNARY_SIGMOID:   task_func = unary_task_f32_tiled_unary_sigmoid;  break;
+                case HTP_OP_UNARY_SILU:      task_func = unary_task_f32_tiled_unary_silu;     break;
+                case HTP_OP_UNARY_GELU:      task_func = unary_task_f32_tiled_unary_gelu;     break;
                case HTP_OP_UNARY_SOFTPLUS:  task_func = unary_task_f32_tiled_unary_softplus; break;
                case HTP_OP_UNARY_TANH:      task_func = unary_task_f32_tiled_unary_tanh;     break;
                case HTP_OP_TRI:             task_func = unary_task_f32_tiled_tri;            break;
@@ -934,6 +988,8 @@ static int execute_op_unary_f32(struct htp_ops_context * octx) {
                case HTP_OP_UNARY_NEG:       task_func = unary_task_f32_unary_neg;            break;
                case HTP_OP_UNARY_EXP:       task_func = unary_task_f32_unary_exp;            break;
                case HTP_OP_UNARY_SIGMOID:   task_func = unary_task_f32_unary_sigmoid;        break;
+                case HTP_OP_UNARY_SILU:      task_func = unary_task_f32_unary_silu;           break;
+                case HTP_OP_UNARY_GELU:      task_func = unary_task_f32_unary_gelu;           break;
                case HTP_OP_UNARY_SOFTPLUS:  task_func = unary_task_f32_unary_softplus;       break;
                case HTP_OP_UNARY_TANH:      task_func = unary_task_f32_unary_tanh;           break;
                case HTP_OP_L2_NORM:         task_func = unary_task_f32_l2_norm;              break;
@@ -51,6 +51,8 @@ static inline bool htp_op_is_unary(uint32_t opcode) {
        case HTP_OP_UNARY_NEG:
        case HTP_OP_UNARY_EXP:
        case HTP_OP_UNARY_SIGMOID:
+        case HTP_OP_UNARY_SILU:
+        case HTP_OP_UNARY_GELU:
        case HTP_OP_UNARY_SOFTPLUS:
        case HTP_OP_UNARY_TANH:
        case HTP_OP_L2_NORM:
@@ -1218,8 +1218,9 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                   (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0);
        case GGML_OP_PAD_REFLECT_1D:
        case GGML_OP_TIMESTEP_EMBEDDING:
-        case GGML_OP_LEAKY_RELU:
            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_LEAKY_RELU:
+            return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
        case GGML_OP_ARGSORT:
        case GGML_OP_TOP_K:
        case GGML_OP_ARANGE:
@@ -355,6 +355,30 @@ struct ggml_webgpu_conv2d_pipeline_key_hash {
    }
 };

+// Same type fields as conv2d plus the input layout (WHCN vs CWHN).
+struct ggml_webgpu_conv2d_dw_pipeline_key {
+    ggml_type weight_type;
+    ggml_type input_type;
+    ggml_type output_type;
+    bool      whcn;
+
+    bool operator==(const ggml_webgpu_conv2d_dw_pipeline_key & other) const {
+        return weight_type == other.weight_type && input_type == other.input_type && output_type == other.output_type &&
+               whcn == other.whcn;
+    }
+};
+
+struct ggml_webgpu_conv2d_dw_pipeline_key_hash {
+    size_t operator()(const ggml_webgpu_conv2d_dw_pipeline_key & key) const {
+        size_t seed = 0;
+        ggml_webgpu_hash_combine(seed, key.weight_type);
+        ggml_webgpu_hash_combine(seed, key.input_type);
+        ggml_webgpu_hash_combine(seed, key.output_type);
+        ggml_webgpu_hash_combine(seed, key.whcn);
+        return seed;
+    }
+};
+
 /** Im2Col **/
 struct ggml_webgpu_im2col_pipeline_key {
    ggml_type input_type;
@@ -1210,6 +1234,8 @@ class ggml_webgpu_shader_lib {
        soft_max_pipelines;
    std::unordered_map<ggml_webgpu_conv2d_pipeline_key, webgpu_pipeline, ggml_webgpu_conv2d_pipeline_key_hash>
        conv2d_pipelines;
+    std::unordered_map<ggml_webgpu_conv2d_dw_pipeline_key, webgpu_pipeline, ggml_webgpu_conv2d_dw_pipeline_key_hash>
+        conv2d_dw_pipelines;
    std::unordered_map<ggml_webgpu_im2col_pipeline_key, webgpu_pipeline, ggml_webgpu_im2col_pipeline_key_hash>
        im2col_pipelines;

@@ -3172,6 +3198,50 @@ class ggml_webgpu_shader_lib {
        return conv2d_pipelines[key];
    }

+    // whcn selects the input layout: contiguous WHCN vs contiguous-channels CWHN
+    webgpu_pipeline get_conv2d_dw_pipeline(const ggml_webgpu_shader_lib_context & context, bool whcn) {
+        ggml_webgpu_conv2d_dw_pipeline_key key = {};
+        key.weight_type                        = context.src0->type;
+        key.input_type                         = context.src1->type;
+        key.output_type                        = context.dst->type;
+        key.whcn                               = whcn;
+
+        auto it = conv2d_dw_pipelines.find(key);
+        if (it != conv2d_dw_pipelines.end()) {
+            return it->second;
+        }
+
+        std::vector<std::string> defines;
+        std::string              variant = whcn ? "conv_2d_dw_whcn" : "conv_2d_dw_cwhn";
+
+        auto push_type_defines = [&](const char * prefix, ggml_type type) {
+            std::string s_prefix = prefix;
+            if (type == GGML_TYPE_F32) {
+                defines.push_back(s_prefix + "_F32");
+            } else if (type == GGML_TYPE_F16) {
+                defines.push_back(s_prefix + "_F16");
+            } else {
+                GGML_ABORT("Unsupported type for CONV_2D_DW shader");
+            }
+        };
+
+        push_type_defines("WEIGHT", key.weight_type);
+        push_type_defines("INPUT", key.input_type);
+        push_type_defines("OUTPUT", key.output_type);
+        if (whcn) {
+            defines.push_back("WHCN");
+        }
+        defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
+
+        auto processed           = preprocessor.preprocess(wgsl_conv2d_dw, defines);
+        auto decisions           = std::make_shared<ggml_webgpu_generic_shader_decisions>();
+        decisions->wg_size       = context.max_wg_size;
+        webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
+        pipeline.context         = decisions;
+        conv2d_dw_pipelines[key] = pipeline;
+        return conv2d_dw_pipelines[key];
+    }
+
    webgpu_pipeline get_im2col_pipeline(const ggml_webgpu_shader_lib_context & context) {
        ggml_webgpu_im2col_pipeline_key key = {};
        key.input_type                      = context.src1->type;
@@ -978,6 +978,67 @@ static webgpu_encoded_op ggml_webgpu_conv_2d(webgpu_context & ctx,
    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
 }

+// Same param/binding layout as conv_2d; the shader differs
+static webgpu_encoded_op ggml_webgpu_conv_2d_dw(webgpu_context & ctx,
+                                                ggml_tensor *    src0,
+                                                ggml_tensor *    src1,
+                                                ggml_tensor *    dst) {
+    const int32_t s0 = ggml_get_op_params_i32(dst, 0);
+    const int32_t s1 = ggml_get_op_params_i32(dst, 1);
+    const int32_t p0 = ggml_get_op_params_i32(dst, 2);
+    const int32_t p1 = ggml_get_op_params_i32(dst, 3);
+    const int32_t d0 = ggml_get_op_params_i32(dst, 4);
+    const int32_t d1 = ggml_get_op_params_i32(dst, 5);
+
+    // Scalar params matching conv2d_dw.wgsl (weight src0 [KW,KH,1,C], input src1, output dst).
+    std::vector<uint32_t> params = {
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)),
+        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)),
+
+        (uint32_t) ggml_nelements(dst),
+        (uint32_t) dst->ne[2],
+        (uint32_t) dst->ne[3],
+        (uint32_t) dst->ne[0],
+        (uint32_t) dst->ne[1],
+        (uint32_t) src1->ne[0],
+        (uint32_t) src1->ne[1],
+        (uint32_t) src0->ne[0],
+        (uint32_t) src0->ne[1],
+
+        (uint32_t) s0,
+        (uint32_t) s1,
+        (uint32_t) p0,
+        (uint32_t) p1,
+        (uint32_t) d0,
+        (uint32_t) d1,
+    };
+
+    std::vector<wgpu::BindGroupEntry> entries = {
+        ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0),
+        ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1),
+        ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst),
+    };
+
+    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+    shader_lib_ctx.src0                           = src0;
+    shader_lib_ctx.src1                           = src1;
+    shader_lib_ctx.dst                            = dst;
+    shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+
+    // Input layout: contiguous -> WHCN, contiguous-channels -> CWHN
+    const bool      whcn      = ggml_is_contiguous(src1);
+    webgpu_pipeline pipeline  = ctx->shader_lib->get_conv2d_dw_pipeline(shader_lib_ctx, whcn);
+    auto *          decisions = static_cast<ggml_webgpu_generic_shader_decisions *>(pipeline.context.get());
+
+    uint32_t wg_x;
+    uint32_t wg_y;
+    uint32_t total_wg = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size);
+    compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y);
+
+    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
+}
+
 static webgpu_encoded_op ggml_webgpu_im2col(webgpu_context & ctx,
                                            ggml_tensor *    src0,
                                            ggml_tensor *    src1,
@@ -3164,6 +3225,8 @@ static std::optional<webgpu_encoded_op> ggml_webgpu_encode(webgpu_context ctx,
            return ggml_webgpu_sum_rows(ctx, src0, node);
        case GGML_OP_CONV_2D:
            return ggml_webgpu_conv_2d(ctx, src0, src1, node);
+        case GGML_OP_CONV_2D_DW:
+            return ggml_webgpu_conv_2d_dw(ctx, src0, src1, node);
        case GGML_OP_IM2COL:
            return ggml_webgpu_im2col(ctx, src0, src1, node);
        case GGML_OP_UPSCALE:
@@ -4349,6 +4412,12 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
                          (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) &&
                          (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
            break;
+        case GGML_OP_CONV_2D_DW:
+            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
+                          (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) &&
+                          (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16) &&
+                          (ggml_is_contiguous(src1) || ggml_is_contiguous_channels(src1));
+            break;
        case GGML_OP_IM2COL:
            supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) &&
                          (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
@@ -0,0 +1,137 @@
+#include "common_decls.tmpl"
+enable f16;
+
+// Ported from the Vulkan backend's conv2d_dw.comp. Two variants (based on WHCN)
+// selected by the input (src1) layout: contiguous -> WHCN, else CWHN.
+// weight (src0) is [KW,KH,1,C]; output matches the input layout.
+
+@group(0) @binding(0)
+#if defined(WEIGHT_F32)
+var<storage, read_write> weights: array<f32>;
+#elif defined(WEIGHT_F16)
+var<storage, read_write> weights: array<f16>;
+#endif
+
+@group(0) @binding(1)
+#if defined(INPUT_F32)
+var<storage, read_write> input: array<f32>;
+#elif defined(INPUT_F16)
+var<storage, read_write> input: array<f16>;
+#endif
+
+@group(0) @binding(2)
+#if defined(OUTPUT_F32)
+var<storage, read_write> output: array<f32>;
+#elif defined(OUTPUT_F16)
+var<storage, read_write> output: array<f16>;
+#endif
+
+struct Params {
+    offset_w: u32,
+    offset_i: u32,
+    offset_o: u32,
+
+    ne: u32,
+    channels: u32,
+    batches: u32,
+    dst_w: u32, dst_h: u32,
+    src_w: u32, src_h: u32,
+    knl_w: u32, knl_h: u32,
+
+    stride_x: i32, stride_y: i32,
+    pad_x: i32, pad_y: i32,
+    dilation_x: i32, dilation_y: i32,
+};
+
+@group(0) @binding(3)
+var<uniform> params: Params;
+
+fn load_weight(idx: u32) -> f32 {
+    #if defined(WEIGHT_F32)
+        return weights[idx];
+    #elif defined(WEIGHT_F16)
+        return f32(weights[idx]);
+    #endif
+}
+fn load_input(idx: u32) -> f32 {
+    #if defined(INPUT_F32)
+        return input[idx];
+    #elif defined(INPUT_F16)
+        return f32(input[idx]);
+    #endif
+}
+fn store_output(idx: u32, val: f32) {
+    #if defined(OUTPUT_F32)
+        output[idx] = val;
+    #elif defined(OUTPUT_F16)
+        output[idx] = f16(val);
+    #endif
+}
+
+#if defined(WHCN)
+// Input/output/kernel contiguous in [W, H, C, N] order (kernel [KW,KH,C]).
+fn conv_2d_dw(idx: u32) -> f32 {
+    let i0    = idx / params.dst_w;
+    let dst_x = idx - i0 * params.dst_w;
+    let i1    = i0 / params.dst_h;
+    let dst_y = i0 - i1 * params.dst_h;
+    let n     = i1 / params.channels;
+    let c     = i1 - n * params.channels;
+
+    let src_i = params.offset_i + n * params.channels * params.src_h * params.src_w
+                                + c * params.src_h * params.src_w;
+    let knl_i = params.offset_w + c * params.knl_h * params.knl_w;
+
+    var sum: f32 = 0.0;
+    for (var ky: u32 = 0u; ky < params.knl_h; ky += 1u) {
+        let src_y = i32(dst_y) * params.stride_y + i32(ky) * params.dilation_y - params.pad_y;
+        if (src_y < 0 || src_y >= i32(params.src_h)) { continue; }
+        for (var kx: u32 = 0u; kx < params.knl_w; kx += 1u) {
+            let src_x = i32(dst_x) * params.stride_x + i32(kx) * params.dilation_x - params.pad_x;
+            if (src_x < 0 || src_x >= i32(params.src_w)) { continue; }
+            let v = load_input(src_i + u32(src_y) * params.src_w + u32(src_x));
+            let k = load_weight(knl_i + ky * params.knl_w + kx);
+            sum += v * k;
+        }
+    }
+    return sum;
+}
+#else
+// Channels contiguous (CWHN): channel is the innermost axis.
+fn conv_2d_dw(idx: u32) -> f32 {
+    let i0    = idx / params.channels;
+    let c     = idx - i0 * params.channels;
+    let i1    = i0 / params.dst_w;
+    let dst_x = i0 - i1 * params.dst_w;
+    let n     = i1 / params.dst_h;
+    let dst_y = i1 - n * params.dst_h;
+
+    let src_i   = params.offset_i + n * params.channels * params.src_h * params.src_w;
+    let src_row = params.src_w * params.channels;
+    let knl_row = params.knl_w * params.channels;
+
+    var sum: f32 = 0.0;
+    for (var ky: u32 = 0u; ky < params.knl_h; ky += 1u) {
+        let src_y = i32(dst_y) * params.stride_y + i32(ky) * params.dilation_y - params.pad_y;
+        if (src_y < 0 || src_y >= i32(params.src_h)) { continue; }
+        for (var kx: u32 = 0u; kx < params.knl_w; kx += 1u) {
+            let src_x = i32(dst_x) * params.stride_x + i32(kx) * params.dilation_x - params.pad_x;
+            if (src_x < 0 || src_x >= i32(params.src_w)) { continue; }
+            let v = load_input(src_i + u32(src_y) * src_row + u32(src_x) * params.channels + c);
+            let k = load_weight(params.offset_w + ky * knl_row + kx * params.channels + c);
+            sum += v * k;
+        }
+    }
+    return sum;
+}
+#endif
+
+@compute @workgroup_size(WG_SIZE)
+fn main(
+    @builtin(global_invocation_id) gid: vec3<u32>,
+    @builtin(num_workgroups) num_wg: vec3<u32>
+) {
+    let idx = gid.x + (num_wg.x * u32(WG_SIZE)) * gid.y;
+    if (idx >= params.ne) { return; }
+    store_output(params.offset_o + idx, conv_2d_dw(idx));
+}
@@ -202,6 +202,16 @@ extern "C" {
        LLAMA_SPLIT_MODE_TENSOR = 3,
    };

+    enum llama_load_mode {
+        LLAMA_LOAD_MODE_NONE      = 0, // no special loading mode
+        LLAMA_LOAD_MODE_MMAP      = 1, // memory map the model
+        LLAMA_LOAD_MODE_MLOCK     = 2, // mmap + force system to keep model in RAM rather than swapping or compressing
+        LLAMA_LOAD_MODE_DIRECT_IO = 3, // use direct I/O if available
+    };
+
+    LLAMA_API const char * llama_load_mode_name(enum llama_load_mode load_mode);
+    LLAMA_API enum llama_load_mode llama_load_mode_from_str(const char * str);
+
    enum llama_context_type {
        LLAMA_CONTEXT_TYPE_DEFAULT = 0,
        LLAMA_CONTEXT_TYPE_MTP     = 1,
@@ -301,6 +311,7 @@ extern "C" {

        int32_t n_gpu_layers; // number of layers to store in VRAM, a negative value means all layers
        enum llama_split_mode split_mode; // how to split the model across multiple GPUs
+        enum llama_load_mode  load_mode;  // how to load the model

        // the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE
        int32_t main_gpu;
@@ -321,9 +332,6 @@ extern "C" {

        // Keep the booleans together to avoid misalignment during copy-by-value.
        bool vocab_only;      // only load the vocabulary, no weights
-        bool use_mmap;        // use mmap if possible
-        bool use_direct_io;   // use direct io, takes precedence over use_mmap when supported
-        bool use_mlock;       // force system to keep model in RAM
        bool check_tensors;   // validate model tensor data
        bool use_extra_bufts; // use extra buffer types (used for weight repacking)
        bool no_host;         // bypass host buffer allowing extra buffers to be used
@@ -8,12 +8,15 @@
    {%- set thinking = false -%}
  {%- endif -%}
 {%- endif -%}
+{%- if not drop_thinking is defined -%}
+  {%- set drop_thinking = false -%}
+{%- endif -%}
 {%- set dsml_token = '｜DSML｜' -%}
 {%- set thinking_start_token = '<think>' -%}
 {%- set thinking_end_token = '</think>' -%}
 {%- set tools_header = '## Tools\n\nYou have access to a set of tools to help answer the user\'s question. You can invoke tools by writing a "<' + dsml_token + 'tool_calls>" block like the following:\n\n<' + dsml_token + 'tool_calls>\n<' + dsml_token + 'invoke name="$TOOL_NAME">\n<' + dsml_token + 'parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</' + dsml_token + 'parameter>\n...\n</' + dsml_token + 'invoke>\n<' + dsml_token + 'invoke name="$TOOL_NAME2">\n...\n</' + dsml_token + 'invoke>\n</' + dsml_token + 'tool_calls>\n\nString parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.\n\nIf thinking_mode is enabled (triggered by ' + thinking_start_token + '), you MUST output your complete reasoning inside ' + thinking_start_token + '...' + thinking_end_token + ' BEFORE any tool calls or final response.\n\nOtherwise, output directly after ' + thinking_end_token + ' with tool calls or final response.\n\n### Available Tool Schemas\n\n' -%}
 {%- set tools_footer = '\nYou MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.\n' -%}
-{%- set ns = namespace(system_prompt='', is_first_sp=true) -%}
+{%- set ns = namespace(system_prompt='', is_first_sp=true, has_tool_calls=false) -%}
 {%- for message in messages -%}
  {%- if message['role'] == 'system' -%}
    {%- if ns.is_first_sp -%}
@@ -46,6 +49,11 @@
  {%- endif -%}
 {%- endfor -%}
 {%- set state = namespace(in_user=false) -%}
+{%- for message in messages -%}
+  {%- if message['role'] == 'tool' -%}
+    {%- set ns.has_tool_calls = true -%}
+  {%- endif -%}
+{%- endfor -%}
 {%- for message in messages -%}
  {%- if message['role'] == 'user' or message['role'] == 'developer' -%}
    {%- if state.in_user -%}
@@ -67,7 +75,8 @@
    {%- set state.in_user = false -%}
    {{- '<｜Assistant｜>' -}}
    {%- set is_after_last_user = loop.index0 > last_user_idx.value -%}
-    {%- if is_after_last_user and thinking -%}
+    {%- set retain_reasoning = (not drop_thinking) or (is_after_last_user or ns.has_tool_calls) -%}
+    {%- if retain_reasoning and thinking -%}
      {{- thinking_start_token -}}
      {%- if message['reasoning_content'] is defined and message['reasoning_content'] -%}
        {{- message['reasoning_content'] -}}
@@ -28,7 +28,7 @@ LLAMA_BENCH_DB_FIELDS = [
    "model_type",   "model_size",   "model_n_params", "n_batch",    "n_ubatch",     "n_threads",
    "cpu_mask",     "cpu_strict",   "poll",           "type_k",     "type_v",       "n_gpu_layers",
    "split_mode",   "main_gpu",     "no_kv_offload",  "flash_attn", "tensor_split", "tensor_buft_overrides",
-    "use_mmap",     "embeddings",   "no_op_offload",  "n_prompt",   "n_gen",        "n_depth",
+    "load_mode",    "embeddings",   "no_op_offload",  "n_prompt",   "n_gen",        "n_depth",
    "test_time",    "avg_ns",       "stddev_ns",      "avg_ts",     "stddev_ts",    "n_cpu_moe",
    "fit_target",   "fit_min_ctx"
 ]
@@ -38,7 +38,7 @@ LLAMA_BENCH_DB_TYPES = [
    "TEXT",    "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER",
    "TEXT",    "INTEGER", "INTEGER", "TEXT",    "TEXT",    "INTEGER",
    "TEXT",    "INTEGER", "INTEGER", "INTEGER", "TEXT",    "TEXT",
-    "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER",
+    "TEXT", "INTEGER", "INTEGER", "INTEGER", "INTEGER", "INTEGER",
    "TEXT",    "INTEGER", "INTEGER", "REAL",    "REAL",    "INTEGER",
    "INTEGER", "INTEGER"
 ]
@@ -63,7 +63,7 @@ assert len(TEST_BACKEND_OPS_DB_FIELDS) == len(TEST_BACKEND_OPS_DB_TYPES)
 LLAMA_BENCH_KEY_PROPERTIES = [
    "cpu_info", "gpu_info", "backends", "n_gpu_layers", "n_cpu_moe", "tensor_buft_overrides", "model_filename", "model_type",
    "n_batch", "n_ubatch", "embeddings", "cpu_mask", "cpu_strict", "poll", "n_threads", "type_k", "type_v",
-    "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen", "n_depth",
+    "load_mode", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen", "n_depth",
    "fit_target", "fit_min_ctx"
 ]

