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>

.github/workflows/release.yml

@@ -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 }}

common/arg.cpp

@@ -351,6 +351,10 @@ 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 +395,14 @@ 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()) {
@@ -527,8 +538,57 @@ 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() ||
+                                    !plan_spec.eagle3.local_path.empty();
+    if (!plan_spec.mtp.local_path.empty() && !had_spec_url) {
+        tasks.emplace_back(plan_spec.mtp, opts, [&]() {
+            // only use the discovered MTP head when no draft path is set yet
+            if (params.speculative.draft.mparams.path.empty()) {
+                params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.mtp);
+            } else {
+                hf_cache::finalize_file(plan_spec.mtp);
+            }
+        });
+    }
+    if (!plan_spec.dflash.local_path.empty() && !had_spec_url) {
+        tasks.emplace_back(plan_spec.dflash, opts, [&]() {
+            // only use the discovered DFlash sidecar when no draft path is set yet
+            if (params.speculative.draft.mparams.path.empty()) {
+                params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.dflash);
+            } else {
+                hf_cache::finalize_file(plan_spec.dflash);
+            }
+        });
+    }
+    if (!plan_spec.eagle3.local_path.empty() && !had_spec_url) {
+        tasks.emplace_back(plan_spec.eagle3, opts, [&]() {
+            // only use the discovered Eagle3 sidecar when no draft path is set yet
+            if (params.speculative.draft.mparams.path.empty()) {
+                params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.eagle3);
+            } else {
+                hf_cache::finalize_file(plan_spec.eagle3);
+            }
+        });
+    }
+
     // handle plan_spec (e.g. --spec-draft-hf)
-    if (!plan_spec.model_files.empty() && !had_spec_url) {
+    if (!plan_spec.model_files.empty() && !had_spec_url && !spec_sidecar_found) {
         add_tasks(plan_spec.model_files, plan_spec.primary, params.speculative.draft.mparams);
         had_spec_url = true;
     }

common/chat-auto-parser-generator.cpp

@@ -47,6 +47,8 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
     data.generation_prompt = common_chat_template_generation_prompt(tmpl, inputs);
     data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.preserved_tokens  = autoparser.preserved_tokens;
+    data.additional_stops.insert(data.additional_stops.end(),
+        autoparser.additional_stops.begin(), autoparser.additional_stops.end());
 
     std::string parser_generation_prompt = data.generation_prompt;
 
@@ -286,7 +288,13 @@ common_peg_parser analyze_tools::build_func_parser(common_chat_peg_builder & p,
         // we only emit tool_close when we can actually see the closing marker. This prevents
         // premature closing during partial parsing when we've seen e.g. "</" which could be
         // either "</tool_call>" (end) or "<arg_key>" prefix that failed to match.
-        func_parser = func_parser + p.tool_close(p.peek(p.literal(format.per_call_end)));
+        // Laguna (v4): the model may emit whitespace between the last </arg_value> and
+        // </tool_call> even though the template renders them tight. Tolerate optional
+        // leading space in the close lookahead so the tool call still closes.
+        auto close_peek = arguments.tolerate_intertag_whitespace
+                              ? p.peek(p.space() + p.literal(format.per_call_end))
+                              : p.peek(p.literal(format.per_call_end));
+        func_parser = func_parser + p.tool_close(close_peek);
     } else {
         func_parser = func_parser + p.tool_close(p.space());  // force this to process tool closing callbacks in mapper
     }

common/chat-auto-parser.h

@@ -206,6 +206,7 @@ struct tool_arguments_analysis {
     std::string value_prefix;  // e.g., "", "<arg_value>", ""
     std::string value_suffix;  // e.g., "</param>", "</arg_value>", ""
     std::string separator;     // e.g., "", "\n", ","
+    bool tolerate_intertag_whitespace = false; // Laguna: accept optional whitespace between arg tags
 };
 
 struct tool_id_analysis {
@@ -388,6 +389,7 @@ struct autoparser {
 
     // Preserved tokens for tokenizer (union of all non-empty markers)
     std::vector<std::string> preserved_tokens;
+    std::vector<std::string> additional_stops;  // literal stop strings (e.g. Laguna </assistant>) caught however tokenized
 