@@ -73,7 +73,7 @@ TEST_BACKEND_OPS_KEY_PROPERTIES = [
 ]

 # Properties that are boolean and are converted to Yes/No for the table:
-LLAMA_BENCH_BOOL_PROPERTIES = ["embeddings", "cpu_strict", "use_mmap", "no_kv_offload", "flash_attn"]
+LLAMA_BENCH_BOOL_PROPERTIES = ["embeddings", "cpu_strict", "no_kv_offload", "flash_attn"]
 TEST_BACKEND_OPS_BOOL_PROPERTIES = ["supported", "passed"]

 # Header names for the table (llama-bench):
@@ -82,7 +82,7 @@ LLAMA_BENCH_PRETTY_NAMES = {
    "tensor_buft_overrides": "Tensor overrides", "model_filename": "File", "model_type": "Model", "model_size": "Model size [GiB]",
    "model_n_params": "Num. of par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", "embeddings": "Embeddings",
    "cpu_mask": "CPU mask", "cpu_strict": "CPU strict", "poll": "Poll", "n_threads": "Threads", "type_k": "K type", "type_v": "V type",
-    "use_mmap": "Use mmap", "no_kv_offload": "NKVO", "split_mode": "Split mode", "main_gpu": "Main GPU", "tensor_split": "Tensor split",
+    "load_mode": "Load mode", "no_kv_offload": "NKVO", "split_mode": "Split mode", "main_gpu": "Main GPU", "tensor_split": "Tensor split",
    "flash_attn": "FlashAttention",
 }

@@ -666,7 +666,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
    {LLM_TENSOR_HC_FFN_SCALE,               {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
    {LLM_TENSOR_ATTN_COMPRESSOR_WKV,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_ATTN_COMPRESSOR_WGATE,      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_ATTN_COMPRESSOR_APE,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_ATTN_COMPRESSOR_APE,        {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
    {LLM_TENSOR_ATTN_COMPRESSOR_NORM,       {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
    {LLM_TENSOR_ATTN_K_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_ATTN_V_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
@@ -833,7 +833,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
    {LLM_TENSOR_INDEXER_ATTN_Q_B,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_INDEXER_COMPRESSOR_WKV,     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_INDEXER_COMPRESSOR_WGATE,   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
-    {LLM_TENSOR_INDEXER_COMPRESSOR_APE,     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
+    {LLM_TENSOR_INDEXER_COMPRESSOR_APE,     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
    {LLM_TENSOR_INDEXER_COMPRESSOR_NORM,    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
    {LLM_TENSOR_FFN_GATE_TID2EID,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
    {LLM_TENSOR_NEXTN_PROJ_PRE,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
@@ -4,6 +4,7 @@
 #include "ggml.h"
 #include "gguf.h"
 #include "llama-hparams.h"
+#include "llama.h"

 #include <algorithm>
 #include <array>
@@ -522,8 +523,7 @@ llama_model_loader::llama_model_loader(
        const std::string & fname,
        std::vector<std::string> & splits,
        FILE * file,
-        bool use_mmap,
-        bool use_direct_io,
+        llama_load_mode load_mode,
        bool check_tensors,
        bool no_alloc,
        const llama_model_kv_override * param_overrides_p,
@@ -542,6 +542,9 @@ llama_model_loader::llama_model_loader(

    tensor_buft_overrides = param_tensor_buft_overrides_p;

+    this->use_mmap      = load_mode == LLAMA_LOAD_MODE_MMAP || load_mode == LLAMA_LOAD_MODE_MLOCK;
+    this->use_direct_io = load_mode == LLAMA_LOAD_MODE_DIRECT_IO;
+
    if (!fname.empty()) {
        // Load the main GGUF
        struct ggml_context * ctx = NULL;
@@ -562,20 +565,6 @@ llama_model_loader::llama_model_loader(
        files.emplace_back(new llama_file(fname.c_str(), "rb", use_direct_io));
        contexts.emplace_back(ctx);

-        if (use_mmap && use_direct_io) {
-            if (files.back()->has_direct_io()) {
-                LLAMA_LOG_WARN("%s: direct I/O is enabled, disabling mmap\n", __func__);
-                use_mmap = false;
-            } else {
-                LLAMA_LOG_WARN("%s: direct I/O is not available, using mmap\n", __func__);
-                use_direct_io = false;
-
-                // reopen file using std::fopen for mmap
-                files.pop_back();
-                files.emplace_back(new llama_file(fname.c_str(), "rb", false));
-            }
-        }
-
        // Save tensors data offset of the main file.
        // For subsidiary files, `meta` tensor data offset must not be used,
        // so we build a unified tensors index for weights.
@@ -816,13 +805,11 @@ llama_model_loader::llama_model_loader(
        }
    }

-    if (!llama_mmap::SUPPORTED) {
+    if (this->use_mmap && !llama_mmap::SUPPORTED) {
        LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
-        use_mmap = false;
+        this->use_mmap = false;
    }

-    this->use_mmap = use_mmap;
-    this->use_direct_io = use_direct_io;
    this->check_tensors = check_tensors;
    this->no_alloc = no_alloc;
 }
@@ -126,8 +126,7 @@ struct llama_model_loader {
        const std::string & fname,
        std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme
        FILE * file,
-        bool use_mmap,
-        bool use_direct_io,
+        llama_load_mode load_mode,
        bool check_tensors,
        bool no_alloc,
        const llama_model_kv_override * param_overrides_p,
@@ -16,6 +16,7 @@
 #include "llama-memory-hybrid-iswa.h"
 #include "llama-memory-recurrent.h"

+#include "llama.h"
 #include "models/models.h"

 #include "ggml.h"
@@ -1243,7 +1244,7 @@ void llama_model_base::load_vocab(llama_model_loader & ml) {

 bool llama_model_base::load_tensors(llama_model_loader & ml) {
    const auto & split_mode   = params.split_mode;
-    const auto & use_mlock    = params.use_mlock;
+    const bool use_mlock      = params.load_mode == LLAMA_LOAD_MODE_MLOCK;
    const auto & tensor_split = params.tensor_split;

    const int n_layer_all = hparams.n_layer_all;
@@ -1253,8 +1254,8 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {

    this->ml = &ml; // to be used by create_tensor() and load_arch_tensors()

-    LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s, direct_io = %s)\n",
-        __func__, ml.use_mmap ? "true" : "false", ml.use_direct_io ? "true" : "false");
+    LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (load_mode = %s)\n",
+        __func__, llama_load_mode_name(params.load_mode));

    // build a list of buffer types for the CPU and GPU devices
    pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host);
@@ -2318,15 +2319,13 @@ llama_model_params llama_model_default_params() {
        /*.tensor_buft_overrides       =*/ nullptr,
        /*.n_gpu_layers                =*/ -1,
        /*.split_mode                  =*/ LLAMA_SPLIT_MODE_LAYER,
+        /*.load_mode                   =*/ LLAMA_LOAD_MODE_MMAP,
        /*.main_gpu                    =*/ 0,
        /*.tensor_split                =*/ nullptr,
        /*.progress_callback           =*/ nullptr,
        /*.progress_callback_user_data =*/ nullptr,
        /*.kv_overrides                =*/ nullptr,
        /*.vocab_only                  =*/ false,
-        /*.use_mmap                    =*/ true,
-        /*.use_direct_io               =*/ false,
-        /*.use_mlock                   =*/ false,
        /*.check_tensors               =*/ false,
        /*.use_extra_bufts             =*/ true,
        /*.no_host                     =*/ false,
@@ -2,6 +2,7 @@
 #include "llama-model.h"
 #include "llama-model-loader.h"
 #include "llama-ext.h"
+#include "llama.h"

 #include <algorithm>
 #include <cmath>
@@ -876,15 +877,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
    // mmap consistently increases speed on Linux, and also increases speed on Windows with
    // hot cache. It may cause a slowdown on macOS, possibly related to free memory.
 #if defined(__linux__) || defined(_WIN32)
-    constexpr bool use_mmap = true;
+    constexpr llama_load_mode load_mode = LLAMA_LOAD_MODE_MMAP;
 #else
-    constexpr bool use_mmap = false;
+    constexpr llama_load_mode load_mode = LLAMA_LOAD_MODE_NONE;
 #endif

    const llama_model_kv_override * kv_overrides = params->kv_overrides;
    std::vector<std::string> splits = {};
    llama_model_loader ml(/*metadata*/ nullptr, /*set_tensor_data*/ nullptr, /*set_tensor_data_ud*/ nullptr,
-        fname_inp, splits, /*file*/ nullptr, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
+        fname_inp, splits, /*file*/ nullptr, /*load_mode*/ load_mode, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
    ml.init_mappings(false); // no prefetching

    auto mparams = llama_model_default_params();
@@ -46,6 +46,28 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty
    GGML_ABORT("fatal error");
 }

+const char * llama_load_mode_name(enum llama_load_mode load_mode) {
+    switch (load_mode) {
+        case LLAMA_LOAD_MODE_NONE:
+            return "none";
+        case LLAMA_LOAD_MODE_MMAP:
+            return "mmap";
+        case LLAMA_LOAD_MODE_MLOCK:
+            return "mlock";
+        case LLAMA_LOAD_MODE_DIRECT_IO:
+            return "dio";
+    }
+    GGML_ABORT("fatal error");
+}
+
+enum llama_load_mode llama_load_mode_from_str(const char * str) {
+    if (std::strcmp(str, "none")  == 0) { return LLAMA_LOAD_MODE_NONE;      }
+    if (std::strcmp(str, "mmap")  == 0) { return LLAMA_LOAD_MODE_MMAP;      }
+    if (std::strcmp(str, "mlock") == 0) { return LLAMA_LOAD_MODE_MLOCK;     }
+    if (std::strcmp(str, "dio")   == 0) { return LLAMA_LOAD_MODE_DIRECT_IO; }
+    throw std::invalid_argument(std::string("unknown load mode: ") + str);
+}
+
 struct llama_sampler_chain_params llama_sampler_chain_default_params() {
    struct llama_sampler_chain_params result = {
        /*.no_perf =*/ true,
@@ -279,7 +301,7 @@ static bool llama_prepare_model_devices(const llama_model_params & params, llama
 static std::pair<int, llama_model *> llama_model_load(struct gguf_context * metadata, llama_model_set_tensor_data_t set_tensor_data, void * set_tensor_data_ud,
        const std::string & fname, std::vector<std::string> & splits, FILE * file, llama_model_params & params) {
    try {
-        llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.use_mmap, params.use_direct_io,
+        llama_model_loader ml(metadata, set_tensor_data, set_tensor_data_ud, fname, splits, file, params.load_mode,
            params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);

        ml.print_info();
@@ -412,7 +434,7 @@ struct llama_model * llama_model_init_from_user(
    GGML_ASSERT(metadata != nullptr);
    std::string path_model;
    std::vector<std::string> splits = {};
-    params.use_mmap = false;
+    params.load_mode = LLAMA_LOAD_MODE_NONE;
    params.use_extra_bufts = false;
    return llama_model_load_from_file_impl(metadata, set_tensor_data, set_tensor_data_ud, path_model, splits, /*file*/ nullptr, params);
 }
@@ -1,6 +1,7 @@
 #include "arg.h"
 #include "common.h"
 #include "download.h"
+#include "llama.h"

 #include <string>
 #include <vector>
@@ -102,11 +103,9 @@ static void test(void) {
    argv = {"binary_name", "--draft", "123"};
    assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_EMBEDDING));

-    // negated arg
-    argv = {"binary_name", "--no-mmap"};
+    argv = {"binary_name", "-lm", "hello"};
    assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));

-
    printf("test-arg-parser: test valid usage\n\n");

    argv = {"binary_name", "-m", "model_file.gguf"};
@@ -132,6 +131,22 @@ static void test(void) {
    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_SPECULATIVE));
    assert(params.speculative.draft.n_max == 123);

+    argv = {"binary_name", "-lm", "none"};
+    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+    assert(params.load_mode == LLAMA_LOAD_MODE_NONE);
+
+    argv = {"binary_name", "-lm", "mmap"};
+    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+    assert(params.load_mode == LLAMA_LOAD_MODE_MMAP);
+
+    argv = {"binary_name", "-lm", "mlock"};
+    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+    assert(params.load_mode == LLAMA_LOAD_MODE_MLOCK);
+
+    argv = {"binary_name", "-lm", "dio"};
+    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+    assert(params.load_mode == LLAMA_LOAD_MODE_DIRECT_IO);
+
    // multi-value args (CSV)
    argv = {"binary_name", "--lora", "file1.gguf,\"file2,2.gguf\",\"file3\"\"3\"\".gguf\",file4\".gguf"};
    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
@@ -158,13 +173,32 @@ static void test(void) {
    assert(params.model.path == "blah.gguf");
    assert(params.cpuparams.n_threads == 1010);

+    setenv("LLAMA_ARG_LOAD_MODE", "blah", true);
+    argv = {"binary_name"};
+    assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+
+    setenv("LLAMA_ARG_LOAD_MODE", "mmap", true);
+    argv = {"binary_name"};
+    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+    assert(params.load_mode == LLAMA_LOAD_MODE_MMAP);
+
+    setenv("LLAMA_ARG_LOAD_MODE", "mlock", true);
+    argv = {"binary_name"};
+    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+    assert(params.load_mode == LLAMA_LOAD_MODE_MLOCK);
+
+    setenv("LLAMA_ARG_LOAD_MODE", "dio", true);
+    argv = {"binary_name"};
+    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+    assert(params.load_mode == LLAMA_LOAD_MODE_DIRECT_IO);
+
    printf("test-arg-parser: test negated environment variables\n\n");

-    setenv("LLAMA_ARG_MMAP", "0", true);
+    setenv("LLAMA_ARG_LOAD_MODE", "none", true);
    setenv("LLAMA_ARG_NO_PERF", "1", true); // legacy format
    argv = {"binary_name"};
    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
-    assert(params.use_mmap == false);
+    assert(params.load_mode == LLAMA_LOAD_MODE_NONE);
    assert(params.no_perf == true);

    printf("test-arg-parser: test environment variables being overwritten\n\n");
@@ -109,6 +109,15 @@ static void assert_contains(const std::string & haystack, const std::string & ne
    }
 }

+static void assert_not_contains(const std::string & haystack, const std::string & needle) {
+    if (haystack.find(needle) != std::string::npos) {
+        LOG_ERR("Expected NOT to contain: %s\n", needle.c_str());
+        LOG_ERR("Actual: %s\n", haystack.c_str());
+        common_log_flush(common_log_main());
+        throw std::runtime_error("Test failed");
+    }
+}
+
 static void assert_ends_with(const std::string & str, const std::string & suffix) {
    if (str.size() < suffix.size() ||
        str.compare(str.size() - suffix.size(), suffix.size(), suffix) != 0) {
@@ -4016,6 +4025,132 @@ static void test_template_output_peg_parsers(bool detailed_debug) {
            .run();
    }

+    // DeepSeek V4 tests - same DSML markup as V3.2, but the tool call block is named
+    // "tool_calls" and the non-thinking generation prompt ends in a bare </think>
+    // instead of an empty <think></think> pair.
+    {
+        auto tst = peg_tester("models/templates/deepseek-ai-DeepSeek-V4.jinja", detailed_debug);
+
+        // Pure content (non-thinking mode; generation prompt ends with </think>)
+        tst.test("Hello, world!\nWhat's up?")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .expect(message_assist)
+            .run();
+
+        // Thinking + content
+        tst.test("I'm\nthinking</think>Hello, world!\nWhat's up?")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .expect(message_assist_thoughts)
+            .run();
+
+        // Thinking + tool call (single, string param)
+        tst.test(
+               "Let me check the time</think>\n\n"
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"get_time\">\n"
+               "<｜DSML｜parameter name=\"city\" string=\"true\">Tokyo</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .tools({ get_time_tool })
+            .expect(message_with_tool_calls_and_reasoning("get_time", R"({"city": "Tokyo"})", "Let me check the time"))
+            .run();
+
+        // Tool call without reasoning (non-thinking mode), integer param (string="false")
+        tst.test(
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"special_function\">\n"
+               "<｜DSML｜parameter name=\"arg1\" string=\"false\">1</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(false)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .tools({ special_function_tool })
+            .expect(message_assist_call)
+            .run();
+
+        // Multiple parallel tool calls with reasoning
+        tst.test(
+               "Calling both</think>\n\n"
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"get_time\">\n"
+               "<｜DSML｜parameter name=\"city\" string=\"true\">Paris</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "<｜DSML｜invoke name=\"get_weather\">\n"
+               "<｜DSML｜parameter name=\"city\" string=\"true\">Paris</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .parallel_tool_calls(true)
+            .tools({ get_time_tool, get_weather_tool })
+            .expect(message_with_reasoning_content_and_multiple_tool_calls(
+                "Calling both", "",
+                { { "get_time", R"({"city": "Paris"})" }, { "get_weather", R"({"city": "Paris"})" } }))
+            .run();
+
+        // Tool call with content before tool calls
+        tst.test(
+               "Thinking about it</think>"
+               "Let me call the function.\n\n"
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"special_function\">\n"
+               "<｜DSML｜parameter name=\"arg1\" string=\"false\">1</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .tools({ special_function_tool })
+            .expect_reasoning("Thinking about it")
+            .expect_content("Let me call the function.")
+            .expect_tool_calls({
+                { "special_function", R"({"arg1": 1})", {} },
+            })
+            .run();
+
+        // Tool call with multiple params (mixed types)
+        tst.test(
+               "Multi-arg call</think>\n\n"
+               "<｜DSML｜tool_calls>\n"
+               "<｜DSML｜invoke name=\"magic_int\">\n"
+               "<｜DSML｜parameter name=\"ref\" string=\"false\">42</｜DSML｜parameter>\n"
+               "<｜DSML｜parameter name=\"name\" string=\"true\">foo bar</｜DSML｜parameter>\n"
+               "</｜DSML｜invoke>\n"
+               "</｜DSML｜tool_calls>")
+            .enable_thinking(true)
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .tools({ magic_int_tool })
+            .expect_reasoning("Multi-arg call")
+            .expect_tool_calls({
+                { "magic_int", R"({"ref": 42, "name": "foo bar"})", {} },
+            })
+            .run();
+
+        // Continuation tests
+        tst.test("world!\nWhat's up?")
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .enable_thinking(true)
+            .messages({ message_user, message_assist_prefill_content })
+            .add_generation_prompt(false)
+            .continue_final_message(COMMON_CHAT_CONTINUATION_CONTENT)
+            .expect_reasoning("I'm thinking")
+            .expect_content("Hello, world!\nWhat's up?")
+            .run();
+
+        tst.test(" thinking</think>Hello, world!\nWhat's up?")
+            .reasoning_format(COMMON_REASONING_FORMAT_DEEPSEEK)
+            .enable_thinking(true)
+            .messages({ message_user, message_assist_prefill_reasoning })
+            .add_generation_prompt(false)
+            .continue_final_message(COMMON_CHAT_CONTINUATION_REASONING)
+            .expect_reasoning("I'm thinking")
+            .expect_content("Hello, world!\nWhat's up?")
+            .run();
+    }
+
    // GLM-4.6 tests - format: <tool_call>function_name\n<arg_key>...</arg_key>\n<arg_value>...</arg_value>\n</tool_call>
    {
        auto tst = peg_tester("models/templates/GLM-4.6.jinja", detailed_debug);
@@ -5918,6 +6053,144 @@ static void test_developer_role_to_system_workaround() {
    }
 }

+// Verify reasoning-trace retention rules in the DeepSeek-V4 template:
+// all traces are retained unless drop_thinking is true AND the conversation
+// has no tool calls, in which case only the last (after-final-user) trace is
+// kept and earlier ones are dropped.
+static void test_deepseek_v4_thinking_retention() {
+    LOG_DBG("%s\n", __func__);
+
+    auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4.jinja");
+
+    common_chat_msg user_q1; user_q1.role = "user"; user_q1.content = "Question 1";
+    common_chat_msg user_q2; user_q2.role = "user"; user_q2.content = "Question 2";
+    common_chat_msg asst_a1 = simple_assist_msg("Answer 1", "thinking A1");
+    common_chat_msg asst_a2 = simple_assist_msg("Answer 2", "thinking A2");
+
+    common_chat_msg tool_assist = message_with_tool_calls("special_function", "{\"arg1\": 1}");
+    common_chat_msg tool_result; tool_result.role = "tool";
+    tool_result.tool_name = "special_function"; tool_result.tool_call_id = "0"; tool_result.content = "result";
+
+    // The template uses U+FF5C as the role separator and literal think tags
+    // for the reasoning block.
+    const std::string asst_marker   = "<\xef\xbd\x9c" "Assistant" "\xef\xbd\x9c>";
+    // Built via concatenation so the thinking tokens are not interpreted by
+    // tooling processing this source file.
+    const std::string think_start   = "<" "think" ">";
+    const std::string think_end     = "</" "think" ">";
+
+    const std::string think_a1      = asst_marker + think_start + "thinking A1" + think_end;
+    const std::string think_a2      = asst_marker + think_start + "thinking A2" + think_end;
+    const std::string asst_no_think = asst_marker + think_end;
+
+    auto render = [&](const std::vector<common_chat_msg> & messages, bool drop_thinking) {
+        common_chat_templates_inputs inputs;
+        inputs.messages = messages;
+        inputs.add_generation_prompt = false;
+        inputs.chat_template_kwargs["thinking"]     = "true";
+        inputs.chat_template_kwargs["drop_thinking"] = drop_thinking ? "true" : "false";
+        return common_chat_templates_apply(tmpls.get(), inputs).prompt;
+    };
+
+    // No tools, drop_thinking=false: all reasoning is retained.
+    {
+        auto prompt = render({ user_q1, asst_a1, user_q2, asst_a2 }, /* drop_thinking = */ false);
+        assert_contains(prompt, think_a1);
+        assert_contains(prompt, think_a2);
+    }
+
+    // No tools, drop_thinking=true: only the last reasoning trace is kept,
+    // earlier ones are dropped (the assistant block emits just the end token).
+    {
+        auto prompt = render({ user_q1, asst_a1, user_q2, asst_a2 }, /* drop_thinking = */ true);
+        assert_not_contains(prompt, think_a1);
+        assert_contains(prompt, think_a2);
+        // The dropped assistant turn still opens with the marker + bare end token.
+        assert_contains(prompt, asst_no_think + "Answer 1");
+    }
+
+    // Single assistant turn, drop_thinking=true: the only trace is the last
+    // one, so it must be retained even with drop_thinking set.
+    {
+        auto prompt = render({ user_q1, asst_a1 }, /* drop_thinking = */ true);
+        assert_contains(prompt, think_a1);
+    }
+
+    // Single assistant turn, drop_thinking=false: reasoning is retained.
+    {
+        auto prompt = render({ user_q1, asst_a1 }, /* drop_thinking = */ false);
+        assert_contains(prompt, think_a1);
+    }
+
+    // With tool calls, drop_thinking=true: tool presence forces all reasoning
+    // to be retained, including the pre-tool-call trace.
+    {
+        auto prompt = render({ user_q1, asst_a1, user_q2, tool_assist, tool_result, asst_a2 },
+                             /* drop_thinking = */ true);
+        assert_contains(prompt, think_a1);
+        assert_contains(prompt, think_a2);
+    }
+
+    // With tool calls, drop_thinking=false: all reasoning retained.
+    {
+        auto prompt = render({ user_q1, asst_a1, user_q2, tool_assist, tool_result, asst_a2 },
+                             /* drop_thinking = */ false);
+        assert_contains(prompt, think_a1);
+        assert_contains(prompt, think_a2);
+    }
+}
+
+// Verify that consecutive tool results are rendered in the tool call order of the
+// preceding assistant message (matched by tool call id), as required by the reference
+// DeepSeek-V4 implementation.
+static void test_deepseek_v4_tool_result_ordering() {
+    LOG_DBG("%s\n", __func__);
+
+    auto tmpls = read_templates("models/templates/deepseek-ai-DeepSeek-V4.jinja");
+
+    common_chat_msg user_q; user_q.role = "user"; user_q.content = "Question";
+
+    common_chat_msg assist_calls;
+    assist_calls.role = "assistant";
+    assist_calls.tool_calls.push_back({ "get_time",    "{\"city\": \"Paris\"}", "call_1" });
+    assist_calls.tool_calls.push_back({ "get_weather", "{\"city\": \"Paris\"}", "call_2" });
+
+    common_chat_msg time_result; time_result.role = "tool";
+    time_result.tool_name = "get_time"; time_result.tool_call_id = "call_1"; time_result.content = "12:00";
+    common_chat_msg weather_result; weather_result.role = "tool";
+    weather_result.tool_name = "get_weather"; weather_result.tool_call_id = "call_2"; weather_result.content = "sunny";
+
+    auto render = [&](const std::vector<common_chat_msg> & messages) {
+        common_chat_templates_inputs inputs;
+        inputs.messages = messages;
+        inputs.add_generation_prompt = false;
+        return common_chat_templates_apply(tmpls.get(), inputs).prompt;
+    };
+
+    // Results sent out of order are reordered to match the tool call order.
+    {
+        auto prompt = render({ user_q, assist_calls, weather_result, time_result });
+        assert_contains(prompt, "<tool_result>12:00</tool_result>\n\n<tool_result>sunny</tool_result>");
+    }
+
+    // Results already in call order stay put.
+    {
+        auto prompt = render({ user_q, assist_calls, time_result, weather_result });
+        assert_contains(prompt, "<tool_result>12:00</tool_result>\n\n<tool_result>sunny</tool_result>");
+    }
+
+    // Without tool call ids there is nothing to match against; order is preserved.
+    {
+        auto no_id_calls = assist_calls;
+        no_id_calls.tool_calls[0].id = "";
+        no_id_calls.tool_calls[1].id = "";
+        auto no_id_weather = weather_result; no_id_weather.tool_call_id = "";
+        auto no_id_time    = time_result;    no_id_time.tool_call_id = "";
+        auto prompt = render({ user_q, no_id_calls, no_id_weather, no_id_time });
+        assert_contains(prompt, "<tool_result>sunny</tool_result>\n\n<tool_result>12:00</tool_result>");
+    }
+}
+
 static void test_reasoning_budget_tokens_per_request() {
    LOG_DBG("%s\n", __func__);
    // Use Qwen3 template which has <think>...</think> reasoning markers.
@@ -6139,6 +6412,8 @@ int main(int argc, char ** argv) {
        test_tools_oaicompat_json_conversion();
        test_convert_responses_to_chatcmpl();
        test_developer_role_to_system_workaround();
+        test_deepseek_v4_thinking_retention();
+        test_deepseek_v4_tool_result_ordering();
        test_template_generation_prompt();
        test_reasoning_budget_tokens_per_request();
        test_reasoning_budget_message_per_request();
@@ -16,7 +16,7 @@ int main(int argc, char *argv[] ) {

    llama_backend_init();
    auto params = llama_model_params{};
-    params.use_mmap = false;
+    params.load_mode = LLAMA_LOAD_MODE_NONE;
    params.progress_callback = [](float progress, void * ctx){
        (void) ctx;
        return progress > 0.50;
@@ -312,7 +312,7 @@ int main(int argc, char ** argv) {

    {
        auto mparams = llama_model_default_params();
-        mparams.use_mlock  = false;
+        mparams.load_mode = LLAMA_LOAD_MODE_NONE;

        model = llama_model_load_from_file(params.model.c_str(), mparams);

@@ -55,9 +55,10 @@
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--rpc SERVERS` | comma-separated list of RPC servers (host:port)<br/>(env: LLAMA_ARG_RPC) |
-| `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
-| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)<br/>(env: LLAMA_ARG_DIO) |
+| `--mlock` | DEPRECATED in favor of `--load-mode`: mmap + force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
+| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_MMAP) |
+| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available<br/>(env: LLAMA_ARG_DIO) |
+| `-lm, --load-mode MODE` | model loading mode (default: mmap)<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
@@ -138,9 +138,10 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--rpc SERVERS` | comma-separated list of RPC servers (host:port)<br/>(env: LLAMA_ARG_RPC) |
-| `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
-| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)<br/>(env: LLAMA_ARG_DIO) |
+| `--mlock` | DEPRECATED in favor of `--load-mode`: mmap + force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
+| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_MMAP) |
+| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available<br/>(env: LLAMA_ARG_DIO) |
+| `-lm, --load-mode MODE` | model loading mode (default: mmap)<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
@@ -26,6 +26,7 @@
 #include "fit.h"
 #include "ggml.h"
 #include "llama.h"
+#include "log.h"

 #ifdef _WIN32
 #    define WIN32_LEAN_AND_MEAN
@@ -339,14 +340,13 @@ struct cmd_params {
    std::vector<int>                 n_gpu_layers;
    std::vector<int>                 n_cpu_moe;
    std::vector<llama_split_mode>    split_mode;
+    std::vector<llama_load_mode>     load_mode;
    std::vector<int>                 main_gpu;
    std::vector<bool>                no_kv_offload;
    std::vector<llama_flash_attn_type> flash_attn;
    std::vector<std::vector<ggml_backend_dev_t>> devices;
    std::vector<std::vector<float>>  tensor_split;
    std::vector<std::vector<llama_model_tensor_buft_override>> tensor_buft_overrides;
-    std::vector<bool>                use_mmap;
-    std::vector<bool>                use_direct_io;
    std::vector<bool>                embeddings;
    std::vector<bool>                no_op_offload;
    std::vector<bool>                no_host;
@@ -384,14 +384,13 @@ static const cmd_params cmd_params_defaults = {
    /* n_gpu_layers         */ { -1 },
    /* n_cpu_moe            */ { 0 },
    /* split_mode           */ { LLAMA_SPLIT_MODE_LAYER },
+    /* load_mode            */ { LLAMA_LOAD_MODE_MMAP },
    /* main_gpu             */ { 0 },
    /* no_kv_offload        */ { false },
    /* flash_attn           */ { LLAMA_FLASH_ATTN_TYPE_AUTO },
    /* devices              */ { {} },
    /* tensor_split         */ { std::vector<float>(llama_max_devices(), 0.0f) },
    /* tensor_buft_overrides*/ { std::vector<llama_model_tensor_buft_override>{ { nullptr, nullptr } } },
-    /* use_mmap             */ { true },
-    /* use_direct_io        */ { false },
    /* embeddings           */ { false },
    /* no_op_offload        */ { false },
    /* no_host              */ { false },
@@ -460,8 +459,9 @@ static void print_usage(int /* argc */, char ** argv) {
    printf("  -nkvo, --no-kv-offload <0|1>                (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
    printf("  -fa, --flash-attn <on|off|auto>             (default: %s)\n", join(transform_to_str(cmd_params_defaults.flash_attn, llama_flash_attn_type_name), ",").c_str());
    printf("  -dev, --device <dev0/dev1/...>              (default: auto)\n");
-    printf("  -mmp, --mmap <0|1>                          (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
-    printf("  -dio, --direct-io <0|1>                     (default: %s)\n", join(cmd_params_defaults.use_direct_io, ",").c_str());
+    printf("  -lm, --load-mode <none|mmap|mlock|dio>      (default: %s)\n", join(transform_to_str(cmd_params_defaults.load_mode, llama_load_mode_name), ",").c_str());
+    printf("  -mmp, --mmap <0|1>                          (DEPRECATED IN FAVOUR OF --load-mode)\n");
+    printf("  -dio, --direct-io <0|1>                     (DEPRECATED IN FAVOUR OF --load-mode)\n");
    printf("  -embd, --embeddings <0|1>                   (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
    printf("  -ts, --tensor-split <ts0/ts1/..>            (default: 0)\n");
    printf("  -ot --override-tensor <tensor name pattern>=<buffer type>;...\n");
@@ -769,6 +769,34 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                    break;
                }
                params.split_mode.insert(params.split_mode.end(), modes.begin(), modes.end());
+            } else if (arg == "-lm" || arg == "--load-mode") {
+                if (++i >= argc) {
+                    invalid_param = true;
+                    break;
+                }
+                auto p = string_split<std::string>(argv[i], split_delim);
+
+                std::vector<llama_load_mode> modes;
+                for (const auto & m : p) {
+                    llama_load_mode mode;
+                    if (m == "none") {
+                        mode = LLAMA_LOAD_MODE_NONE;
+                    } else if (m == "mmap") {
+                        mode = LLAMA_LOAD_MODE_MMAP;
+                    } else if (m == "mlock") {
+                        mode = LLAMA_LOAD_MODE_MLOCK;
+                    } else if (m == "dio") {
+                        mode = LLAMA_LOAD_MODE_DIRECT_IO;
+                    } else {
+                        invalid_param = true;
+                        break;
+                    }
+                    modes.push_back(mode);
+                }
+                if (invalid_param) {
+                    break;
+                }
+                params.load_mode.insert(params.load_mode.end(), modes.begin(), modes.end());
            } else if (arg == "-mg" || arg == "--main-gpu") {
                if (++i >= argc) {
                    invalid_param = true;
@@ -829,15 +857,39 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                    invalid_param = true;
                    break;
                }
+                LOG_WRN("DEPRECATED: -mmp and --mmap are deprecated in favour of --load-mode. Please use --load-mode mmap instead.");
                auto p = string_split<bool>(argv[i], split_delim);
-                params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end());
+
+                std::vector<llama_load_mode> modes;
+                for (const auto & m : p) {
+                    llama_load_mode mode;
+                    if (m) {
+                        mode = LLAMA_LOAD_MODE_MMAP;
+                    } else {
+                        mode = LLAMA_LOAD_MODE_NONE;
+                    }
+                    modes.push_back(mode);
+                }
+                params.load_mode.insert(params.load_mode.end(), modes.begin(), modes.end());
            } else if (arg == "-dio" || arg == "--direct-io") {
                if (++i >= argc) {
                    invalid_param = true;
                    break;
                }
+                LOG_WRN("DEPRECATED: -dio and --direct-io are deprecated in favour of --load-mode. Please use --load-mode dio instead.");
                auto p = string_split<bool>(argv[i], split_delim);
-                params.use_direct_io.insert(params.use_direct_io.end(), p.begin(), p.end());
+
+                std::vector<llama_load_mode> modes;
+                for (const auto & m : p) {
+                    llama_load_mode mode;
+                    if (m) {
+                        mode = LLAMA_LOAD_MODE_DIRECT_IO;
+                    } else {
+                        mode = LLAMA_LOAD_MODE_NONE;
+                    }
+                    modes.push_back(mode);
+                }
+                params.load_mode.insert(params.load_mode.end(), modes.begin(), modes.end());
            } else if (arg == "-embd" || arg == "--embeddings") {
                if (++i >= argc) {
                    invalid_param = true;
@@ -1093,6 +1145,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
    if (params.split_mode.empty()) {
        params.split_mode = cmd_params_defaults.split_mode;
    }
+    if (params.load_mode.empty()) {
+        params.load_mode = cmd_params_defaults.load_mode;
+    }
    if (params.main_gpu.empty()) {
        params.main_gpu = cmd_params_defaults.main_gpu;
    }
@@ -1111,12 +1166,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
    if (params.tensor_buft_overrides.empty()) {
        params.tensor_buft_overrides = cmd_params_defaults.tensor_buft_overrides;
    }
-    if (params.use_mmap.empty()) {
-        params.use_mmap = cmd_params_defaults.use_mmap;
-    }
-    if (params.use_direct_io.empty()) {
-        params.use_direct_io = cmd_params_defaults.use_direct_io;
-    }
    if (params.embeddings.empty()) {
        params.embeddings = cmd_params_defaults.embeddings;
    }
@@ -1164,14 +1213,13 @@ struct cmd_params_instance {
    int                n_gpu_layers;
    int                n_cpu_moe;
    llama_split_mode   split_mode;
+    llama_load_mode    load_mode;
    int                main_gpu;
    bool               no_kv_offload;
    llama_flash_attn_type flash_attn;
    std::vector<ggml_backend_dev_t> devices;
    std::vector<float> tensor_split;
    std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
-    bool               use_mmap;
-    bool               use_direct_io;
    bool               embeddings;
    bool               no_op_offload;
    bool               no_host;
@@ -1186,10 +1234,9 @@ struct cmd_params_instance {
            mparams.devices = const_cast<ggml_backend_dev_t *>(devices.data());
        }
        mparams.split_mode    = split_mode;
+        mparams.load_mode     = load_mode;
        mparams.main_gpu      = main_gpu;
        mparams.tensor_split  = tensor_split.data();
-        mparams.use_mmap      = use_mmap;
-        mparams.use_direct_io = use_direct_io;
        mparams.no_host       = no_host;

        if (n_cpu_moe <= 0) {
@@ -1235,9 +1282,7 @@ struct cmd_params_instance {
        return model == other.model && n_gpu_layers == other.n_gpu_layers && n_cpu_moe == other.n_cpu_moe &&
               split_mode == other.split_mode &&
               main_gpu == other.main_gpu && tensor_split == other.tensor_split &&
-               use_mmap == other.use_mmap && use_direct_io == other.use_direct_io &&
-               devices == other.devices &&
-               no_host == other.no_host &&
+               load_mode == other.load_mode && devices == other.devices && no_host == other.no_host &&
               vec_tensor_buft_override_equal(tensor_buft_overrides, other.tensor_buft_overrides);
    }