     autoparser() = default;
 

common/chat-diff-analyzer.cpp

@@ -173,6 +173,26 @@ static std::vector<std::function<void(const common_chat_template & tmpl, autopar
               LOG_DBG(ANSI_ORANGE "[Patch: JSON name/parameters tool instruction]\n" ANSI_RESET);
           }
       },
+      // Laguna (poolside) - the v4 chat template renders reasoning and tool-arg
+      // delimiters with formatting whitespace ("<think>\n", "</arg_value>\n") that
+      // the model does not emit, so the inferred delimiters carry a spurious
+      // newline and never match the model output. Trim to the bare tag. (v8
+      // renders without the whitespace, so this is a no-op there.)
+      [](const common_chat_template & tmpl, autoparser & analysis) -> void {
+          if (tmpl.src.find("laguna_glm_thinking") != std::string::npos) {
+              analysis.reasoning.start              = trim_whitespace(analysis.reasoning.start);
+              analysis.reasoning.end                = trim_whitespace(analysis.reasoning.end);
+              analysis.tools.arguments.value_prefix = trim_whitespace(analysis.tools.arguments.value_prefix);
+              analysis.tools.arguments.value_suffix = trim_whitespace(analysis.tools.arguments.value_suffix);
+              analysis.tools.arguments.separator    = trim_whitespace(analysis.tools.arguments.separator);
+              analysis.tools.arguments.tolerate_intertag_whitespace = true;
+              // The CONTROL/eot </assistant> token only halts generation when emitted as the
+              // single token; after tool calls the model can spell it out as text tokens.
+              // A literal stop string catches it either way.
+              analysis.additional_stops.push_back("</assistant>");
+              LOG_DBG(ANSI_ORANGE "[Patch: Laguna]\n" ANSI_RESET);
+          }
+      },
 
     });
 

common/chat.cpp

@@ -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);
     }
 

common/jinja/caps.cpp

@@ -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,

conversion/__init__.py

@@ -18,6 +18,7 @@ __all__ = [
 
 TEXT_MODEL_MAP: dict[str, str] = {
     "AfmoeForCausalLM": "afmoe",
+    "LagunaForCausalLM": "laguna",
     "ApertusForCausalLM": "llama",
     "ArceeForCausalLM": "llama",
     "ArcticForCausalLM": "arctic",

conversion/base.py

@@ -1682,6 +1682,9 @@ class TextModel(ModelBase):
         if chkhsh == "9dcf830ee9990cdbf78cc523a5f7bd9ad8f3f9890c2d3581d2785ad10f07049d":
             # ref: https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Base
             res = "mellum2"
+        if chkhsh == "972da7b59cec44d1f0a490a86c96df53859e486e481563e5dddac155013d87ac":
+            # ref: https://huggingface.co/poolside/Laguna-XS.2
+            res = "laguna"
 
         if res is None:
             logger.warning("\n")

conversion/hunyuan.py

@@ -338,6 +338,12 @@ class HunyuanVLTextModel(HunYuanModel):
 
     def __init__(self, dir_model: Path, *args, **kwargs):
         super().__init__(dir_model, *args, **kwargs)
+        # transformers 5.13.0 encodes HunyuanVL XD-RoPE as dynamic + mrope_section.
+        # Normalize it to avoid the HunYuan dynamic-RoPE context assertion.
+        if self.rope_parameters.get("rope_type") == "dynamic" and "mrope_section" in self.rope_parameters:
+            self.rope_parameters["rope_type"] = "xdrope"
+            self.rope_parameters["type"] = "xdrope"
+            self.rope_parameters["xdrope_section"] = list(self.rope_parameters["mrope_section"])
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()

conversion/laguna.py

@@ -0,0 +1,207 @@
+from __future__ import annotations
+
+import re
+from collections.abc import Iterable
+from typing import TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, TextModel, gguf, logger
+
+
+@ModelBase.register("LagunaForCausalLM")
+class LagunaModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.LAGUNA
+    _experts: list[dict] | None = None
+    _gate_types: list[str] | None = None
+
+    # --- vocab ---------------------------------------------------------------
+
+    def set_vocab(self) -> None:
+        self._set_vocab_gpt2()
+
+        # Some Laguna releases wrap the chat template in tokenizer_config.json as
+        # "{% include 'chat_template.jinja' %}", which SpecialVocab embeds verbatim
+        # and llama.cpp's jinja engine cannot process. Prefer the resolved template
+        # from the chat_template.jinja file so the GGUF is self-contained.
+        tmpl_file = self.dir_model / "chat_template.jinja"
+        if tmpl_file.is_file():
+            self.gguf_writer.add_chat_template(tmpl_file.read_text(encoding="utf-8"))
+            logger.info("gguf: embedded resolved chat_template.jinja (overriding include directive)")
+
+        # eos_token_id is a list [2, 24]: token 2 (EOS, also BOS) and token 24
+        # (</assistant>, the turn-end). _set_vocab_gpt2 only records the scalar
+        # eos, so register the extra id as eot; llama.cpp folds eot into its EOG
+        # set, so the model halts on </assistant> natively.
+        eos_ids = self.hparams.get("eos_token_id")
+        if isinstance(eos_ids, list):
+            bos_id = self.hparams.get("bos_token_id")
+            extra = [e for e in eos_ids if e != bos_id]
+            if extra:
+                self.gguf_writer.add_eot_token_id(extra[0])
+                logger.info(f"gguf: registered eot_token_id={extra[0]} from eos list {eos_ids}")
+
+    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
+        # </assistant> is the assistant turn-end (registered as eot below). The
+        # HF tokenizer flags it special=false, so the base classifies it as
+        # USER_DEFINED and llama.cpp renders its text into generated content,
+        # leaking "</assistant>" and breaking response parsing. It is a control
+        # marker, so promote it to CONTROL: llama.cpp then treats it as
+        # end-of-generation and suppresses its text.
+        tokens, toktypes, tokpre = super().get_vocab_base()
+        for i, tok in enumerate(tokens):
+            if tok == "</assistant>":
+                toktypes[i] = gguf.TokenType.CONTROL
+                logger.info(f"gguf: marked </assistant> (id {i}) as CONTROL token")
+        return tokens, toktypes, tokpre
+
+    # --- hparams -------------------------------------------------------------
+
+    def set_gguf_parameters(self) -> None:
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        # super() does not emit vocab_size for the gpt2 vocab path; head_count is
+        # overridden with a per-layer array (XS.2 varies heads per layer via
+        # num_attention_heads_per_layer; M.1 is uniform and omits it).
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+
+        per_layer_heads = hparams.get("num_attention_heads_per_layer")
+        if not per_layer_heads:
+            per_layer_heads = [hparams["num_attention_heads"]] * hparams["num_hidden_layers"]
+        assert len(per_layer_heads) == hparams["num_hidden_layers"], (
+            f"num_attention_heads_per_layer length {len(per_layer_heads)} != "
+            f"num_hidden_layers {hparams['num_hidden_layers']}"
+        )
+        self.gguf_writer.add_head_count(per_layer_heads)
+
+        # Resolve + validate the attention gate type now so an inconsistent
+        # `gating` field fails at conversion time. See _attn_gate_types.
+        self._attn_gate_types()
+
+        # SWA window size (M.1 has none -> key omitted, swa_type stays NONE).
+        sliding_window = hparams.get("sliding_window") or 0
+        if sliding_window > 0:
+            self.gguf_writer.add_sliding_window(sliding_window)
+
+        # MoE (expert_count / expert_used_count come from super().set_gguf_parameters())
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(hparams["shared_expert_intermediate_size"])
+        self.gguf_writer.add_expert_weights_norm(True)  # HF reference always sum-normalises after top-k
+        self.gguf_writer.add_expert_weights_scale(float(hparams["moe_routed_scaling_factor"]))
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        # Leading dense layers (XS.2 has 1, M.1 has 3) before the MoE layers.
+        mlp_layer_types: list[str] = hparams["mlp_layer_types"]
+        leading_dense = 0
+        for t in mlp_layer_types:
+            if t == "dense":
+                leading_dense += 1
+            else:
+                break
+        self.gguf_writer.add_leading_dense_block_count(leading_dense)
+
+        # Per-layer-type RoPE dimension count (partial rotary). base emits
+        # rope_freq_base(_swa) and the YaRN params from self.rope_parameters.
+        head_dim = hparams["head_dim"]
+        full_rope = self.rope_parameters["full_attention"]