@@ -1270,12 +1315,11 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
    for (const auto & nl : params.n_gpu_layers)
    for (const auto & ncmoe : params.n_cpu_moe)
    for (const auto & sm : params.split_mode)
+    for (const auto & lm : params.load_mode)
    for (const auto & mg : params.main_gpu)
    for (const auto & devs : params.devices)
    for (const auto & ts : params.tensor_split)
    for (const auto & ot : params.tensor_buft_overrides)
-    for (const auto & mmp : params.use_mmap)
-    for (const auto & dio : params.use_direct_io)
    for (const auto & noh : params.no_host)
    for (const auto & embd : params.embeddings)
    for (const auto & nopo : params.no_op_offload)
@@ -1295,34 +1339,33 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                continue;
            }
            cmd_params_instance instance = {
-                /* .model        = */ m,
-                /* .n_prompt     = */ n_prompt,
-                /* .n_gen        = */ 0,
-                /* .n_depth      = */ nd,
-                /* .n_batch      = */ nb,
-                /* .n_ubatch     = */ nub,
-                /* .type_k       = */ tk,
-                /* .type_v       = */ tv,
-                /* .n_threads    = */ nt,
-                /* .cpu_mask     = */ cm,
-                /* .cpu_strict   = */ cs,
-                /* .poll         = */ pl,
-                /* .n_gpu_layers = */ nl,
-                /* .n_cpu_moe    = */ ncmoe,
-                /* .split_mode   = */ sm,
-                /* .main_gpu     = */ mg,
-                /* .no_kv_offload= */ nkvo,
-                /* .flash_attn   = */ fa,
-                /* .devices      = */ devs,
-                /* .tensor_split = */ ts,
+                /* .model                 = */ m,
+                /* .n_prompt              = */ n_prompt,
+                /* .n_gen                 = */ 0,
+                /* .n_depth               = */ nd,
+                /* .n_batch               = */ nb,
+                /* .n_ubatch              = */ nub,
+                /* .type_k                = */ tk,
+                /* .type_v                = */ tv,
+                /* .n_threads             = */ nt,
+                /* .cpu_mask              = */ cm,
+                /* .cpu_strict            = */ cs,
+                /* .poll                  = */ pl,
+                /* .n_gpu_layers          = */ nl,
+                /* .n_cpu_moe             = */ ncmoe,
+                /* .split_mode            = */ sm,
+                /* .load_mode             = */ lm,
+                /* .main_gpu              = */ mg,
+                /* .no_kv_offload         = */ nkvo,
+                /* .flash_attn            = */ fa,
+                /* .devices               = */ devs,
+                /* .tensor_split          = */ ts,
                /* .tensor_buft_overrides = */ ot,
-                /* .use_mmap     = */ mmp,
-                /* .use_direct_io= */ dio,
-                /* .embeddings   = */ embd,
-                /* .no_op_offload= */ nopo,
-                /* .no_host      = */ noh,
-                /* .fit_target   = */ fpt,
-                /* .fit_min_ctx  = */ fpc,
+                /* .embeddings            = */ embd,
+                /* .no_op_offload         = */ nopo,
+                /* .no_host               = */ noh,
+                /* .fit_target            = */ fpt,
+                /* .fit_min_ctx           = */ fpc,
            };
            instances.push_back(instance);
        }
@@ -1332,34 +1375,33 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                continue;
            }
            cmd_params_instance instance = {
-                /* .model        = */ m,
-                /* .n_prompt     = */ 0,
-                /* .n_gen        = */ n_gen,
-                /* .n_depth      = */ nd,
-                /* .n_batch      = */ nb,
-                /* .n_ubatch     = */ nub,
-                /* .type_k       = */ tk,
-                /* .type_v       = */ tv,
-                /* .n_threads    = */ nt,
-                /* .cpu_mask     = */ cm,
-                /* .cpu_strict   = */ cs,
-                /* .poll         = */ pl,
-                /* .n_gpu_layers = */ nl,
-                /* .n_cpu_moe    = */ ncmoe,
-                /* .split_mode   = */ sm,
-                /* .main_gpu     = */ mg,
-                /* .no_kv_offload= */ nkvo,
-                /* .flash_attn   = */ fa,
-                /* .devices      = */ devs,
-                /* .tensor_split = */ ts,
+                /* .model                 = */ m,
+                /* .n_prompt              = */ 0,
+                /* .n_gen                 = */ n_gen,
+                /* .n_depth               = */ nd,
+                /* .n_batch               = */ nb,
+                /* .n_ubatch              = */ nub,
+                /* .type_k                = */ tk,
+                /* .type_v                = */ tv,
+                /* .n_threads             = */ nt,
+                /* .cpu_mask              = */ cm,
+                /* .cpu_strict            = */ cs,
+                /* .poll                  = */ pl,
+                /* .n_gpu_layers          = */ nl,
+                /* .n_cpu_moe             = */ ncmoe,
+                /* .split_mode            = */ sm,
+                /* .load_mode             = */ lm,
+                /* .main_gpu              = */ mg,
+                /* .no_kv_offload         = */ nkvo,
+                /* .flash_attn            = */ fa,
+                /* .devices               = */ devs,
+                /* .tensor_split          = */ ts,
                /* .tensor_buft_overrides = */ ot,
-                /* .use_mmap     = */ mmp,
-                /* .use_direct_io= */ dio,
-                /* .embeddings   = */ embd,
-                /* .no_op_offload= */ nopo,
-                /* .no_host      = */ noh,
-                /* .fit_target   = */ fpt,
-                /* .fit_min_ctx  = */ fpc,
+                /* .embeddings            = */ embd,
+                /* .no_op_offload         = */ nopo,
+                /* .no_host               = */ noh,
+                /* .fit_target            = */ fpt,
+                /* .fit_min_ctx           = */ fpc,
            };
            instances.push_back(instance);
        }
@@ -1369,34 +1411,33 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                continue;
            }
            cmd_params_instance instance = {
-                /* .model        = */ m,
-                /* .n_prompt     = */ n_pg.first,
-                /* .n_gen        = */ n_pg.second,
-                /* .n_depth      = */ nd,
-                /* .n_batch      = */ nb,
-                /* .n_ubatch     = */ nub,
-                /* .type_k       = */ tk,
-                /* .type_v       = */ tv,
-                /* .n_threads    = */ nt,
-                /* .cpu_mask     = */ cm,
-                /* .cpu_strict   = */ cs,
-                /* .poll         = */ pl,
-                /* .n_gpu_layers = */ nl,
-                /* .n_cpu_moe    = */ ncmoe,
-                /* .split_mode   = */ sm,
-                /* .main_gpu     = */ mg,
-                /* .no_kv_offload= */ nkvo,
-                /* .flash_attn   = */ fa,
-                /* .devices      = */ devs,
-                /* .tensor_split = */ ts,
+                /* .model                 = */ m,
+                /* .n_prompt              = */ n_pg.first,
+                /* .n_gen                 = */ n_pg.second,
+                /* .n_depth               = */ nd,
+                /* .n_batch               = */ nb,
+                /* .n_ubatch              = */ nub,
+                /* .type_k                = */ tk,
+                /* .type_v                = */ tv,
+                /* .n_threads             = */ nt,
+                /* .cpu_mask              = */ cm,
+                /* .cpu_strict            = */ cs,
+                /* .poll                  = */ pl,
+                /* .n_gpu_layers          = */ nl,
+                /* .n_cpu_moe             = */ ncmoe,
+                /* .split_mode            = */ sm,
+                /* .load_mode             = */ lm,
+                /* .main_gpu              = */ mg,
+                /* .no_kv_offload         = */ nkvo,
+                /* .flash_attn            = */ fa,
+                /* .devices               = */ devs,
+                /* .tensor_split          = */ ts,
                /* .tensor_buft_overrides = */ ot,
-                /* .use_mmap     = */ mmp,
-                /* .use_direct_io= */ dio,
-                /* .embeddings   = */ embd,
-                /* .no_op_offload= */ nopo,
-                /* .no_host      = */ noh,
-                /* .fit_target   = */ fpt,
-                /* .fit_min_ctx  = */ fpc,
+                /* .embeddings            = */ embd,
+                /* .no_op_offload         = */ nopo,
+                /* .no_host               = */ noh,
+                /* .fit_target            = */ fpt,
+                /* .fit_min_ctx           = */ fpc,
            };
            instances.push_back(instance);
        }
@@ -1426,14 +1467,13 @@ struct test {
    int                      n_gpu_layers;
    int                      n_cpu_moe;
    llama_split_mode         split_mode;
+    llama_load_mode          load_mode;
    int                      main_gpu;
    bool                     no_kv_offload;
    llama_flash_attn_type    flash_attn;
    std::vector<ggml_backend_dev_t> devices;
    std::vector<float>       tensor_split;
    std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
-    bool                     use_mmap;
-    bool                     use_direct_io;
    bool                     embeddings;
    bool                     no_op_offload;
    bool                     no_host;
@@ -1466,14 +1506,13 @@ struct test {
        n_gpu_layers   = inst.n_gpu_layers;
        n_cpu_moe      = inst.n_cpu_moe;
        split_mode     = inst.split_mode;
+        load_mode      = inst.load_mode;
        main_gpu       = inst.main_gpu;
        no_kv_offload  = inst.no_kv_offload;
        flash_attn     = inst.flash_attn;
        devices        = inst.devices;
        tensor_split   = inst.tensor_split;
        tensor_buft_overrides = inst.tensor_buft_overrides;
-        use_mmap       = inst.use_mmap;
-        use_direct_io  = inst.use_direct_io;
        embeddings     = inst.embeddings;
        no_op_offload  = inst.no_op_offload;
        no_host        = inst.no_host;
@@ -1535,8 +1574,8 @@ struct test {
            "n_ubatch",       "n_threads",      "cpu_mask",      "cpu_strict",     "poll",
            "type_k",         "type_v",         "n_gpu_layers",  "n_cpu_moe",      "split_mode",
            "main_gpu",       "no_kv_offload",  "flash_attn",    "devices",        "tensor_split",
-            "tensor_buft_overrides",            "use_mmap",      "use_direct_io",  "embeddings",
-            "no_op_offload",  "no_host",        "fit_target",     "fit_min_ctx",
+            "tensor_buft_overrides",            "load_mode",     "embeddings",
+            "no_op_offload",  "no_host",        "fit_target",    "fit_min_ctx",
            "n_prompt",       "n_gen",          "n_depth",
            "test_time",      "avg_ns",         "stddev_ns",     "avg_ts",         "stddev_ts"
        };
@@ -1554,12 +1593,15 @@ struct test {
            return INT;
        }
        if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" ||
-            field == "use_mmap" || field == "use_direct_io" || field == "embeddings" || field == "no_host") {
+            field == "embeddings" || field == "no_host") {
            return BOOL;
        }
        if (field == "avg_ts" || field == "stddev_ts") {
            return FLOAT;
        }
+        if (field == "load_mode") {
+            return STRING;
+        }
        return STRING;
    }

@@ -1626,8 +1668,7 @@ struct test {
                                            devices_to_string(devices),
                                            tensor_split_str,
                                            tensor_buft_overrides_str,
-                                            std::to_string(use_mmap),
-                                            std::to_string(use_direct_io),
+                                            llama_load_mode_name(load_mode),
                                            std::to_string(embeddings),
                                            std::to_string(no_op_offload),
                                            std::to_string(no_host),
@@ -1806,18 +1847,15 @@ struct markdown_printer : public printer {
        if (field == "split_mode") {
            return 6;
        }
+        if (field == "load_mode") {
+            return 10;
+        }
        if (field == "flash_attn") {
            return 3;
        }
        if (field == "devices") {
            return -12;
        }
-        if (field == "use_mmap") {
-            return 4;
-        }
-        if (field == "use_direct_io") {
-            return 3;
-        }
        if (field == "test") {
            return 15;
        }
@@ -1852,11 +1890,8 @@ struct markdown_printer : public printer {
        if (field == "flash_attn") {
            return "fa";
        }
-        if (field == "use_mmap") {
-            return "mmap";
-        }
-        if (field == "use_direct_io") {
-            return "dio";
+        if (field == "load_mode") {
+            return "lm";
        }
        if (field == "embeddings") {
            return "embd";
@@ -1945,11 +1980,8 @@ struct markdown_printer : public printer {
        if (params.tensor_buft_overrides.size() > 1 || !vec_vec_tensor_buft_override_equal(params.tensor_buft_overrides, cmd_params_defaults.tensor_buft_overrides)) {
            fields.emplace_back("tensor_buft_overrides");
        }
-        if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) {
-            fields.emplace_back("use_mmap");
-        }
-        if (params.use_direct_io.size() > 1 || params.use_direct_io != cmd_params_defaults.use_direct_io) {
-            fields.emplace_back("use_direct_io");
+        if (params.load_mode.size() > 1 || params.load_mode != cmd_params_defaults.load_mode) {
+            fields.emplace_back("load_mode");
        }
        if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) {
            fields.emplace_back("embeddings");
@@ -238,6 +238,29 @@ struct decode_embd_batch {
    }
 };

+// Helper class to set non-causal attention via RAII
+class scope_non_causal {
+public:
+    scope_non_causal(llama_context * context, bool enabled) : context_(context), enabled_(enabled) {
+        if (enabled_) {
+            // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image
+            llama_set_causal_attn(context_, false);
+        }
+    }
+    ~scope_non_causal() {
+        if (enabled_) {
+            llama_set_causal_attn(context_, true);
+        }
+    }
+
+    scope_non_causal(const scope_non_causal &) = delete;
+    scope_non_causal & operator=(const scope_non_causal &) = delete;
+
+private:
+    llama_context * context_;
+    bool enabled_;
+};
+
 // Helper function for decoding an image whose embeddings have already been calculated
 int32_t mtmd_helper_decode_image_chunk(
        mtmd_context * ctx,
@@ -288,10 +311,7 @@ int32_t mtmd_helper_decode_image_chunk(
    }

    const bool use_non_causal = mtmd_decode_use_non_causal(ctx, chunk);
-    if (use_non_causal) {
-        llama_set_causal_attn(lctx, false);
-        // TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image
-    }
+    const scope_non_causal non_causal(lctx, use_non_causal);

    while (i_batch < n_img_batches) { // split into batches
        int pos_offset = i_batch*n_batch;
@@ -304,9 +324,6 @@ int32_t mtmd_helper_decode_image_chunk(
        int32_t ret = llama_decode(lctx, batch_embd_view);
        if (ret != 0) {
            LOG_ERR("failed to decode %s\n", name);
-            if (use_non_causal) {
-                llama_set_causal_attn(lctx, true);
-            }
            return ret;
        }

@@ -314,9 +331,6 @@ int32_t mtmd_helper_decode_image_chunk(
            ret = callback(batch_embd_view, user_data);
            if (ret != 0) {
                LOG_ERR("post-decode callback failed\n");
-                if (use_non_causal) {
-                    llama_set_causal_attn(lctx, true);
-                }
                return ret;
            }
        }
@@ -329,9 +343,6 @@ int32_t mtmd_helper_decode_image_chunk(
    n_past += mtmd_input_chunk_get_n_pos(chunk);
    *new_n_past = n_past;

-    if (use_non_causal) {
-        llama_set_causal_attn(lctx, true);
-    }
    return 0;
 }

@@ -72,9 +72,10 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `--rpc SERVERS` | comma-separated list of RPC servers (host:port)<br/>(env: LLAMA_ARG_RPC) |
-| `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--mmap, --no-mmap` | whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
-| `-dio, --direct-io, -ndio, --no-direct-io` | use DirectIO if available. (default: disabled)<br/>(env: LLAMA_ARG_DIO) |
+| `--mlock` | DEPRECATED in favor of `--load-mode`: mmap + force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
+| `--mmap, --no-mmap` | DEPRECATED in favor of `--load-mode`: whether to memory-map model. (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_MMAP) |
+| `-dio, --direct-io, -ndio, --no-direct-io` | DEPRECATED in favor of `--load-mode`: use DirectIO if available<br/>(env: LLAMA_ARG_DIO) |
+| `-lm, --load-mode MODE` | model loading mode (default: mmap)<br/>- none: no special loading mode<br/>- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)<br/>- mlock: mmap + force system to keep model in RAM rather than swapping or compressing<br/>- dio: use DirectIO if available<br/><br/>(env: LLAMA_ARG_LOAD_MODE) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
@@ -36,3 +36,7 @@ static/favicon*
 *storybook.log
 storybook-static
 *.code-workspace
+
+# Vitest browser mode failure artifacts
+.vitest-attachments/
+tests/**/__screenshots__/
@@ -16,3 +16,6 @@ build/
 /build/
 /.svelte-kit/
 test-results
+
+# Vendored third party sources, kept byte identical to upstream
+src/lib/vendors/
@@ -59,7 +59,8 @@ export default ts.config(
 			'.svelte-kit/**',
 			'test-results/**',
 			'.storybook/**/*',
-			'src/lib/services/sandbox-worker.js'
+			'src/lib/services/sandbox-worker.js',
+			'src/lib/vendors/**'
 		]
 	},
 	storybook.configs['flat/recommended']
@@ -0,0 +1,49 @@
+import { build } from 'esbuild';
+import { dirname, resolve } from 'path';
+import { fileURLToPath } from 'url';
+import type { Plugin } from 'vite';
+
+const __dirname = dirname(fileURLToPath(import.meta.url));
+
+const VENDORS_DIR = resolve(__dirname, '../src/lib/vendors');
+const VIRTUAL_ID = 'virtual:nerdamer';
+const RESOLVED_ID = '\0' + VIRTUAL_ID;
+
+/**
+ * Bundle the vendored nerdamer-prime source into a minified IIFE string,
+ * exposed as the `virtual:nerdamer` module. Flags mirror the upstream
+ * build (esbuild --bundle --minify --format=iife --global-name=nerdamer),
+ * so only human readable source lives in the repo and minification is a
+ * build artifact. Vendored under src/lib/vendors/, upstream snapshot:
+ * https://github.com/together-science/nerdamer-prime/commit/1936145f8af306ec0d883b9bfd7730aedd175c24
+ */
+export function nerdamerPlugin(): Plugin {
+	let bundled: string | null = null;
+
+	return {
+		name: 'llamacpp:nerdamer',
+		resolveId(id) {
+			return id === VIRTUAL_ID ? RESOLVED_ID : undefined;
+		},
+		async load(id) {
+			if (id !== RESOLVED_ID) return undefined;
+			if (bundled === null) {
+				const result = await build({
+					entryPoints: [resolve(VENDORS_DIR, 'nerdamer-prime/all.js')],
+					bundle: true,
+					minify: true,
+					format: 'iife',
+					globalName: 'nerdamer',
+					alias: {
+						'big-integer': resolve(VENDORS_DIR, 'big-integer/BigInteger.js'),
+						'decimal.js': resolve(VENDORS_DIR, 'decimal.js/decimal.js')
+					},
+					write: false,
+					logLevel: 'silent'
+				});
+				bundled = result.outputFiles[0].text;
+			}
+			return `export default ${JSON.stringify(bundled)};`;
+		}
+	};
+}
@@ -71,7 +71,9 @@

 <div class="flex items-center gap-1 {className}">
 	<DropdownMenu.Root bind:open={dropdownOpen}>
-		<Tooltip.Root>
+		<!-- ignoreNonKeyboardFocus prevents the tooltip from flashing when the
+		     menu closes and focus returns to the trigger -->
+		<Tooltip.Root ignoreNonKeyboardFocus>
 			<Tooltip.Trigger>
 				{#snippet child({ props })}
 					<DropdownMenu.Trigger
@@ -5,18 +5,18 @@
 	import * as Tooltip from '$lib/components/ui/tooltip';
 	import { useReasoningMenu } from '$lib/hooks/use-reasoning-menu.svelte';

-	let subOpen = $state(false);
-
 	const reasoning = useReasoningMenu();
 </script>

 {#if reasoning.modelSupportsThinking}
-	<DropdownMenu.Sub bind:open={subOpen}>
+	<DropdownMenu.Sub>
 		<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
 			{#if reasoning.thinkingEnabled}
 				<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-amber-400" />
-			{:else}
+			{:else if reasoning.isOff}
 				<LightbulbOff class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
+			{:else}
+				<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
 			{/if}

 			<span
@@ -27,7 +27,7 @@
 				Reasoning

 				<span class="capitalize text-muted-foreground">
-					{reasoning.thinkingEnabled ? reasoning.currentEffort : 'off'}
+					{reasoning.currentEffort}
 				</span>
 			</span>
 		</DropdownMenu.SubTrigger>
@@ -37,14 +37,13 @@
 		>
 			{#each reasoning.levels as level (level.value)}
 				{@const tokenLabel = reasoning.tokenLabel(level)}
-				<button
-					type="button"
-					class="flex w-full cursor-pointer items-center gap-3 rounded-md px-2 py-1.75 text-left text-sm transition-colors hover:bg-accent"
-					class:bg-accent={reasoning.isSelected(level)}
-					onclick={() => {
-						reasoning.select(level);
-						subOpen = false;
-					}}
+				<DropdownMenu.Item
+					class="flex w-full cursor-pointer items-center gap-3 rounded-md px-2 py-1.75 text-left text-sm transition-colors hover:bg-accent {reasoning.isSelected(
+						level
+					)
+						? 'bg-accent'
+						: ''}"
+					onclick={() => reasoning.select(level)}
 				>
 					{#if reasoning.isSelected(level)}
 						<Check class="{ICON_CLASS_DEFAULT} shrink-0 text-foreground" />
@@ -70,7 +69,7 @@
 							</Tooltip.Content>
 						</Tooltip.Root>
 					{/if}
-				</button>
+				</DropdownMenu.Item>
 			{/each}
 		</DropdownMenu.SubContent>
 	</DropdownMenu.Sub>
@@ -116,14 +116,16 @@

 							{#if reasoning.thinkingEnabled}
 								<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-amber-400" />
-							{:else}
+							{:else if reasoning.isOff}
 								<LightbulbOff class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
+							{:else}
+								<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
 							{/if}

 							<span class="flex-1">Reasoning</span>

 							<span class="text-xs capitalize text-muted-foreground">
-								{reasoning.thinkingEnabled ? reasoning.currentEffort : 'off'}
+								{reasoning.currentEffort}
 							</span>
 						</Collapsible.Trigger>

@@ -1,127 +0,0 @@
-<script lang="ts">
-	import { Check, Info, Lightbulb, LightbulbOff } from '@lucide/svelte';
-	import * as DropdownMenu from '$lib/components/ui/dropdown-menu';
-	import * as Tooltip from '$lib/components/ui/tooltip';
-	import { ReasoningEffort, MessageRole } from '$lib/enums';
-	import { REASONING_EFFORT_TOKENS } from '$lib/constants/reasoning-effort-tokens';
-	import { REASONING_EFFORT_LEVELS } from '$lib/constants/reasoning-effort';
-	import type { ReasoningEffortLevel } from '$lib/types';
-	import { DIALOG_SUBMENU_CONTENT, ICON_CLASS_DEFAULT } from '$lib/constants/css-classes';
-	import {
-		modelsStore,
-		checkModelSupportsThinking,
-		supportsThinking,
-		propsCacheVersion,
-		loadedModelIds
-	} from '$lib/stores/models.svelte';
-	import { chatStore } from '$lib/stores/chat.svelte';
-	import { conversationsStore, activeMessages } from '$lib/stores/conversations.svelte';
-	import { isRouterMode } from '$lib/stores/server.svelte';
-	import type { DatabaseMessage } from '$lib/types/database';
-
-	let thinkingEnabled = $derived(conversationsStore.getThinkingEnabled());
-	let currentEffort = $derived(conversationsStore.getReasoningEffort());
-	let isOff = $derived(!thinkingEnabled);
-	let subOpen = $state(false);
-
-	// Get conversation model from message history
-	let conversationModel = $derived(
-		chatStore.getConversationModel(activeMessages() as DatabaseMessage[])
-	);
-
-	let modelSupportsThinkingFromMessages = $derived.by(() => {
-		const modelId = isRouterMode() ? modelsStore.selectedModelName || conversationModel : null;
-		if (!modelId) return false;
-
-		const messages = conversationsStore.activeMessages;
-
-		return messages.some(
-			(m: DatabaseMessage) =>
-				m.role === MessageRole.ASSISTANT && m.model === modelId && !!m.reasoningContent
-		);
-	});
-
-	let modelSupportsThinking = $derived.by(() => {
-		loadedModelIds();
-		propsCacheVersion();
-
-		if (isRouterMode()) {
-			const modelId = modelsStore.selectedModelName || conversationModel;
-			return checkModelSupportsThinking(modelId ?? '') || modelSupportsThinkingFromMessages;
-		}
-
-		return supportsThinking() || modelSupportsThinkingFromMessages;
-	});
-
-	function isSelected(item: ReasoningEffortLevel): boolean {
-		if (item.isOff) return isOff;
-
-		return thinkingEnabled && currentEffort === item.value;
-	}
-
-	function handleSelection(item: ReasoningEffortLevel) {
-		if (item.isOff) {
-			conversationsStore.setThinkingEnabled(false);
-		} else {
-			conversationsStore.setThinkingEnabled(true);
-			conversationsStore.setReasoningEffort(item.value as ReasoningEffort);
-		}
-		subOpen = false;
-	}
-</script>
-
-{#if modelSupportsThinking}
-	<DropdownMenu.Sub bind:open={subOpen}>
-		<DropdownMenu.SubTrigger class="flex cursor-pointer items-center gap-2">
-			{#if thinkingEnabled}
-				<Lightbulb class="{ICON_CLASS_DEFAULT} shrink-0 text-amber-400" />
-			{:else}
-				<LightbulbOff class="{ICON_CLASS_DEFAULT} shrink-0 text-muted-foreground" />
-			{/if}
-
-			<span class="flex-1">Thinking</span>
-
-			{#if thinkingEnabled}
-				<span class="text-xs text-muted-foreground">{currentEffort}</span>
-			{:else}
-				<span class="text-xs text-muted-foreground">off</span>
-			{/if}
-		</DropdownMenu.SubTrigger>
-
-		<DropdownMenu.SubContent class={DIALOG_SUBMENU_CONTENT}>
-			{#each REASONING_EFFORT_LEVELS as level (level.value)}
-				<button
-					type="button"
-					class="flex w-full cursor-pointer items-center gap-2"
-					class:bg-accent={isSelected(level)}
-					onclick={() => handleSelection(level)}
-				>
-					<span class="flex-1 text-left">{level.label}</span>
-
-					{#if !level.isOff}
-						<span class="text-[11px] text-muted-foreground opacity-60">
-							{REASONING_EFFORT_TOKENS[level.value] === -1
-								? 'Unlimited'
-								: `Max ${REASONING_EFFORT_TOKENS[level.value].toLocaleString()} tokens`}
-						</span>
-					{/if}
-
-					{#if level.hasInfo}
-						<Tooltip.Root>
-							<Tooltip.Trigger>
-								<Info class="h-3.5 w-3.5 shrink-0 text-muted-foreground" />
-							</Tooltip.Trigger>
-							<Tooltip.Content side="left">
-								<p>Maximum thinking effort with extended context usage</p>
-							</Tooltip.Content>
-						</Tooltip.Root>
-					{/if}
-
-					{#if isSelected(level)}
-						<Check class="{ICON_CLASS_DEFAULT} shrink-0 text-foreground" />
-					{/if}
-				</button>
-			{/each}
-		</DropdownMenu.SubContent>
-	</DropdownMenu.Sub>
-{/if}
@@ -6,6 +6,7 @@ import type { ReasoningEffortLevel } from '$lib/types';
 * Keys match the ReasoningEffort enum values for type-safe lookups.
 */
 export const REASONING_EFFORT_LABELS: Record<string, string> = {
+	[ReasoningEffort.DEFAULT]: 'Default',
 	[ReasoningEffort.OFF]: 'Off',
 	[ReasoningEffort.LOW]: 'Low',
 	[ReasoningEffort.MEDIUM]: 'Medium',
@@ -14,7 +15,8 @@ export const REASONING_EFFORT_LABELS: Record<string, string> = {
 };

 export const REASONING_EFFORT_LEVELS: ReasoningEffortLevel[] = [
-	{ value: ReasoningEffort.OFF, label: 'Off', isOff: true },
+	{ value: ReasoningEffort.DEFAULT, label: 'Default' },
+	{ value: ReasoningEffort.OFF, label: 'Off' },
 	{ value: ReasoningEffort.LOW, label: 'Low' },
 	{ value: ReasoningEffort.MEDIUM, label: 'Medium' },
 	{ value: ReasoningEffort.HIGH, label: 'High' },
@@ -13,27 +13,44 @@ export const SANDBOX_EMPTY_OUTPUT = '(no output)';

 export const SANDBOX_TRUNCATION_NOTICE = '[output truncated]';

-export const SANDBOX_TOOL_DEFINITION: OpenAIToolDefinition = {
-	type: ToolCallType.FUNCTION,
-	function: {
-		name: SANDBOX_TOOL_NAME,
-		description:
-			'Execute JavaScript in a sandboxed browser worker (no DOM, no page access). ' +
-			'Top level await is supported. Use console.log to print intermediate values; ' +
-			'a top level return statement is captured as the result.',
-		parameters: {
-			type: JsonSchemaType.OBJECT,
-			properties: {
-				code: {
-					type: JsonSchemaType.STRING,
-					description: 'JavaScript source to execute'
+const NERDAMER_DESCRIPTION = `
+Symbolic/numeric math via \`nerdamer\` (pre-loaded, do not require, use it directly).
+nerdamer('diff(sin(x)/x,x)') or nerdamer.diff('sin(x)/x','x') → Expression; convert with .toString()/.text()/.toTeX(), or .evaluate() (→ still Expression, then .toString()).
+nerdamer(expr,{x:2}) substitutes only; chain .evaluate() or pass 'numer' for numeric result.
+solve(expr,var)→Symbol[]; solveEquations([eq1,..])→[[var,val],..] pairs.
+Functions: simplify/expand/factor(expr), diff(expr,var[,n]), integrate(expr,var), defint(expr,from,to,var), limit(expr,var,to), laplace(expr,t,s), ilt(expr,s,t), gcd/lcm(a,b), roots/coeffs/partfrac(expr,var), pfactor(n), numer/decimals/erf(expr), product/sum(expr,var,from,to), mean/median/stdev/variance(...vals).
+Object.keys(nerdamer).filter(k=>typeof nerdamer[k]==='function') lists all available functions. If you need a function not documented above, list them first — do not guess function names.`;
+
+/**
+ * Build the sandbox tool definition. When `includeSymbolicMath` is true,
+ * the description includes nerdamer API documentation; otherwise it
+ * describes a plain JavaScript sandbox.
+ */
+export function buildSandboxToolDefinition(includeSymbolicMath: boolean): OpenAIToolDefinition {
+	return {
+		type: ToolCallType.FUNCTION,
+		function: {
+			name: SANDBOX_TOOL_NAME,
+			description: includeSymbolicMath
+				? `Execute JS in a sandboxed browser worker (no DOM/page access). Top-level await ok; console.log for intermediates; top-level return is captured as result.${NERDAMER_DESCRIPTION}`
+				: 'Execute JS in a sandboxed browser worker (no DOM/page access). Top-level await ok; console.log for intermediates; top-level return is captured as result.',
+			parameters: {
+				type: JsonSchemaType.OBJECT,
+				properties: {
+					code: {
+						type: JsonSchemaType.STRING,
+						description: 'JavaScript source to execute'
+					},
+					timeout_ms: {
+						type: JsonSchemaType.NUMBER,
+						description: `Execution timeout in milliseconds, default ${SANDBOX_TIMEOUT_MS_DEFAULT}, max ${SANDBOX_TIMEOUT_MS_MAX}`
+					}
 				},
-				timeout_ms: {
-					type: JsonSchemaType.NUMBER,
-					description: `Execution timeout in milliseconds, default ${SANDBOX_TIMEOUT_MS_DEFAULT}, max ${SANDBOX_TIMEOUT_MS_MAX}`
-				}
-			},
-			required: ['code']
+				required: ['code']
+			}
 		}
-	}
-};
+	};
+}
+
+/** @deprecated Use {@link buildSandboxToolDefinition} instead. Kept for backward compatibility. */
+export const SANDBOX_TOOL_DEFINITION = buildSandboxToolDefinition(true);
@@ -67,6 +67,7 @@ export const SETTINGS_KEYS = {
 	EXCLUDE_REASONING_FROM_CONTEXT: 'excludeReasoningFromContext',
 	SHOW_RAW_OUTPUT_SWITCH: 'showRawOutputSwitch',
 	JS_SANDBOX_ENABLED: 'jsSandboxEnabled',
+	SYMBOLIC_MATH_ENABLED: 'symbolicMathEnabled',
 	// PY_INTERPRETER_ENABLED: 'pyInterpreterEnabled',
 	CUSTOM_JSON: 'customJson',
 	CUSTOM_CSS: 'customCss'
@@ -724,6 +724,15 @@ const SETTINGS_REGISTRY: Record<string, SettingsSectionEntry> = {
 					paramType: SyncableParameterType.BOOLEAN
 				}
 			},
+			{
+				key: SETTINGS_KEYS.SYMBOLIC_MATH_ENABLED,
+				label: 'Symbolic math (nerdamer)',
+				help: 'Pre-load nerdamer in the sandbox for symbolic computation: simplify, diff, integrate, solve, and more. Requires "JavaScript sandbox tool" to be enabled.',
+				defaultValue: false,
+				type: SettingsFieldType.CHECKBOX,
+				section: SETTINGS_SECTION_SLUGS.DEVELOPER,
+				dependsOn: SETTINGS_KEYS.JS_SANDBOX_ENABLED
+			},
 			{
 				key: SETTINGS_KEYS.CUSTOM_JSON,
 				label: 'Custom JSON',
@@ -3,6 +3,7 @@
 * These values are sent to the server and mapped to token budgets.
 */
 export enum ReasoningEffort {
+	DEFAULT = 'default',
 	OFF = 'off',
 	LOW = 'low',
 	MEDIUM = 'medium',
@@ -17,6 +17,7 @@ import { isRouterMode } from '$lib/stores/server.svelte';
 export interface UseReasoningMenuReturn {
 	readonly modelSupportsThinking: boolean;
 	readonly thinkingEnabled: boolean;
+	readonly isOff: boolean;
 	readonly currentEffort: ReasoningEffort;
 	readonly levels: ReasoningEffortLevel[];
 	isSelected(level: ReasoningEffortLevel): boolean;
@@ -59,8 +60,10 @@ export function useReasoningMenu(): UseReasoningMenuReturn {
 		return supportsThinking() || modelSupportsThinkingFromMessages;
 	});

-	const thinkingEnabled = $derived(conversationsStore.getThinkingEnabled());
 	const currentEffort = $derived(conversationsStore.getReasoningEffort());
+	const thinkingEnabled = $derived(
+		currentEffort !== ReasoningEffort.OFF && currentEffort !== ReasoningEffort.DEFAULT
+	);

 	return {
 		get modelSupportsThinking() {
@@ -69,6 +72,9 @@ export function useReasoningMenu(): UseReasoningMenuReturn {
 		get thinkingEnabled() {
 			return thinkingEnabled;
 		},
+		get isOff() {
+			return currentEffort === ReasoningEffort.OFF;
+		},
 		get currentEffort() {
 			return currentEffort;
 		},
@@ -76,20 +82,15 @@ export function useReasoningMenu(): UseReasoningMenuReturn {
 			return REASONING_EFFORT_LEVELS;
 		},
 		isSelected(level: ReasoningEffortLevel): boolean {
-			if (level.isOff) return !thinkingEnabled;
-			return thinkingEnabled && currentEffort === level.value;
+			return currentEffort === level.value;
 		},
 		tokenLabel(level: ReasoningEffortLevel): string | null {
-			if (level.isOff) return null;
+			if (level.value === ReasoningEffort.DEFAULT) return 'Model default';
 			const tokens = REASONING_EFFORT_TOKENS[level.value];
+			if (tokens === undefined) return null;
 			return tokens === -1 ? 'Unlimited' : `Max ${tokens.toLocaleString()} tokens`;
 		},
 		select(level: ReasoningEffortLevel): void {
-			if (level.isOff) {
-				conversationsStore.setThinkingEnabled(false);
-				return;
-			}
-			conversationsStore.setThinkingEnabled(true);
 			conversationsStore.setReasoningEffort(level.value as ReasoningEffort);
 		}
 	};
@@ -271,10 +271,14 @@ export class ChatService {
 		const reasoningBudgetTokens =
 			enableThinking && reasoningEffort ? (REASONING_EFFORT_TOKENS[reasoningEffort] ?? -1) : -1;

-		requestBody.chat_template_kwargs = {
-			...(requestBody.chat_template_kwargs ?? {}),
-			enable_thinking: enableThinking
-		};
+		// an explicit user choice injects the kwarg, otherwise it is omitted so
+		// the server default applies (--reasoning flag or chat template)
+		if (enableThinking !== undefined) {
+			requestBody.chat_template_kwargs = {
+				...(requestBody.chat_template_kwargs ?? {}),
+				enable_thinking: enableThinking
+			};
+		}

 		if (reasoningBudgetTokens >= 0) {
 			requestBody.thinking_budget_tokens = reasoningBudgetTokens;
@@ -276,7 +276,7 @@ export { MCPService } from './mcp.service';
 * - **toolsStore**: Exposes the tool definition when the sandbox is enabled
 * - **agenticStore**: Dispatches ToolSource.FRONTEND calls here
 *
- * @see SANDBOX_TOOL_DEFINITION in constants/sandbox.ts - tool schema sent to the LLM
+ * @see buildSandboxToolDefinition in constants/sandbox.ts - tool schema sent to the LLM
 * @see agenticStore in stores/agentic.svelte.ts - tool dispatch
 */
 export { SandboxService } from './sandbox.service';
@@ -1,14 +1,25 @@
+import { NEWLINE } from '$lib/constants';
 import WORKER_SHIM from './sandbox-worker.js?raw';

+/**
+ * CSP for the harness document, inherited by the blob worker. connect-src
+ * falls back to default-src, removing network egress for model and vendored
+ * code. 'unsafe-eval' is required by the worker's AsyncFunction constructor,
+ * 'unsafe-inline' by the inline script below, worker-src by the blob worker.
+ */
+const HARNESS_CSP = `default-src 'none'; script-src 'unsafe-inline' 'unsafe-eval'; worker-src blob:`;
+
 /**
 * Harness loaded as srcdoc into a sandboxed iframe (allow-scripts only).
 * The opaque origin is the security boundary: no access to the app origin,
 * its storage or its API. The harness spawns a worker so model code never
 * runs on a main thread, which makes the parent timeout enforceable by
- * removing the iframe.
+ * removing the iframe. The prelude runs in the worker before the shim,
+ * exposing globals such as `nerdamer` to model code.
 */
-export const SANDBOX_HARNESS_HTML = `<!doctype html><script>
-const SHIM = ${JSON.stringify(WORKER_SHIM)};
+export function buildSandboxHarness(preludeJs: string): string {
+	return `<!doctype html><meta http-equiv="Content-Security-Policy" content="${HARNESS_CSP}"><script>
+const SHIM = ${JSON.stringify(preludeJs + NEWLINE + WORKER_SHIM)};
 addEventListener('message', (event) => {
 	const respond = (payload) => parent.postMessage(payload, '*');
 	let worker;
@@ -23,3 +34,4 @@ addEventListener('message', (event) => {
 	worker.postMessage({ code: event.data.code });
 });
 </script>`;
+}
@@ -21,7 +21,9 @@ self.onmessage = async (event) => {
 	const reply = { logs, result: null, error: null };
 	try {
 		const AsyncFunction = Object.getPrototypeOf(async function () {}).constructor;
-		const value = await new AsyncFunction(event.data.code)();
+		// The prelude bundled ahead of this shim defines self.nerdamer,
+		// passed into the execution scope as the `nerdamer` parameter.
+		const value = await new AsyncFunction('nerdamer', event.data.code)(self.nerdamer);
 		if (value !== undefined) reply.result = fmt(value);
 	} catch (err) {
 		reply.error = err instanceof Error ? err.stack || err.message : String(err);
@@ -7,9 +7,32 @@ import {
 	SANDBOX_TOOL_NAME,
 	SANDBOX_TRUNCATION_NOTICE
 } from '$lib/constants';
-import { SANDBOX_HARNESS_HTML } from './sandbox-harness';
+import { buildSandboxHarness } from './sandbox-harness';
+import { config } from '$lib/stores/settings.svelte';
 import type { ToolExecutionResult } from '$lib/types';

+/** Cached harnesses keyed by whether nerdamer is included. */
+const harnessCache: Record<string, string> = {};
+
+/**
+ * Build the sandbox harness. When symbolic math is enabled, loads the
+ * nerdamer prelude lazily; otherwise builds a plain harness with an empty
+ * prelude. Cached per variant so toggling the setting is instant.
+ */
+async function getHarness(): Promise<string> {
+	const enabled = !!config().symbolicMathEnabled;
+	const key = enabled ? 'nerdamer' : 'plain';
+	if (!harnessCache[key]) {
+		if (enabled) {
+			const { default: nerdamerJs } = await import('virtual:nerdamer');
+			harnessCache[key] = buildSandboxHarness(nerdamerJs);
+		} else {
+			harnessCache[key] = buildSandboxHarness('');
+		}
+	}
+	return harnessCache[key];
+}
+
 interface SandboxReply {
 	logs?: unknown;
 	result?: unknown;
@@ -45,20 +68,22 @@ export class SandboxService {
 	 * timeout or abort. Removing the iframe terminates the worker
 	 * at the browser level, so runaway code cannot outlive it.
 	 */
-	static executeTool(
+	static async executeTool(
 		toolName: string,
 		params: Record<string, unknown>,
 		signal?: AbortSignal
 	): Promise<ToolExecutionResult> {
 		if (toolName !== SANDBOX_TOOL_NAME) {
-			return Promise.resolve({ content: `Unknown frontend tool: ${toolName}`, isError: true });
+			return { content: `Unknown frontend tool: ${toolName}`, isError: true };
 		}

 		const code = typeof params.code === 'string' ? params.code : '';
 		if (!code) {
-			return Promise.resolve({ content: 'Missing required parameter: code', isError: true });
+			return { content: 'Missing required parameter: code', isError: true };
 		}

+		const harness = await getHarness();
+
 		const requested = Number(params.timeout_ms);
 		const timeoutMs =
 			Number.isFinite(requested) && requested > 0
@@ -69,7 +94,7 @@ export class SandboxService {
 			const iframe = document.createElement('iframe');
 			iframe.setAttribute('sandbox', 'allow-scripts');
 			iframe.style.display = 'none';
-			iframe.srcdoc = SANDBOX_HARNESS_HTML;
+			iframe.srcdoc = harness;

 			let settled = false;

@@ -2373,9 +2373,12 @@ class ChatStore {

 		if (currentConfig.excludeReasoningFromContext) apiOptions.excludeReasoningFromContext = true;

-		apiOptions.enableThinking = conversationsStore.getThinkingEnabled();
+		// an explicit reasoning choice overrides the server default, DEFAULT sends nothing
 		const effort = conversationsStore.getReasoningEffort();
-		if (effort !== ReasoningEffort.OFF) apiOptions.reasoningEffort = effort;
+		if (effort !== ReasoningEffort.DEFAULT) {
+			apiOptions.enableThinking = effort !== ReasoningEffort.OFF;
+			if (effort !== ReasoningEffort.OFF) apiOptions.reasoningEffort = effort;
+		}

 		if (hasValue(currentConfig.temperature))
 			apiOptions.temperature = Number(currentConfig.temperature);
@@ -80,25 +80,17 @@ class ConversationsStore {
 	/** Whether the store has been initialized */
 	isInitialized = $state(false);

-	/** Global (non-conversation-specific) thinking toggle default, derived from reasoning effort */
-	pendingThinkingEnabled = $state(false);
-
 	/** Global (non-conversation-specific) reasoning effort default */
-	pendingReasoningEffort = $state<ReasoningEffort | ReasoningEffort.OFF>(
-		ConversationsStore.loadReasoningEffortDefault()
-	);
+	pendingReasoningEffort = $state<ReasoningEffort>(ConversationsStore.loadReasoningEffortDefault());

-	/** Last non-off reasoning effort, restored when re-enabling thinking globally */
-	private lastNonOffEffort: ReasoningEffort | null = null;
-
-	/** Load reasoning effort default from localStorage */
-	private static loadReasoningEffortDefault(): ReasoningEffort | ReasoningEffort.OFF {
-		if (typeof globalThis.localStorage === 'undefined') return ReasoningEffort.OFF;
+	/** Load reasoning effort default from localStorage, DEFAULT defers to the server */
+	private static loadReasoningEffortDefault(): ReasoningEffort {
+		if (typeof globalThis.localStorage === 'undefined') return ReasoningEffort.DEFAULT;
 		try {
 			const raw = localStorage.getItem(REASONING_EFFORT_DEFAULT_LOCALSTORAGE_KEY);
-			return (raw as ReasoningEffort | ReasoningEffort.OFF) || ReasoningEffort.OFF;
+			return (raw as ReasoningEffort) || ReasoningEffort.DEFAULT;
 		} catch {
-			return ReasoningEffort.OFF;
+			return ReasoningEffort.DEFAULT;
 		}
 	}

@@ -235,17 +227,10 @@ class ConversationsStore {
 		// servers without a per-conversation override to `mcpServers[i].enabled`,
 		// and only explicit toggles are stored on the conversation.