+        self.gguf_writer.add_rope_dimension_count(
+            int(head_dim * float(full_rope.get("partial_rotary_factor", 1.0))))
+        swa_rope = self.rope_parameters.get("sliding_attention")
+        if swa_rope is not None:
+            self.gguf_writer.add_rope_dimension_count_swa(
+                int(head_dim * float(swa_rope.get("partial_rotary_factor", 1.0))))
+
+    def _attn_gate_types(self) -> list[str]:
+        """Per-layer attention output gate type: "per_head" or "per_element".
+
+        `gating_types` (per layer) is authoritative when present; otherwise the
+        scalar `gating` field is used (the "per-element"/"per-head" string, or
+        the legacy boolean True == per-head, as in Laguna-XS.2).
+
+        Fails loudly when the model is per-element but the `gating` field does
+        not declare that as a string: runtimes that key off `gating` (vLLM,
+        transformers) ignore gating_types and read a bare boolean True as
+        per-head, silently corrupting the model. Surfacing it here keeps a
+        broken checkpoint from being packaged as if it were fine.
+        """
+        if self._gate_types is not None:
+            return self._gate_types
+        hparams = self.hparams
+        n_layer = hparams["num_hidden_layers"]
+        gating = hparams.get("gating")
+        gating_types = hparams.get("gating_types")
+
+        def _norm(t: object) -> str:
+            sval = str(t).replace("-", "_")
+            if sval in ("per_element", "per_head"):
+                return sval
+            raise ValueError(f"Laguna: unrecognised attention gate type {t!r}")
+
+        if gating_types:
+            assert len(gating_types) == n_layer, (
+                f"gating_types length {len(gating_types)} != num_hidden_layers {n_layer}")
+            types = [_norm(t) for t in gating_types]
+        elif isinstance(gating, str):
+            types = [_norm(gating)] * n_layer
+        elif gating is True:
+            types = ["per_head"] * n_layer
+        else:
+            raise ValueError(
+                f"Laguna: cannot determine attention gate type "
+                f"(gating={gating!r}, gating_types={gating_types!r})")
+
+        if any(t == "per_element" for t in types) and not (
+                isinstance(gating, str) and _norm(gating) == "per_element"):
+            raise ValueError(
+                f"Laguna config declares a per-element attention gate but "
+                f"`gating`={gating!r} is not the string \"per-element\". Runtimes that "
+                f"read `gating` (vLLM, transformers) will mis-handle this checkpoint as "
+                f"per-head. Set gating=\"per-element\" in the source config.")
+
+        self._gate_types = types
+        return types
+
+    # --- tensor handling -----------------------------------------------------
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Per-expert MoE weights: model.layers.{bid}.mlp.experts.{xid}.{w}.weight.
+        # Only the NUMBERED per-expert weights are stacked; the router bias
+        # (mlp.experts.e_score_correction_bias) takes the normal mapping path.
+        if re.search(r"mlp\.experts\.\d+\.", name):
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
+            assert bid is not None
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+            self._experts[bid][name] = data_torch
+            needed = [f"model.layers.{bid}.mlp.experts.{x}.{w}.weight"
+                      for x in range(n_experts) for w in ("gate_proj", "up_proj", "down_proj")]
+            if all(e in self._experts[bid] for e in needed):
+                for w_name in ["gate_proj", "up_proj", "down_proj"]:
+                    datas = [self._experts[bid][f"model.layers.{bid}.mlp.experts.{x}.{w_name}.weight"]
+                             for x in range(n_experts)]
+                    stacked = torch.stack(datas, dim=0)
+                    merged = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+                    yield from TextModel.modify_tensors(self, stacked, merged, bid)
+                self._experts[bid].clear()
+                return
+            return
+        # Cross-check the gate projection width against the declared gate type;
+        # a mismatch means the weights and config disagree -> fail, do not guess.
+        if bid is not None and name.endswith("self_attn.g_proj.weight"):
+            heads = (self.hparams.get("num_attention_heads_per_layer")
+                     or [self.hparams["num_attention_heads"]] * self.hparams["num_hidden_layers"])
+            n_head = heads[bid]
+            head_dim = self.hparams["head_dim"]
+            gate_type = self._attn_gate_types()[bid]
+            expected = n_head * head_dim if gate_type == "per_element" else n_head
+            out_features = int(data_torch.shape[0])
+            if out_features != expected:
+                raise ValueError(
+                    f"Laguna layer {bid}: g_proj output width {out_features} contradicts the "
+                    f"declared {gate_type} gate (expected {expected}); weights and config disagree.")
+
+        yield from TextModel.modify_tensors(self, data_torch, name, bid)

convert_hf_to_gguf_update.py

@@ -162,6 +162,7 @@ models = [
     {"name": "granite-embed-multi-97m", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-embedding-97m-multilingual-r2", },
     {"name": "granite-embed-multi-311m", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-embedding-311m-multilingual-r2", },
     {"name": "mellum2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Base"},
+    {"name": "laguna",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/poolside/Laguna-XS.2", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions

docs/ops.md

@@ -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 | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |

ggml/src/CMakeLists.txt

@@ -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)

ggml/src/ggml-cpu/kleidiai/kleidiai.cpp

@@ -1719,6 +1719,7 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
 
             return true;
         }
+
         return false;
     }
 
@@ -1727,6 +1728,20 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
             if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type()) {
                 return (ggml::cpu::tensor_traits *) op->src[0]->extra;
             } else {
+                // KleidiAI only has kernels for Q4_0 and Q8_0. For a quantized weight of any
+                // other type (K-quants, IQ) it declines the op and returns nullptr below, so
+                // KleidiAI does not accelerate it. Another CPU backend may still take the op,
+                // and this can run during graph planning, so the message says what KleidiAI
+                // did rather than what ends up executing. Warn once per process.
+                if (ggml_is_quantized(op->src[0]->type) &&
+                    op->src[0]->type != GGML_TYPE_Q4_0 && op->src[0]->type != GGML_TYPE_Q8_0) {
+                    static std::atomic<bool> warned(false);
+                    if (!warned.exchange(true)) {
+                        GGML_LOG_WARN("kleidiai: no kernel for tensor type %s, not accelerated by KleidiAI "
+                                      "(kernels available for Q4_0 and Q8_0)\n",
+                                      ggml_type_name(op->src[0]->type));
+                    }
+                }
                 if (op->src[0]->type != GGML_TYPE_F16) {
                     return nullptr;
                 }

ggml/src/ggml-cpu/llamafile/sgemm.cpp

@@ -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) {

ggml/src/ggml-cuda/common.cuh

@@ -937,6 +937,9 @@ 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
 ) {

ggml/src/ggml-cuda/convert.cu

@@ -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>

ggml/src/ggml-cuda/dequantize.cuh

@@ -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);
+    }
+}

ggml/src/ggml-cuda/getrows.cu

@@ -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;
     }

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -2703,6 +2703,7 @@ static int ggml_cuda_try_gdn_cache_fusion(
 
 static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int node_idx, ggml_cuda_topk_moe_args & args) {
     args.sigmoid         = false;
+    args.sqrt_softplus   = false;
     args.softmax         = false;
     args.delayed_softmax = false;
     args.prob_bias       = false;
@@ -2716,10 +2717,17 @@ static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int nod
     }
 
     if (nodes[node_idx]->op == GGML_OP_UNARY) {
-        if (ggml_get_unary_op(nodes[node_idx]) != GGML_UNARY_OP_SIGMOID) {
+        const ggml_unary_op unary_op = ggml_get_unary_op(nodes[node_idx]);
+        if (unary_op == GGML_UNARY_OP_SIGMOID) {
+            args.sigmoid = true;
+        } else if (unary_op == GGML_UNARY_OP_SOFTPLUS && node_idx + 1 < n_nodes &&
+                   nodes[node_idx + 1]->op == GGML_OP_SQRT && nodes[node_idx + 1]->src[0] == nodes[node_idx]) {
+            // sqrt(softplus(x)) scoring (DeepSeek-V4)
+            args.sqrt_softplus = true;
+            node_idx++;
+        } else {
             return false;
         }
-        args.sigmoid = true;
     }
 
     if (nodes[node_idx]->op == GGML_OP_ARGSORT) {
@@ -2728,7 +2736,7 @@ static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int nod
 
     node_idx++;
 