-		// Inherit global thinking/reasoning defaults into the new conversation
-		const thinkingEnabled = this.getThinkingEnabled();
-		conversation.thinkingEnabled = thinkingEnabled;
-		conversation.reasoningEffort =
-			this.pendingReasoningEffort === ReasoningEffort.OFF ? undefined : this.pendingReasoningEffort;
+		// Inherit the global reasoning default into the new conversation
+		conversation.reasoningEffort = this.pendingReasoningEffort;
 		await DatabaseService.updateConversation(conversation.id, {
-			thinkingEnabled,
-			reasoningEffort:
-				this.pendingReasoningEffort === ReasoningEffort.OFF
-					? undefined
-					: this.pendingReasoningEffort
+			reasoningEffort: this.pendingReasoningEffort
 		});

 		this.conversations = [conversation, ...this.conversations];
@@ -793,63 +778,21 @@ class ConversationsStore {
 		await this.setMcpServerOverride(serverId, undefined);
 	}

-	/**
-	 * Gets the effective thinking-enabled state for the active conversation.
-	 * Returns the conversation override if set, otherwise the global default.
-	 */
-	getThinkingEnabled(): boolean {
-		if (this.activeConversation) {
-			if (this.activeConversation.thinkingEnabled !== undefined) {
-				return this.activeConversation.thinkingEnabled;
-			}
-		}
-		return this.getReasoningEffort() !== ReasoningEffort.OFF;
-	}
-
-	/**
-	 * Sets the thinking-enabled state for the active conversation.
-	 * If no conversation exists, stores the global default.
-	 * @param enabled - The enabled state
-	 */
-	async setThinkingEnabled(enabled: boolean): Promise<void> {
-		if (!this.activeConversation) {
-			if (enabled) {
-				const effort = this.lastNonOffEffort ?? ReasoningEffort.LOW;
-				this.pendingReasoningEffort = effort;
-				this.saveReasoningEffortDefaults();
-			} else {
-				if (this.pendingReasoningEffort !== ReasoningEffort.OFF) {
-					this.lastNonOffEffort = this.pendingReasoningEffort;
-				}
-				this.pendingReasoningEffort = ReasoningEffort.OFF;
-				this.saveReasoningEffortDefaults();
-			}
-			return;
-		}
-
-		this.activeConversation = {
-			...this.activeConversation,
-			thinkingEnabled: enabled
-		};
-
-		await DatabaseService.updateConversation(this.activeConversation.id, {
-			thinkingEnabled: enabled
-		});
-
-		const convIndex = this.conversations.findIndex((c) => c.id === this.activeConversation!.id);
-		if (convIndex !== -1) {
-			this.conversations[convIndex].thinkingEnabled = enabled;
-			this.conversations = [...this.conversations];
-		}
-	}
-
 	/**
 	 * Gets the effective reasoning effort for the active conversation.
 	 * Returns the conversation override if set, otherwise the global default.
+	 * DEFAULT means no override is sent and the server decides.
 	 */
-	getReasoningEffort(): ReasoningEffort | ReasoningEffort.OFF {
+	getReasoningEffort(): ReasoningEffort {
 		if (this.activeConversation) {
-			return this.activeConversation.reasoningEffort ?? this.pendingReasoningEffort;
+			if (this.activeConversation.reasoningEffort !== undefined) {
+				return this.activeConversation.reasoningEffort;
+			}
+			// conversations created before the tri-state store an explicit
+			// opt-out only as thinkingEnabled = false
+			if (this.activeConversation.thinkingEnabled === false) {
+				return ReasoningEffort.OFF;
+			}
 		}
 		return this.pendingReasoningEffort;
 	}
@@ -857,7 +800,7 @@ class ConversationsStore {
 	/**
 	 * Sets the reasoning effort for the active conversation.
 	 * If no conversation exists, stores the global default.
-	 * @param effort - The effort level ('low' | 'medium' | 'high' | 'max')
+	 * @param effort - The effort level ('default' | 'off' | 'low' | 'medium' | 'high' | 'max')
 	 */
 	async setReasoningEffort(effort: ReasoningEffort): Promise<void> {
 		if (!this.activeConversation) {
@@ -5,7 +5,7 @@ import { HealthCheckStatus, JsonSchemaType, ToolCallType, ToolSource } from '$li
 import { config } from '$lib/stores/settings.svelte';
 import {
 	DISABLED_TOOL_KEYS_LOCALSTORAGE_KEY,
-	SANDBOX_TOOL_DEFINITION,
+	buildSandboxToolDefinition,
 	TOOL_GROUP_LABELS,
 	TOOL_SERVER_LABELS
 } from '$lib/constants';
@@ -143,7 +143,9 @@ class ToolsStore {
 	}

 	get frontendTools(): OpenAIToolDefinition[] {
-		return config().jsSandboxEnabled ? [SANDBOX_TOOL_DEFINITION] : [];
+		return config().jsSandboxEnabled
+			? [buildSandboxToolDefinition(!!config().symbolicMathEnabled)]
+			: [];
 	}