-    if (args.sigmoid || args.softmax) {
+    if (args.sigmoid || args.sqrt_softplus || args.softmax) {
         // SOFTMAX -> RESHAPE
         if (node_idx >= n_nodes || nodes[node_idx]->op != GGML_OP_RESHAPE ||
                 nodes[node_idx]->src[0] != nodes[node_idx - 1]) {
@@ -3172,21 +3180,27 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph
             const ggml_tensor * scale   = nullptr;
 
             if (!args.delayed_softmax) {
-                ggml_op gating_op = args.sigmoid ? GGML_OP_UNARY : GGML_OP_SOFT_MAX;
-                int     out_nodes[2];  // nodes which can't be elided
+                int out_nodes[2];  // nodes which can't be elided
+
+                if (args.sigmoid) {
+                    ops.insert(ops.end(), { GGML_OP_UNARY });
+                } else if (args.sqrt_softplus) {
+                    ops.insert(ops.end(), { GGML_OP_UNARY, GGML_OP_SQRT });
+                } else {
+                    ops.insert(ops.end(), { GGML_OP_SOFT_MAX });
+                }
+                const int i_probs = i + (int) ops.size() - 1;  // last node of the gating activation
 
                 if (args.prob_bias) {
-                    bias = cgraph->nodes[i + 2]->src[1];
-                    ops.insert(ops.end(), { gating_op, GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, GGML_OP_VIEW,
+                    bias = cgraph->nodes[i_probs + 2]->src[1];
+                    ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_ADD, GGML_OP_ARGSORT, GGML_OP_VIEW,
                                             GGML_OP_GET_ROWS });
-                    out_nodes[0] = i + 4;
-                    ids          = cgraph->nodes[i + 4];
+                    out_nodes[0] = i_probs + 4;
                 } else {
-                    ops.insert(ops.end(),
-                               { gating_op, GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS });
-                    out_nodes[0] = i + 3;
-                    ids          = cgraph->nodes[i + 3];
+                    ops.insert(ops.end(), { GGML_OP_RESHAPE, GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS });
+                    out_nodes[0] = i_probs + 3;
                 }
+                ids = cgraph->nodes[out_nodes[0]];
 
                 if (args.norm) {
                     ops.insert(ops.end(),
@@ -4831,7 +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;
                 }

ggml/src/ggml-cuda/topk-moe.cu

@@ -8,6 +8,7 @@
 // Kernel config struct - passed by value to CUDA kernel
 struct topk_moe_config {
     bool use_sigmoid;
+    bool use_sqrt_softplus;
     bool with_norm;
     bool delayed_softmax;
 };
@@ -67,6 +68,16 @@ __device__ void sigmoid_warp_inplace(float (&vals)[experts_per_thread], const in
     }
 }
 
+template <int experts_per_thread, bool use_limit>
+__device__ void sqrt_softplus_warp_inplace(float (&vals)[experts_per_thread], const int limit, const int lane) {
+#pragma unroll
+    for (int i = 0; i < experts_per_thread; i++) {
+        const int  idx    = lane + i * WARP_SIZE;
+        const bool active = !use_limit || (idx < limit);
+        vals[i]           = active ? sqrtf(vals[i] > 20.0f ? vals[i] : logf(1.0f + expf(vals[i]))) : -INFINITY;
+    }
+}
+
 /*
     This kernel does the following:
     1. optionally softmax over the logits per token [n_experts, n_tokens]
@@ -115,6 +126,8 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float *
     if (!config.delayed_softmax) {
         if (config.use_sigmoid) {
            sigmoid_warp_inplace<experts_per_thread, false>(wt, n_experts, threadIdx.x);
+        } else if (config.use_sqrt_softplus) {
+           sqrt_softplus_warp_inplace<experts_per_thread, false>(wt, n_experts, threadIdx.x);
         } else {
            softmax_warp_inplace<experts_per_thread, false>(wt, n_experts, threadIdx.x);
         }
@@ -364,9 +377,10 @@ void ggml_cuda_op_topk_moe(ggml_backend_cuda_context &     ctx,
     }
 