 	get customTools(): OpenAIToolDefinition[] {
@@ -1,6 +1,5 @@
 export interface ReasoningEffortLevel {
 	value: string;
 	label: string;
-	isOff?: boolean;
 	hasInfo?: boolean;
 }
@@ -0,0 +1,24 @@
+This is free and unencumbered software released into the public domain.
+
+Anyone is free to copy, modify, publish, use, compile, sell, or
+distribute this software, either in source code form or as a compiled
+binary, for any purpose, commercial or non-commercial, and by any
+means.
+
+In jurisdictions that recognize copyright laws, the author or authors
+of this software dedicate any and all copyright interest in the
+software to the public domain. We make this dedication for the benefit
+of the public at large and to the detriment of our heirs and
+successors. We intend this dedication to be an overt act of
+relinquishment in perpetuity of all present and future rights to this
+software under copyright law.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+OTHER DEALINGS IN THE SOFTWARE.
+
+For more information, please refer to <http://unlicense.org>
@@ -0,0 +1,23 @@
+The MIT Licence.
+
+Copyright (c) 2025 Michael Mclaughlin
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+'Software'), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
@@ -0,0 +1,926 @@
+/*
+ * Author : Martin Donk
+ * Website : http://www.nerdamer.com
+ * Email : martin.r.donk@gmail.com
+ * License : MIT
+ * Source : https://github.com/jiggzson/nerdamer
+ */
+
+// Type imports for JSDoc ======================================================
+// These typedefs provide type aliases for the interfaces defined in index.d.ts.
+// They enable proper type checking when working with the classes defined in this file.
+//
+// Usage patterns:
+// - For return types: @returns {NerdamerSymbolType}
+// - For parameters: @param {NerdamerSymbolType} symbol
+// - For variable declarations: /** @type {NerdamerSymbolType} */
+//
+// Note: When casting local class instances to interface types, use the pattern:
+//   /** @type {InterfaceType} */ (/** @type {unknown} */ (localInstance))
+// This is needed because TypeScript sees local classes and interfaces as separate types.
+
+/**
+ * Core type aliases from index.d.ts
+ *
+ * @typedef {import('./index').NerdamerCore.NerdamerSymbol} NerdamerSymbolType
+ *
+ * @typedef {import('./index').NerdamerCore.Frac} FracType
+ *
+ * @typedef {import('./index').NerdamerCore.Vector} VectorType
+ *
+ * @typedef {import('./index').NerdamerCore.Matrix} MatrixType
+ *
+ * @typedef {import('./index').NerdamerCore.Parser} ParserType
+ *
+ * @typedef {import('./index').NerdamerCore.Settings} SettingsType
+ *
+ * @typedef {import('./index').NerdamerExpression} ExpressionType
+ *
+ * @typedef {typeof import('./index')} NerdamerType
+ *
+ * @typedef {import('./index').NerdamerCore.Utils} UtilsInterface
+ *
+ * @typedef {import('./index').NerdamerCore.Math2} Math2Interface
+ *
+ * @typedef {import('./index').NerdamerCore.Core} CoreType
+ *
+ * @typedef {import('./index').ExpressionParam} ExpressionParam
+ *
+ * @typedef {import('./index').ArithmeticOperand} ArithmeticOperand
+ *
+ * @typedef {import('./index').NerdamerCore.AlgebraModule} AlgebraModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.PartFracSubModule} PartFracSubModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.CalculusModule} CalculusModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.ExtraModule} ExtraModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.LaPlaceSubModule} LaPlaceSubModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.StatisticsSubModule} StatisticsSubModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.UnitsSubModule} UnitsSubModuleType
+ *
+ * @typedef {import('./index').NerdamerCore.DecomposeResultObject} DecomposeResultType
+ */
+
+// Check if nerdamer exists globally (browser) or needs to be required (Node.js)
+let nerdamer = typeof globalThis !== 'undefined' && globalThis.nerdamer ? globalThis.nerdamer : undefined;
+if (typeof module !== 'undefined' && nerdamer === undefined) {
+    nerdamer = require('./nerdamer.core.js');
+    require('./Calculus');
+    require('./Algebra');
+}
+
+/** @returns {ExtraModuleType} */
+(function initExtraModule() {
+    /** @type {CoreType} */
+    const core = nerdamer.getCore();
+    /** @type {ParserType} */
+    const _ = core.PARSER;
+    const {
+        NerdamerSymbol,
+        Vector: _Vector,
+        /** @type {AlgebraModuleType} */
+        Algebra,
+        /** @type {CalculusModuleType} */
+        Calculus,
+    } = core;
+    const { format, isVector, isArray, isSymbol } = core.Utils;
+    const { S, EX: _EX, CP, PL, CB, FN } = core.groups;
+    core.Settings.Laplace_integration_depth = 40;
+
+    /**
+     * Check if a symbol's power is itself a symbol with group S or CB
+     *
+     * @param {NerdamerSymbolType} sym
+     * @returns {boolean}
+     */
+    function hasPowerGroupSOrCB(sym) {
+        return isSymbol(sym.power) && (sym.power.group === S || sym.power.group === CB);
+    }
+
+    /**
+     * Finds a function by name within this symbol's tree.
+     *
+     * @this {NerdamerSymbolType}
+     * @param {string} fname The function name to search for
+     * @returns {NerdamerSymbolType | undefined} The found function symbol clone, or undefined if not found
+     */
+    NerdamerSymbol.prototype.findFunction = function findFunction(fname) {
+        // This is what we're looking for
+        if (this.group === FN && this.fname === fname) {
+            return this.clone();
+        }
+        let found;
+        if (this.symbols) {
+            for (const x in this.symbols) {
+                if (!Object.hasOwn(this.symbols, x)) {
+                    continue;
+                }
+                found = this.symbols[x].findFunction(fname);
+                if (found) {
+                    break;
+                }
+            }
+        }
+
+        return found;
+    };
+
+    /** @type {ExtraModuleType} */
+    const __ = (core.Extra = {
+        version: '1.4.2',
+        // http://integral-table.com/downloads/LaplaceTable.pdf
+        // Laplace assumes all coefficients to be positive
+        LaPlace: {
+            // Using: integral_0^oo f(t)*e^(-s*t) dt
+            /**
+             * @param {NerdamerSymbolType} symbol
+             * @param {NerdamerSymbolType | string} t
+             * @param {NerdamerSymbolType | string} s
+             * @returns {NerdamerSymbolType}
+             */
+            transform(symbol, t, s) {
+                /** @type {NerdamerSymbolType} */
+                symbol = symbol.clone();
+
+                t = t.toString();
+                // First try a lookup for a speed boost
+                symbol = NerdamerSymbol.unwrapSQRT(symbol, true);
+                /** @type {NerdamerSymbolType} */
+                let retval;
+                const coeff = symbol.stripVar(t);
+                const g = symbol.group;
+
+                symbol = /** @type {NerdamerSymbolType} */ (_.divide(symbol, coeff.clone()));
+
+                if (symbol.isConstant() || !symbol.contains(t, true)) {
+                    retval = _.parse(format('({0})/({1})', symbol, s));
+                } else if (g === S && core.Utils.isInt(symbol.power)) {
+                    const n = String(symbol.power);
+                    retval = _.parse(format('factorial({0})/({1})^({0}+1)', n, s));
+                } else if (symbol.group === S && symbol.power.equals(1 / 2)) {
+                    retval = _.parse(format('sqrt(pi)/(2*({0})^(3/2))', s));
+                } else if (symbol.isComposite()) {
+                    retval = new NerdamerSymbol(0);
+                    symbol.each(x => {
+                        retval = /** @type {NerdamerSymbolType} */ (_.add(retval, __.LaPlace.transform(x, t, s)));
+                    }, true);
+                } else if (symbol.isE() && hasPowerGroupSOrCB(symbol)) {
+                    const a = /** @type {NerdamerSymbolType} */ (symbol.power).stripVar(t);
+                    retval = _.parse(format('1/(({1})-({0}))', a, s));
+                } else {
+                    const fns = ['sin', 'cos', 'sinh', 'cosh'];
+                    // Support for symbols in fns with arguments in the form a*t or n*t where a = symbolic and n = Number
+                    if (
+                        symbol.group === FN &&
+                        fns.indexOf(symbol.fname) !== -1 &&
+                        (symbol.args[0].group === S || symbol.args[0].group === CB)
+                    ) {
+                        const a = symbol.args[0].stripVar(t);
+
+                        switch (symbol.fname) {
+                            case 'sin':
+                                retval = _.parse(format('({0})/(({1})^2+({0})^2)', a, s));
+                                break;
+                            case 'cos':
+                                retval = _.parse(format('({1})/(({1})^2+({0})^2)', a, s));
+                                break;
+                            case 'sinh':
+                                retval = _.parse(format('({0})/(({1})^2-({0})^2)', a, s));
+                                break;
+                            case 'cosh':
+                                retval = _.parse(format('({1})/(({1})^2-({0})^2)', a, s));
+                                break;
+                        }
+                    } else {
+                        // Try to integrate for a solution
+                        // we need at least the Laplace integration depth
+                        const depthIsLower = core.Settings.integration_depth < core.Settings.Laplace_integration_depth;
+
+                        let savedIntegrationDepth;
+                        if (depthIsLower) {
+                            savedIntegrationDepth = core.Settings.integration_depth; // Save the depth
+                            core.Settings.integration_depth = core.Settings.Laplace_integration_depth; // Transforms need a little more room
+                        }
+
+                        core.Utils.block(
+                            'PARSE2NUMBER',
+                            () => {
+                                const u = t;
+                                const sym = symbol.sub(t, u);
+                                const integrationExpr = _.parse(`e^(-${s}*${u})*${sym}`);
+                                retval = Calculus.integrate(integrationExpr, u);
+                                if (retval.hasIntegral?.()) {
+                                    retval = _.symfunction('laplace', [symbol, _.parse(String(t)), _.parse(String(s))]);
+                                    return;
+                                }
+                                //                                _.error('Unable to compute transform');
+                                retval = retval.sub(t, 0);
+                                retval = /** @type {NerdamerSymbolType} */ (
+                                    _.expand(_.multiply(retval, new NerdamerSymbol(-1)))
+                                );
+                                retval = retval.sub(u, t);
+                            },
+                            false
+                        );
+
+                        retval = /** @type {NerdamerSymbolType} */ (
+                            core.Utils.block('PARSE2NUMBER', () => _.parse(retval), true)
+                        );
+
+                        if (depthIsLower) // Put the integration depth as it was
+                        {
+                            core.Settings.integration_depth = savedIntegrationDepth;
+                        }
+                    }
+                }
+
+                return /** @type {NerdamerSymbolType} */ (_.multiply(retval, coeff));
+            },
+            /**
+             * @param {NerdamerSymbolType} symbol
+             * @param {NerdamerSymbolType | string} s_
+             * @param {NerdamerSymbolType | string} t
+             * @returns {NerdamerSymbolType}
+             */
+            inverse(symbol, s_, t) {
+                const inputSymbol = symbol.clone();
+                return core.Utils.block(
+                    'POSITIVE_MULTIPLIERS',
+                    () => {
+                        /** @type {NerdamerSymbolType | undefined} */
+                        let retval;
+                        // Expand and get partial fractions
+                        if (symbol.group === CB) {
+                            symbol = /** @type {NerdamerSymbolType} */ (
+                                /** @type {PartFracSubModuleType} */ (Algebra.PartFrac).partfrac(
+                                    /** @type {NerdamerSymbolType} */ (_.expand(symbol)),
+                                    s_
+                                )
+                            );
+                        }
+
+                        if (symbol.group === S || symbol.group === CB || symbol.isComposite()) {
+                            /** @type {number | FracType} */
+                            let p;
+                            /** @type {FracType} */
+                            let denP;
+                            /** @type {NerdamerSymbolType} */
+                            let a;
+                            /** @type {NerdamerSymbolType | string} */
+                            let b;
+                            /** @type {NerdamerSymbolType} */
+                            let d;
+                            /** @type {string} */
+                            let exp;
+                            /** @type {DecomposeResultType} */
+                            let f2;
+                            /** @type {string | number} */
+                            let fact;
+                            // Remove the multiplier
+                            const m = symbol.multiplier.clone();
+                            symbol.toUnitMultiplier();
+                            // Get the numerator and denominator
+                            let num = symbol.getNum();
+                            const den = symbol.getDenom().toUnitMultiplier();
+
+                            // TODO: Make it so factor doesn't destroy pi
+                            // num = core.Algebra.Factor.factor(symbol.getNum());
+                            // den = core.Algebra.Factor.factor(symbol.getDenom().invert(null, true));
+
+                            if (den.group === CP || den.group === PL) {
+                                denP = /** @type {FracType} */ (den.power.clone());
+                                den.toLinear();
+                            } else {
+                                denP = new core.Frac(1);
+                            }
+
+                            // Convert s to a string
+                            const s = s_.toString();
+                            // Split up the denominator if in the form ax+b
+                            /** @type {DecomposeResultType} */
+                            const f = core.Utils.decompose_fn(den, s, true);
+                            // Move the multiplier to the numerator
+                            /** @type {DecomposeResultType} */
+                            const _fe = core.Utils.decompose_fn(
+                                /** @type {NerdamerSymbolType} */ (_.expand(num.clone())),
+                                s,
+                                true
+                            );
+                            num.multiplier = num.multiplier.multiply(m);
+
+                            const finalize = function () {
+                                // Put back the numerator
+                                retval = /** @type {NerdamerSymbolType} */ (_.multiply(retval, num));
+                                retval.multiplier = retval.multiplier.multiply(symbol.multiplier);
+                                // Put back a
+                                retval = /** @type {NerdamerSymbolType} */ (_.divide(retval, f.a));
+                            };
+
+                            // Store the parts in variables for easy recognition
+                            // check if in the form t^n where n = integer
+                            if (
+                                (den.group === S || den.group === CB) &&
+                                f.x.value === s &&
+                                f.b.equals(0) &&
+                                core.Utils.isInt(f.x.power)
+                            ) {
+                                p = /** @type {number} */ (/** @type {unknown} */ (f.x.power)) - 1;
+                                fact = core.Math2.factorial(p);
+                                //  N!/s^(n-1)
+                                retval = /** @type {NerdamerSymbolType} */ (
+                                    _.divide(_.pow(_.parse(String(t)), new NerdamerSymbol(p)), new NerdamerSymbol(fact))
+                                );
+                                // Wrap it up
+                                finalize();
+                            } else if (den.group === CP && denP.equals(1)) {
+                                if (f.x.group === core.groups.PL && Algebra.degree(den).equals(2)) {
+                                    // Possibly in the form 1/(s^2+2*s+1)
+                                    // Try factoring to get it in a more familiar form{
+                                    // Apply inverse of F(s-a)
+                                    /**
+                                     * @type {{
+                                     *     f: NerdamerSymbolType;
+                                     *     a: NerdamerSymbolType;
+                                     *     h: NerdamerSymbolType;
+                                     *     c?: NerdamerSymbolType;
+                                     * }}
+                                     */
+                                    const completed = Algebra.sqComplete(den, s);
+                                    const u = core.Utils.getU(den);
+                                    // Get a for the function above
+                                    a = core.Utils.decompose_fn(completed.a, s, true).b;
+                                    const tf = __.LaPlace.inverse(
+                                        _.parse(`1/((${u})^2+(${completed.c}))`),
+                                        u,
+                                        String(t)
+                                    );
+                                    retval = /** @type {NerdamerSymbolType} */ (
+                                        _.multiply(tf, _.parse(`(${m})*e^(-(${a})*(${t}))`))
+                                    );
+                                    // A/(b*s-c) -> ae^(-bt)
+                                } else if (f.x.isLinear() && !num.contains(s)) {
+                                    t = /** @type {NerdamerSymbolType | string} */ (
+                                        _.divide(_.parse(String(t)), f.a.clone())
+                                    );
+
+                                    // Don't add factorial of one or zero
+                                    p = /** @type {number} */ (/** @type {unknown} */ (denP)) - 1;
+                                    fact = p === 0 || p === 1 ? '1' : `(${denP}-1)!`;
+                                    retval = _.parse(
+                                        format(
+                                            '(({0})^({3}-1)*e^(-(({2})*({0}))/({1})))/(({4})*({1})^({3}))',
+                                            t,
+                                            f.a,
+                                            f.b,
+                                            denP,
+                                            fact
+                                        )
+                                    );
+                                    // Wrap it up
+                                    finalize();
+                                } else if (f.x.group === S && f.x.power.equals(2)) {
+                                    if (num.contains(s)) {
+                                        // A*s/(b*s^2+c^2)
+                                        a = new NerdamerSymbol(1);
+                                        if (num.group === CB) {
+                                            /** @type {NerdamerSymbolType} */
+                                            let newNum = new NerdamerSymbol(1);
+                                            num.each(x => {
+                                                if (x.contains(s)) {
+                                                    newNum = /** @type {NerdamerSymbolType} */ (_.multiply(newNum, x));
+                                                } else {
+                                                    a = /** @type {NerdamerSymbolType} */ (_.multiply(a, x));
+                                                }
+                                            });
+                                            num = newNum;
+                                        }
+
+                                        // We need more information about the denominator to decide
+                                        f2 = core.Utils.decompose_fn(num, s, true);
+                                        const fn1 = f2.a;
+                                        const fn2 = f2.b;
+                                        const aHasSin = fn1.containsFunction('sin');
+                                        const aHasCos = fn1.containsFunction('cos');
+                                        const bHasCos = fn2.containsFunction('cos');
+                                        const bHasSin = fn2.containsFunction('sin');
+                                        if (
+                                            f2.x.value === s &&
+                                            f2.x.isLinear() &&
+                                            !((aHasSin && bHasCos) || aHasCos || bHasSin)
+                                        ) {
+                                            retval = _.parse(
+                                                format(
+                                                    '(({1})*cos((sqrt(({2})*({3}))*({0}))/({2})))/({2})',
+                                                    t,
+                                                    f2.a,
+                                                    f.a,
+                                                    f.b
+                                                )
+                                            );
+                                        } else if (aHasSin && bHasCos) {
+                                            const sin = /** @type {NerdamerSymbolType} */ (fn1.findFunction?.('sin'));
+                                            const cos = /** @type {NerdamerSymbolType} */ (fn2.findFunction?.('cos'));
+                                            // Who has the s?
+                                            if (sin?.args?.[0].equals(cos?.args?.[0]) && !sin?.args?.[0].contains(s)) {
+                                                b = /** @type {NerdamerSymbolType} */ (
+                                                    _.divide(fn2, cos.toUnitMultiplier())
+                                                ).toString();
+                                                const c = sin.args[0].toString();
+                                                d = f.b;
+                                                const e = _.divide(fn1, sin.toUnitMultiplier());
+                                                exp =
+                                                    '(({1})*({2})*cos({3})*sin(sqrt({4})*({0})))/sqrt({4})+({1})*sin({3})*({5})*cos(sqrt({4})*({0}))';
+                                                retval = _.parse(format(exp, t, a, b, c, d, e));
+                                            }
+                                        }
+                                    } else {
+                                        retval = _.parse(
+                                            format(
+                                                '(({1})*sin((sqrt(({2})*({3}))*({0}))/({2})))/sqrt(({2})*({3}))',
+                                                t,
+                                                num,
+                                                f.a,
+                                                f.b
+                                            )
+                                        );
+                                    }
+                                }
+                            } else if (
+                                /** @type {FracType} */ (f.x.power).num &&
+                                /** @type {FracType} */ (f.x.power).num.equals(3) &&
+                                /** @type {FracType} */ (f.x.power).den.equals(2) &&
+                                num.contains('sqrt(pi)') &&
+                                !num.contains(s) &&
+                                num.isLinear()
+                            ) {
+                                b = /** @type {NerdamerSymbolType} */ (_.divide(num.clone(), _.parse('sqrt(pi)')));
+                                retval = _.parse(format('(2*({2})*sqrt({0}))/({1})', t, f.a, b, num));
+                            } else if (denP.equals(2) && f.x.power.equals(2)) {
+                                if (num.contains(s)) {
+                                    // Decompose the numerator to check value of s
+                                    f2 = core.Utils.decompose_fn(
+                                        /** @type {NerdamerSymbolType} */ (_.expand(num.clone())),
+                                        s,
+                                        true
+                                    );
+                                    if (f2.x.isComposite()) {
+                                        /** @type {DecomposeResultType[]} */
+                                        const sTerms = [];
+                                        // First collect the factors e.g. (a)(bx)(cx^2+d)
+                                        /** @type {DecomposeResultType[]} */
+                                        const symbols = /** @type {DecomposeResultType[]} */ (
+                                            num
+                                                .collectSymbols(x => {
+                                                    x = NerdamerSymbol.unwrapPARENS(x);
+                                                    /** @type {DecomposeResultType} */
+                                                    const decomp = core.Utils.decompose_fn(x, s, true);
+                                                    decomp.symbol = x;
+                                                    return decomp;
+                                                })
+                                                // Then sort them by power hightest to lowest
+                                                .sort((x1, x2) => {
+                                                    const p1 =
+                                                        /** @type {DecomposeResultType} */ (x1).x.value === s
+                                                            ? /** @type {number} */ (
+                                                                  /** @type {unknown} */ (
+                                                                      /** @type {DecomposeResultType} */ (x1).x.power
+                                                                  )
+                                                              )
+                                                            : 0;
+                                                    const p2 =
+                                                        /** @type {DecomposeResultType} */ (x2).x.value === s
+                                                            ? /** @type {number} */ (
+                                                                  /** @type {unknown} */ (
+                                                                      /** @type {DecomposeResultType} */ (x2).x.power
+                                                                  )
+                                                              )
+                                                            : 0;
+                                                    return p2 - p1;
+                                                })
+                                        );
+                                        a = new NerdamerSymbol(-1);
+                                        // Grab only the ones which have s
+                                        for (let i = 0; i < symbols.length; i++) {
+                                            const fc = symbols[i];
+                                            if (fc.x.value === s) {
+                                                sTerms.push(fc);
+                                            } else {
+                                                a = /** @type {NerdamerSymbolType} */ (_.multiply(a, fc.symbol));
+                                            }
+                                        }
+                                        // The following 2 assumptions are made
+                                        // 1. since the numerator was factored above then each s_term has a unique power
+                                        // 2. because the terms are sorted by descending powers then the first item
+                                        //    has the highest power
+                                        // We can now check for the next type s(s^2-a^2)/(s^2+a^2)^2
+                                        if (
+                                            sTerms[0].x.power.equals(2) &&
+                                            sTerms[1].x.power.equals(1) &&
+                                            sTerms[1].b.equals(0) &&
+                                            !sTerms[0].b.equals(0)
+                                        ) {
+                                            b = sTerms[0].a.negate();
+                                            exp =
+                                                '-(({1})*({2})*({5})*({0})*sin((sqrt(({4})*({5}))*({0}))/({4})))/' +
+                                                '(2*({4})^2*sqrt(({4})*({5})))-(({1})*({3})*({0})*sin((sqrt(({4})*({5}))*({0}))/({4})))' +
+                                                '/(2*({4})*sqrt(({4})*({5})))+(({1})*({2})*cos((sqrt(({4})*({5}))*({0}))/({4})))/({4})^2';
+                                            retval = _.parse(format(exp, t, a, b, sTerms[0].b, f.a, f.b));
+                                        }
+                                    } else if (f2.x.isLinear()) {
+                                        a = /** @type {NerdamerSymbolType} */ (_.divide(f2.a, new NerdamerSymbol(2)));
+                                        exp =
+                                            '(({1})*({0})*sin((sqrt(({2})*({3}))*({0}))/({2})))/(({2})*sqrt(({2})*({3})))';
+                                        retval = _.parse(format(exp, t, a, f.a, f.b));
+                                    } else if (f2.x.power.equals(2)) {
+                                        if (f2.b.equals(0)) {
+                                            a = /** @type {NerdamerSymbolType} */ (
+                                                _.divide(f2.a, new NerdamerSymbol(2))
+                                            );
+                                            exp =
+                                                '(({1})*sin((sqrt(({2})*({3}))*({0}))/({2})))/(({2})*sqrt(({2})*({3})))+(({1})*({0})*cos((sqrt(({2})*({3}))*({0}))/({2})))/({2})^2';
+                                            retval = _.parse(format(exp, t, a, f.a, f.b));
+                                        } else {
+                                            a = /** @type {NerdamerSymbolType} */ (
+                                                _.divide(f2.a, new NerdamerSymbol(2))
+                                            );
+                                            d = f2.b.negate();
+                                            exp =
+                                                '-((({2})*({4})-2*({1})*({3}))*sin((sqrt(({2})*({3}))*({0}))/({2})))/(2*({2})*({3})*sqrt(({2})*({3})))+' +
+                                                '(({4})*({0})*cos((sqrt(({2})*({3}))*({0}))/({2})))/(2*({2})*({3}))+(({1})*({0})*cos((sqrt(({2})*({3}))*({0}))/({2})))/({2})^2';
+                                            retval = _.parse(format(exp, t, a, f.a, f.b, d));
+                                        }
+                                    }
+                                } else {
+                                    a = /** @type {NerdamerSymbolType} */ (_.divide(num, new NerdamerSymbol(2)));
+                                    exp =
+                                        '(({1})*sin((sqrt(({2})*({3}))*({0}))/({2})))/(({3})*sqrt(({2})*({3})))-(({1})*({0})*cos((sqrt(({2})*({3}))*({0}))/({2})))/(({2})*({3}))';
+                                    retval = _.parse(format(exp, t, a, f.a, f.b));
+                                }
+                            } else if (symbol.isComposite()) {
+                                // 1/(s+1)^2
+                                if (denP.equals(2) && f.x.group === S) {
+                                    retval = _.parse(`(${m})*(${t})*e^(-(${f.b})*(${t}))`);
+                                } else {
+                                    retval = new NerdamerSymbol(0);
+
+                                    symbol = /** @type {NerdamerSymbolType} */ (
+                                        /** @type {PartFracSubModuleType} */ (Algebra.PartFrac).partfrac(
+                                            /** @type {NerdamerSymbolType} */ (_.expand(symbol)),
+                                            s_
+                                        )
+                                    );
+
+                                    symbol.each(x => {
+                                        retval = /** @type {NerdamerSymbolType} */ (
+                                            _.add(retval, __.LaPlace.inverse(x, s_, t))
+                                        );
+                                    }, true);
+                                }
+                            }
+                        }
+
+                        retval ||= _.symfunction('ilt', [inputSymbol, _.parse(String(s_)), _.parse(String(t))]);
+
+                        return /** @type {NerdamerSymbolType} */ (retval);
+                    },
+                    true
+                );
+            },
+        },
+        Statistics: {
+            /**
+             * @param {NerdamerSymbolType[]} arr
+             * @returns {Record<string, number>}
+             */
+            frequencyMap(arr) {
+                /** @type {Record<string, number>} */
+                const map = {};
+                // Get the frequency map
+                for (let i = 0, l = arr.length; i < l; i++) {
+                    const e = arr[i];
+                    const key = e.toString();
+                    map[key] ||= 0; // Default it to zero
+                    map[key]++; // Increment
+                }
+                return map;
+            },
+            /**
+             * @param {NerdamerSymbolType[]} arr
+             * @returns {NerdamerSymbolType[]}
+             */
+            sort(arr) {
+                return arr.sort((a, b) => {
+                    if (!a.isConstant() || !b.isConstant()) {
+                        _.error('Unable to sort! All values must be numeric');
+                    }
+                    return /** @type {number} */ (/** @type {unknown} */ (a.multiplier.subtract(b.multiplier)));
+                });
+            },
+            /**
+             * @param {NerdamerSymbolType[]} arr
+             * @returns {NerdamerSymbolType}
+             */
+            count(arr) {
+                return new NerdamerSymbol(arr.length);
+            },
+            /**
+             * @param {NerdamerSymbolType[]} arr
+             * @param {NerdamerSymbolType} [x_]
+             * @returns {NerdamerSymbolType}
+             */
+            sum(arr, x_) {
+                /** @type {NerdamerSymbolType} */
+                let sum = new NerdamerSymbol(0);
+                for (let i = 0, l = arr.length; i < l; i++) {
+                    const xi = arr[i].clone();
+                    if (x_) {
+                        sum = /** @type {NerdamerSymbolType} */ (
+                            _.add(_.pow(_.subtract(xi, x_.clone()), new NerdamerSymbol(2)), sum)
+                        );
+                    } else {
+                        sum = /** @type {NerdamerSymbolType} */ (_.add(xi, sum));
+                    }
+                }
+
+                return sum;
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            mean(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.mean(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+                return /** @type {NerdamerSymbolType} */ (_.divide(__.Statistics.sum(args), __.Statistics.count(args)));
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            median(...args) {
+                /** @type {NerdamerSymbolType} */
+                let retval;
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.median(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+                try {
+                    const sorted = __.Statistics.sort(args);
+                    const l = args.length;
+                    if (core.Utils.even(l)) {
+                        const mid = l / 2;
+                        retval = __.Statistics.mean(sorted[mid - 1], sorted[mid]);
+                    } else {
+                        retval = sorted[Math.floor(l / 2)];
+                    }
+                } catch (e) {
+                    if (/** @type {Error} */ (e).message === 'timeout') {
+                        throw e;
+                    }
+                    retval = _.symfunction('median', args);
+                }
+                return retval;
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            mode(...args) {
+                /** @type {NerdamerSymbolType} */
+                let retval;
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.mode(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+
+                const map = __.Statistics.frequencyMap(args);
+
+                // The mode of 1 item is that item as per issue #310 (verified by Happypig375).
+                if (core.Utils.keys(map).length === 1) {
+                    retval = args[0];
+                } else {
+                    // Invert by arraning them according to their frequency
+                    /** @type {Record<number, string | string[]>} */
+                    const inverse = {};
+                    for (const x in map) {
+                        if (!Object.hasOwn(map, x)) {
+                            continue;
+                        }
+                        const freq = map[x];
+                        // Check if it's in the inverse already
+                        if (freq in inverse) {
+                            const e = inverse[freq];
+                            // If it's already an array then just add it
+                            if (isArray(e)) {
+                                e.push(x);
+                            }
+                            // Convert it to and array
+                            else {
+                                inverse[freq] = [x, /** @type {string} */ (inverse[freq])];
+                            }
+                        } else {
+                            inverse[freq] = x;
+                        }
+                    }
+                    // The keys now represent the maxes. We want the max of those keys
+                    const keyNums = core.Utils.keys(inverse).map(k => Number(k));
+                    const maxKey = Math.max.apply(null, keyNums);
+                    const max = inverse[maxKey];
+                    // Check it's an array. If it is then map over the results and convert
+                    // them to NerdamerSymbol
+                    if (isArray(max)) {
+                        retval = _.symfunction(
+                            'mode',
+                            max.sort().map(v => _.parse(v))
+                        );
+                    } else {
+                        retval = _.parse(/** @type {string} */ (max));
+                    }
+                }
+
+                return retval;
+            },
+            /**
+             * @param {NerdamerSymbolType} k
+             * @param {NerdamerSymbolType[]} args
+             * @returns {NerdamerSymbolType}
+             */
+            gVariance(k, args) {
+                const x_ = __.Statistics.mean(...args);
+                const sum = __.Statistics.sum(args, x_);
+                return /** @type {NerdamerSymbolType} */ (_.multiply(k, sum));
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            variance(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.variance(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+                const k = /** @type {NerdamerSymbolType} */ (
+                    _.divide(new NerdamerSymbol(1), __.Statistics.count(args))
+                );
+                return __.Statistics.gVariance(k, args);
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            sampleVariance(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.sampleVariance(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+
+                const k = /** @type {NerdamerSymbolType} */ (
+                    _.divide(new NerdamerSymbol(1), _.subtract(__.Statistics.count(args), new NerdamerSymbol(1)))
+                );
+                return __.Statistics.gVariance(k, args);
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            standardDeviation(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.standardDeviation(.../** @type {NerdamerSymbolType[]} */ (args[0].elements));
+                }
+                return /** @type {NerdamerSymbolType} */ (
+                    _.pow(__.Statistics.variance(...args), new NerdamerSymbol(1 / 2))
+                );
+            },
+            /**
+             * @param {...NerdamerSymbolType} args
+             * @returns {NerdamerSymbolType}
+             */
+            sampleStandardDeviation(...args) {
+                // Handle arrays
+                if (isVector(args[0])) {
+                    return __.Statistics.sampleStandardDeviation(
+                        .../** @type {NerdamerSymbolType[]} */ (args[0].elements)
+                    );
+                }
+                return /** @type {NerdamerSymbolType} */ (
+                    _.pow(__.Statistics.sampleVariance(...args), new NerdamerSymbol(1 / 2))
+                );
+            },
+            /**
+             * @param {NerdamerSymbolType} x
+             * @param {NerdamerSymbolType} mean
+             * @param {NerdamerSymbolType} stdev
+             * @returns {NerdamerSymbolType}
+             */
+            zScore(x, mean, stdev) {
+                return /** @type {NerdamerSymbolType} */ (_.divide(_.subtract(x, mean), stdev));
+            },
+        },
+        Units: {
+            table: {
+                foot: '12 inch',
+                meter: '100 cm',
+                decimeter: '10 cm',
+            },
+        },
+    });
+
+    nerdamer.register([
+        {
+            name: 'laplace',
+            visible: true,
+            numargs: 3,
+            build() {
+                return __.LaPlace.transform;
+            },
+        },
+        {
+            name: 'ilt',
+            visible: true,
+            numargs: 3,
+            build() {
+                return __.LaPlace.inverse;
+            },
+        },
+        // Statistical
+        {
+            name: 'mean',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.mean;
+            },
+        },
+        {
+            name: 'median',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.median;
+            },
+        },
+        {
+            name: 'mode',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.mode;
+            },
+        },
+        {
+            name: 'smpvar',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.sampleVariance;
+            },
+        },
+        {
+            name: 'variance',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.variance;
+            },
+        },
+        {
+            name: 'smpstdev',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.sampleStandardDeviation;
+            },
+        },
+        {
+            name: 'stdev',
+            visible: true,
+            numargs: -1,
+            build() {
+                return __.Statistics.standardDeviation;
+            },
+        },
+        {
+            name: 'zscore',
+            visible: true,
+            numargs: 3,
+            build() {
+                return __.Statistics.zScore;
+            },
+        },
+    ]);
+
+    // Link registered functions externally
+    nerdamer.updateAPI();
+})();
+
+// Added for all.min.js
+if (typeof module !== 'undefined') {
+    module.exports = nerdamer;
+}
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 together-science
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
@@ -0,0 +1,16 @@
+/*
+ * Author : Martin Donk
+ * Website : http://www.nerdamer.com
+ * Email : martin.r.donk@gmail.com
+ * Source : https://github.com/jiggzson/nerdamer
+ * Can be used to load all add-ons with one require
+ */
+
+const nerdamer = require('./nerdamer.core.js');
+require('./Algebra.js');
+require('./Calculus.js');
+require('./Solve.js');
+require('./Extra.js');
+
+// Export nerdamer
+module.exports = nerdamer;
@@ -0,0 +1,261 @@
+/*
+ * Mathematical constants for nerdamer
+ * This file contains precomputed values and mathematical constants
+ * used throughout the library.
+ */
+
+/**
+ * Container of pregenerated prime numbers up to 2083 This array is used as a cache and can be extended at runtime by
+ * functions like generatePrimes()
+ */
+const PRIMES = [
+    2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109,
+    113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239,
+    241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379,
+    383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521,
+    523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661,
+    673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827,
+    829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991,
+    997, 1009, 1013, 1019, 1021, 1031, 1033, 1039, 1049, 1051, 1061, 1063, 1069, 1087, 1091, 1093, 1097, 1103, 1109,
+    1117, 1123, 1129, 1151, 1153, 1163, 1171, 1181, 1187, 1193, 1201, 1213, 1217, 1223, 1229, 1231, 1237, 1249, 1259,
+    1277, 1279, 1283, 1289, 1291, 1297, 1301, 1303, 1307, 1319, 1321, 1327, 1361, 1367, 1373, 1381, 1399, 1409, 1423,
+    1427, 1429, 1433, 1439, 1447, 1451, 1453, 1459, 1471, 1481, 1483, 1487, 1489, 1493, 1499, 1511, 1523, 1531, 1543,
+    1549, 1553, 1559, 1567, 1571, 1579, 1583, 1597, 1601, 1607, 1609, 1613, 1619, 1621, 1627, 1637, 1657, 1663, 1667,
+    1669, 1693, 1697, 1699, 1709, 1721, 1723, 1733, 1741, 1747, 1753, 1759, 1777, 1783, 1787, 1789, 1801, 1811, 1823,
+    1831, 1847, 1861, 1867, 1871, 1873, 1877, 1879, 1889, 1901, 1907, 1913, 1931, 1933, 1949, 1951, 1973, 1979, 1987,
+    1993, 1997, 1999, 2003, 2011, 2017, 2027, 2029, 2039, 2053, 2063, 2069, 2081, 2083,
+];
+
+/** Set representation of PRIMES for O(1) lookup This object is used as a cache and can be extended at runtime */
+/** @type {Record<number, boolean>} */
+const PRIMES_SET = {};
+for (const p of PRIMES) {
+    PRIMES_SET[p] = true;
+}
+
+/** High precision value of Pi (200 decimal places) Used for high-precision calculations */
+const LONG_PI =
+    '3.14159265358979323846264338327950288419716939937510582097494459230781640628620899862803482534211706798214' +
+    '808651328230664709384460955058223172535940812848111745028410270193852110555964462294895493038196';
+
+/** High precision value of Euler's number e (200 decimal places) Used for high-precision calculations */
+const LONG_E =
+    '2.718281828459045235360287471352662497757247093699959574966967627724076630353547594571382178525166427427466' +
+    '39193200305992181741359662904357290033429526059563073813232862794349076323382988075319525101901';
+
+/**
+ * Precomputed high-precision fraction values for the bigLog function. These are used for arbitrary-precision logarithm
+ * calculations. Each entry is a string representation of a high-precision rational number.
+ */
+const BIG_LOG_CACHE = [
+    '-253631954333118718762629409109262279926288908775918712466601196032/39970093576053625963957478139049824030906352922262642968060706375',
+    '0',
+    '24553090145869607172412918483124184864289170814122579923404694986469653261608528681589949629750677407356463601998534945057511664951799678336/35422621391945757431676178435630229283255250779216421054188228659061954317501699707236864189383591478024245495110561124597124995986978302375',
+    '369017335340917140706044240090243368728616279239227943871048759140274862131699550043150713059889196223917527172547/335894053932612728969975338549993764554481173661218585876475837409922537622385232776657791604345125227005476864000',
+    '24606853025626737903121303930100462245506322607985779603220820323211395607931699126390918477501325805513849611930008427268176602460462988972957593458726734897129954728102144/17750092415977639787139561330326170936321452137635322313122938207611787444311735251389066106937796085669460151963285086542745859461943369606018450213014148175716400146484375',
+    '399073568781976806715759409052286641738926636328983929439450824555613704676637191564699164303012247386095942144825603522401740680808466858044/247958349743620302021733249049411604982786755454514947379317600613433680222511897950658049325685140346169718465773927872179874971908848116625',
+    '1468102989495846944084741146947295378041808701256909016224309866143294556551407470861354311593351276612463858816796714569499021375899793849136855085849133702029337910502448189055357182595424959360/819363879309286303497217527375463120404739098260200279520788950777458900438307356738082930586032462601215802636320993648007907724899611296693997216938989854861043298494990214825163523387600982777',
+    '5896704855274661767824574093605344871722790278354431422729640950821239030785642943033153793245906863203822369276271050164634206965056233097479117980782641839669/3030306850569309344013726745100070601277982132543905537366562638553198167007159067544789592089960911065181606283478843359856123992707598685058297067179343872000',
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+    '3159076083816399509754948610929467278257473888282947311280653574634802580912280940686954763313882823327077171624015737719617373932318151594325834524000275847475866299387913048/1437757485694188822758304467756419845842037623148461107362957994816554782989250555362514354661961482939226272309026092009962414616417412938087494467254146002233028411865234375',
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+    '1953999166296955830935495158735359200362904181792947794529339487489730042568305997099959302322956898299616194932283060554261566410988618045107398092345476532371402134206635235570281738377188438407703089325315446371127042537576093536896282955524842632708645655481028161471313608974238110718242273935956977555610147714316158486553633871312187084618154014921190595222799283957140353/375191165084882521037046014569185165885459082629136124177286500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
+];
+
+if (typeof module !== 'undefined') {
+    module.exports = {
+        PRIMES,
+        PRIMES_SET,
+        LONG_PI,
+        LONG_E,
+        BIG_LOG_CACHE,
+    };
+}
@@ -0,0 +1,3 @@
+{
+	"type": "commonjs"
+}
@@ -0,0 +1,4 @@
+declare module 'virtual:nerdamer' {
+	const code: string;
+	export default code;
+}
@@ -0,0 +1,59 @@
+import { beforeEach, describe, expect, it } from 'vitest';
+import { SandboxService } from '$lib/services/sandbox.service';
+import { SANDBOX_TOOL_NAME } from '$lib/constants';
+
+const run = (code: string, timeoutMs?: number) =>
+	SandboxService.executeTool(SANDBOX_TOOL_NAME, {
+		code,
+		...(timeoutMs !== undefined ? { timeout_ms: timeoutMs } : {})
+	});
+
+describe('sandbox service', () => {
+	beforeEach(async () => {
+		const { settingsStore } = await import('$lib/stores/settings.svelte');
+		settingsStore.config = {
+			...settingsStore.config,
+			symbolicMathEnabled: true
+		};
+	});
+
+	it('executes plain JavaScript', async () => {
+		const reply = await run('return 1 + 1;');
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('=> 2');
+	});
+
+	it('exposes nerdamer for symbolic computation', async () => {
+		const reply = await run("return nerdamer.diff('sin(x)/x', 'x').toString();");
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('cos(x)');
+	});
+
+	it('computes exact rational arithmetic', async () => {
+		const reply = await run("return nerdamer('1/3 + 1/6').toString();");
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('=> 1/2');
+	});
+
+	it('proves a polynomial identity symbolically', async () => {
+		const reply = await run(
+			"return nerdamer('expand((1+x*y)^3 - (1 + 3*x*y + 3*x^2*y^2 + x^3*y^3))').toString();"
+		);
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('=> 0');
+	});
+
+	it('blocks network egress in the worker via CSP', async () => {
+		const reply = await run(
+			"try { await fetch('https://example.com/'); return 'leaked'; } catch { return 'blocked'; }"
+		);
+		expect(reply.isError).toBe(false);
+		expect(reply.content).toContain('=> blocked');
+	});
+
+	it('enforces the timeout on runaway code', async () => {
+		const reply = await run('while (true) {}', 500);
+		expect(reply.isError).toBe(true);
+		expect(reply.content).toContain('timed out');
+	});
+});
@@ -25,7 +25,7 @@
 		".storybook/**/*.ts",
 		".storybook/**/*.svelte"
 	],
-	"exclude": ["src/lib/services/sandbox-worker.js"]
+	"exclude": ["src/lib/services/sandbox-worker.js", "src/lib/vendors/**"]
 	// Path aliases are handled by https://svelte.dev/docs/kit/configuration#alias
 	// except $lib which is handled by https://svelte.dev/docs/kit/configuration#files
 	//
@@ -9,6 +9,7 @@ import { storybookTest } from '@storybook/addon-vitest/vitest-plugin';
 import { splashScreenPlugin } from './scripts/vite-plugin-splash-screen';
 import { buildInfoPlugin } from './scripts/vite-plugin-build-info';
 import { relativizeBasePlugin } from './scripts/vite-plugin-relativize-base';
+import { nerdamerPlugin } from './scripts/vite-plugin-nerdamer';
 import { playwright } from '@vitest/browser-playwright';
 import { SVELTEKIT_PWA_OPTIONS } from './src/lib/constants/pwa';