     topk_moe_config config;
-    config.use_sigmoid     = args.sigmoid;
-    config.with_norm       = with_norm;
-    config.delayed_softmax = args.delayed_softmax;
+    config.use_sigmoid       = args.sigmoid;
+    config.use_sqrt_softplus = args.sqrt_softplus;
+    config.with_norm         = with_norm;
+    config.delayed_softmax   = args.delayed_softmax;
 
     if (bias) {
         launch_topk_moe_cuda<true>(ctx, logits_d, weights_d, ids_d, bias_d, n_rows, n_experts, n_expert_used, clamp_val,
@@ -415,7 +429,7 @@ bool ggml_cuda_should_use_topk_moe(const ggml_tensor * gating_op,
     } else if (gating_op->op == GGML_OP_UNARY) {
         ggml_unary_op op = ggml_get_unary_op(gating_op);
 
-        if (op != GGML_UNARY_OP_SIGMOID) {
+        if (op != GGML_UNARY_OP_SIGMOID && op != GGML_UNARY_OP_SOFTPLUS) {
             return false;
         }
     }

ggml/src/ggml-cuda/topk-moe.cuh

@@ -5,6 +5,7 @@
 
 struct ggml_cuda_topk_moe_args {
     bool sigmoid{};
+    bool sqrt_softplus{};
     bool softmax{};
     bool delayed_softmax{};
     bool prob_bias{};

ggml/src/ggml-hexagon/ggml-hexagon.cpp

@@ -1286,7 +1286,8 @@ struct ggml_hexagon_opbatch {
         int64_t nb2 = is_repack ? nb1 * ne1 : t->nb[2];
         int64_t nb3 = is_repack ? nb2 * t->ne[2] : t->nb[3];
 
-        return (h->ne[0] == ne0) && (h->ne[1] == ne1) && (h->ne[2] == t->ne[2]) && (h->ne[3] == t->ne[3]) &&
+        return (h->type == t->type) &&
+               (h->ne[0] == ne0) && (h->ne[1] == ne1) && (h->ne[2] == t->ne[2]) && (h->ne[3] == t->ne[3]) &&
                (h->nb[0] == t->nb[0]) && (h->nb[1] == nb1) && (h->nb[2] == nb2) && (h->nb[3] == nb3);
     }
 
@@ -3083,7 +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;
     }
 
@@ -3094,7 +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;
         }
     }
@@ -4151,12 +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;

ggml/src/ggml-hexagon/htp/main.c

@@ -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:

ggml/src/ggml-hexagon/htp/unary-ops.c

@@ -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;

ggml/src/ggml-hexagon/htp/unary-ops.h

@@ -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:

ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp

@@ -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;

ggml/src/ggml-webgpu/ggml-webgpu.cpp

@@ -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);

ggml/src/ggml-webgpu/wgsl-shaders/conv2d_dw.wgsl

@@ -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));
+}

gguf-py/gguf/constants.py

@@ -507,6 +507,7 @@ class MODEL_ARCH(IntEnum):
     DOTS1            = auto()
     ARCEE            = auto()
     AFMOE            = auto()
+    LAGUNA           = auto()
     ERNIE4_5         = auto()
     ERNIE4_5_MOE     = auto()
     HUNYUAN_MOE      = auto()
@@ -1088,6 +1089,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.DOTS1:            "dots1",
     MODEL_ARCH.ARCEE:            "arcee",
     MODEL_ARCH.AFMOE:            "afmoe",
+    MODEL_ARCH.LAGUNA:           "laguna",
     MODEL_ARCH.ERNIE4_5:         "ernie4_5",
     MODEL_ARCH.ERNIE4_5_MOE:     "ernie4_5-moe",
     MODEL_ARCH.FALCON_H1:        "falcon-h1",
@@ -3823,6 +3825,31 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_POST_NORM,
         MODEL_TENSOR.FFN_EXP_PROBS_B,
     ],
+    MODEL_ARCH.LAGUNA: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+    ],
     MODEL_ARCH.ERNIE4_5: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

gguf-py/gguf/tensor_mapping.py

@@ -479,6 +479,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.e_score_correction",                    # exaone-moe
             "model.layers.{bid}.block_sparse_moe.gate.e_score_correction",  # kimi
             "model.layers.{bid}.moe.router_bias",                           # step3.5 expert selection bias
+            "model.layers.{bid}.mlp.experts.e_score_correction",       # laguna
         ),
 