@@ -46,6 +47,7 @@ export default defineConfig({
 		SvelteKitPWA(SVELTEKIT_PWA_OPTIONS),
 		splashScreenPlugin(),
 		buildInfoPlugin(),
+		nerdamerPlugin(),
 		relativizeBasePlugin()
 	],
Author	SHA1	Message	Date
Johannes Gäßler	1425386fd9	CUDA: fix external compilation of q1_0 MMQ (#25778 )	2026-07-23 14:45:51 +02:00
Aaron Teo	e6dd0e29a6	args: refactor mlock/mmap/directio into load-mode (#20834 ) * args: overhaul mmap/mlock/dio into single arg Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: update docs with llama-gen-docs Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * chore: satisfy code quality Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * args: make the `+` sign an actual modifier now Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * chore: general code clean up + comments Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: fix deprecated flags support Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: quick sanity check Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * bench: sync llama-bench argument parsing Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * fix: bugfix variable behaviour + llama-bench lm column size Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: inverse commands should do the opposite instead of doing nothing Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * bench: fix incorrect dash Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * bench: fix missing modifiers for deprecated flags Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * llama: switch back to thread_local Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: switch back to single enum Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: update arg docs Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * chore: fix missing `mlock` from llama_load_mode_from_str + cleanup llama-bench Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * llama: fix mlock not activating Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: add deprecation warning when old and new flags are combined Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: cont add comment for todo in the future Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: sync with upstream Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: re-sync with upstream again Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> --------- Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>	2026-07-23 20:32:56 +08:00
Pascal	da296d6e72	contrib: fix leftovers from the AI usage policy update (#26030 )	2026-07-23 12:32:23 +02:00
Ilia Ilmer	c588c4f476	metal : add f16 type support to leaky relu (#25981 )	2026-07-23 11:45:46 +08:00
Shahir BIn Zulfiker	d941f6e1c9	conversion: fix non-MoE NomicBert GGUF conversion error (#25996 )	2026-07-23 11:01:35 +08:00
Xuan-Son Nguyen	4310aa4f87	contrib: allow all AI-generated code in general (#26012 )	2026-07-23 00:29:03 +02:00
Pascal	cf512566dc	ui: Add a "Default" option for the reasoning selector (#25846 ) * ui: add Default reasoning option that defers to the server The webui always injected enable_thinking, overriding the chat template default and the --reasoning flag, breaking models that reason unconditionally (e.g. Gemma 4 E4B) on a fresh client. Default sends nothing so the server decides, Off and effort levels force the value as before. All choices are remembered. Also remove the boolean thinking API from the conversations store and drop ChatFormReasoningEffortSubmenu.svelte (dead code). * ui: close the whole menu tree on reasoning level selection The reasoning levels were raw buttons inside the SubContent, so selecting one only closed the submenu via manual state while the root dropdown stayed open. DropdownMenu.Item closes the full tree on select like the sibling entries and brings native keyboard navigation. * ui: prevent the add menu tooltip from flashing when the dropdown closes	2026-07-22 23:09:49 +02:00
Oliver Simons	1a064ab092	CUDA: Improve NVFP4 W4A4 activation quantization (#25730 ) * Squash history before conflict-resolution during rebase on master WIP commit Add 32-byte loads, restore per-block amax Use nvfp4x4 intrinsic when available Fuse per-channel amax and quantization kernels Do pointer arithmetic only once on x Remove unnecessary ternary in the load We assert on host side that ne00 is 64-aligned Add back scale-search, but optimize it with intrinsics Code cleanup Make scale in MMQ-epilogue NVFP4-specific/restrictive for now Remove unneeded include, add comment Fix trailing whitespace Guard __builtin_align__(32) struct to NVIDIA Seems like HIP doesn't have this available, see https://github.com/ggml-org/llama.cpp/actions/runs/29438651734/job/87431623001 * compiler massaging to avoid unnecessary LDCs * kvalues_mxfp4 -> kvalues_nvfp4 in quantize_mmq_nvfp4 * Always pass in src1_scale.ptr * Extract ggml_cuda_is_aligned helper	2026-07-22 19:28:02 +02:00
Todor Boinovski	0278d8362d	hexagon: activation ops update (#25974 ) * hex-geglu: optimized all-in-one geglu microkernel * hex-geglu: enable non-contiguous src and strided DMA * hex-act: enable non-contiguous srs and strided DMA for rest of ACT ops * hex-act: generalize GLU per-thread functions via DEFINE_GLU_PER_THREAD macro * hexagon: move UNARY_SILU and UNARY_GELU to unary-ops * hex-act: replace the generic ops_context scratchpad usage with a local htp_vtcm_layout computation per act op. --------- Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>	2026-07-22 09:25:04 -07:00
Niklas Wenzel	e0833bf686	mtmd: use RAII for setting and resetting non-causal attention (#25723 ) * mtmd: use RAII for setting and resetting non-causal attention * mtmd: drop dependency on <optional> * mtmd: shorten class and variable names	2026-07-22 18:10:03 +02:00
rankaiyx	61328e6a91	feat(ui): add symbolic math support to JS sandbox via nerdamer (#25948 ) * feat(ui): add symbolic math support to JS sandbox via nerdamer Preload nerdamer (with decimal.js) in the sandboxed worker, exposing the `nerdamer` global for symbolic computation: simplify, expand, factor, diff, integrate, solve, laplace, ilt, limit, partfrac, gcd/lcm, roots, coefficients, and more. Mirrors the math.js integration pattern from the feature/sandbox-symbolic-math branch, but uses nerdamer for a lighter, more focused symbolic math engine. * Update sandbox-harness.ts * docs(ui): update sandbox tool description with detailed nerdamer usage guide * Clarify nerdamer usage in sandbox tool description Updated the description of the sandbox tool to clarify usage of nerdamer. * ui: build nerdamer sandbox prelude from vendored source Replace the vendored all.min.js with the readable nerdamer-prime source and its two bundled deps (big-integer, decimal.js), licenses included. A vite plugin bundles and minifies them at build time with the upstream esbuild flags, exposed as virtual:nerdamer and imported lazily on first sandbox use. The vendors package.json pins commonjs so the project level type: module does not break esbuild format detection. The harness gains a CSP removing network egress from the worker, and browser tests cover the prelude, exact arithmetic, the fetch block and the timeout. Upstream snapshot: together-science/nerdamer-prime@1936145 * feat(ui): make symbolic math (nerdamer) a user-toggleable setting - Add SYMBOLIC_MATH_ENABLED setting key and registry entry (checkbox, default false) - Convert SANDBOX_TOOL_DEFINITION to buildSandboxToolDefinition(includeSymbolicMath) so the tool description includes/excludes nerdamer API docs dynamically - Cache sandbox harness per variant ('nerdamer' / 'plain') for instant toggle - Deprecate SANDBOX_TOOL_DEFINITION constant alias for backward compatibility - Update tools store to pass symbolic math config into tool definition * docs(ui): tell LLM to list nerdamer functions first, do not guess * test(ui): enable symbolic math in sandbox tests via settingsStore config * style(ui): fix formatting for tools.svelte.ts --------- Co-authored-by: Pascal <admin@serveurperso.com>	2026-07-22 17:52:55 +02:00
Piotr Wilkin (ilintar)	e8e6c7af24	minor: fix reasoning preserve var for DS4 [no ci] (#25999 )	2026-07-22 14:32:54 +02:00
Pascal	6d5a910c50	common: infer the speculative type from the draft repo sidecars (#25989 ) With -hfd pointing to a repo that ships mtp-/dflash-/eagle3- sidecars and no --spec-type given, the draft resolved to a full model while the sidecar was the intended draft. When the speculative types are still at their default, discover the sidecars of the draft repo, pick the first available following the existing mtp > dflash > eagle3 priority, and set the corresponding type, so this now works without any extra flag: llama-server -hf repo:Q3_K_M -hfd repo:Q8_0 An explicit --spec-type disables the inference, and a draft repo without sidecars keeps resolving to a full model as before.	2026-07-22 13:06:35 +02:00
Piotr Wilkin (ilintar)	f534da26e4	Fix DeepSeek4 crafted template (#25414 ) * chat: fix DS4 template to explicitly follow reference behavior * Support DeepSeekv4 flag (`drop_reasoning`). * fix: hook DS3.2 parser for DS4 as well * fix: add tool result reordering * fix: post-merge	2026-07-22 12:54:40 +02:00
shalinib-ibm	3ce7da2c85	ggml: enable PowerPC backend variants on AIX (#25983 ) * ggml: enable PowerPC backend variants on AIX Allow the PowerPC CPU backend variants to be built on AIX by extending the platform check in the CMake configuration. This reuses the existing PowerPC backend implementations without changing their behavior. Also fix a missing semicolon in the PowerPC Q0 matmul implementation. * Fix missing semicolon in sgemm.cpp	2026-07-22 17:26:40 +08:00
KyleHagy	b4d6c7d8ff	ci : fix SYCL package shared library lookup (#25987 )	2026-07-22 17:20:40 +08:00
m1el	7347430f44	webgpu : add CONV_2D_DW (depthwise conv2d) kernel (#25847 ) * webgpu : add CONV_2D_DW (depthwise conv2d) kernel Implement GGML_OP_CONV_2D_DW for the WebGPU backend, ported from the Vulkan backend's conv2d_dw.comp. Assisted-by: Claude Opus-4.8 * Remove unnecessary comments in webgpu support * update supported ops tables, triggered by adding webgpu CONV_2D_DW	2026-07-22 17:24:44 +09:00
Pascal	c5a4a0bb83	cuda: GET_ROWS quants (#25962 ) * cuda: add k-quant support to GET_ROWS Device-side embedding lookups require GET_ROWS to handle the k-quants used by common GGUF recipes (Q4_K_M stores token_embd as q6_K). Without it the backend rejects the op and the scheduler falls back to the host, copying the full embedding matrix back on every token in single-device graphs. Factor the super-block dequantizers out of the dequantize_block kernels in convert.cu into shared device functions in dequantize.cuh and reuse them from a new k_get_rows_kq kernel : one thread block dequantizes one (dst row, super-block) pair with the existing thread layouts, 32 threads for q4_K and 64 for the other k-quants. Covers q2_K to q6_K in get_rows_cuda and supports_op. i-quants are left as a TODO. * cuda: add i-quant support to GET_ROWS Extends the shared super-block dequantizers to the nine i-quants and reuses them from k_get_rows_kq with the 32-thread layout of the matching convert.cu kernels. supports_op gates the k-quant and i-quant path on ne0 being a multiple of QK_K, which iq4_nl does not guarantee on its own (QK4_NL sub-blocks). mxfp4 is left as a TODO. * cuda: add mxfp4 support to GET_ROWS Moves the mxfp4 dequantizer into the shared super-block helpers and reuses it from k_get_rows_kq with the 32-thread layout of the matching convert.cu kernel. mxfp4 joins the ne0 % QK_K gate in supports_op since its 32-value sub-blocks do not guarantee QK_K-aligned rows on their own. This closes GET_ROWS type coverage on CUDA: every quantized GGML type now takes the direct device path. * cuda: gate the GET_ROWS row size only for 32-value sub-block types Address review from @pwilkin: the i-quant commit replaced the return shared by the whole supported type cascade, so f16/f32/bf16/i32 and the legacy quants also inherited the ne0 % QK_K == 0 gate and any row size that is not a multiple of 256 fell back to the scheduler. Split the cascade: unconditional support is restored everywhere, the gate stays only on iq4_nl and mxfp4 whose 32-value sub-blocks do not guarantee the QK_K super-blocks the kernel iterates on.	2026-07-22 08:42:47 +02:00
helanfxz	67b9b0e7f6	llama-arch: fix DeepSeek4 APE tensor op (#25945 )	2026-07-22 10:55:44 +08:00