         # Feed-forward up

models/templates/deepseek-ai-DeepSeek-V4.jinja

@@ -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'] -}}

models/templates/poolside-Laguna-S-2.1.jinja

@@ -0,0 +1,93 @@
+{#- Iteration on laguna_glm_thinking_v8/chat_template.jinja -#}
+{#- No formatting instructions -#}
+{{- "〈|EOS|〉" -}}
+{%- set enable_thinking = enable_thinking | default(false) -%}
+{%- set add_generation_prompt = add_generation_prompt | default(false) -%}
+
+{#- ───── header (system message) ───── -#}
+{#- A caller-supplied system message with empty content opts out of the default below, producing no <system> block — used to train without a system message. -#}
+{%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%}
+{%- if messages and messages[0].role == "system" -%}
+  {%- set system_message = messages[0].content -%}
+  {%- set messages = messages[1:] -%}
+{%- endif -%}
+
+{%- set has_sys = system_message and system_message.strip() -%}
+{%- if has_sys or tools or enable_thinking -%}
+  {{- "<system>" -}}
+
+  {%- if has_sys -%}
+    {{- system_message.rstrip() -}}
+    {%- if tools -%}{{- "\n\n" -}}{%- endif -%}
+  {%- endif -%}
+
+  {%- if tools -%}
+    {{- "### Tools\n\n" -}}
+    {{- "You may call functions to assist with the user query.\n" -}}
+    {{- "All available function signatures are listed below:\n" -}}
+    {{- "<available_tools>\n" -}}
+    {%- for tool in tools -%}
+      {{- (tool | tojson) ~ "\n" -}}
+    {%- endfor -%}
+    {{- "</available_tools>" -}}
+  {%- endif -%}
+
+  {{- "</system>\n" -}}
+{%- endif -%}
+
+{#- ───── main loop ───── -#}
+{%- for message in messages -%}
+  {%- set content = message.content if message.content is string else "" -%}
+  {%- if message.role == "user" -%}
+    {{- "<user>" + content + "</user>\n" -}}
+  {%- elif message.role == "assistant" -%}
+    {%- generation -%}
+      {{- "<assistant>" -}}
+      {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content -#}
+      {%- set reasoning_content = '' -%}
+      {%- if message.reasoning is string -%}
+        {%- set reasoning_content = message.reasoning -%}
+      {%- elif message.reasoning_content is string -%}
+        {%- set reasoning_content = message.reasoning_content -%}
+      {%- endif -%}
+      {#- Display reasoning content for all messages if enable_thinking -#}
+      {%- if enable_thinking -%}
+        {{- '<think>' + reasoning_content + '</think>' -}}
+      {%- else -%}
+        {{- '</think>' -}}
+      {%- endif -%}
+      {#- Display main content (trailing newline only when no tool_calls follow) -#}
+      {%- if content -%}
+        {{- content -}}
+      {%- endif -%}
+      {%- if message.tool_calls -%}
+        {%- for tool_call in message.tool_calls -%}
+          {%- set function_data = tool_call.function -%}
+          {{- '<tool_call>' + function_data.name -}}
+          {%- set _args = function_data.arguments -%}
+          {%- for k, v in _args.items() -%}
+            {{- "<arg_key>" ~ k ~ "</arg_key>" -}}
+            {{- "<arg_value>" -}}{{- v | tojson(ensure_ascii=False) if v is not string else v -}}{{- "</arg_value>" -}}
+          {%- endfor -%}
+          {{- "</tool_call>" -}}
+        {%- endfor -%}
+      {%- endif -%}
+      {{- "</assistant>\n" -}}
+    {%- endgeneration -%}
+  {%- elif message.role == "tool" -%}
+    {{- "<tool_response>" + content + "</tool_response>\n" -}}
+  {%- elif message.role == "system" -%}
+    {#- Render additional system messages (the first one, if any, is handled separately in the header and was sliced off above) -#}
+    {{- "<system>" + content + "</system>\n" -}}
+  {%- endif -%}
+{%- endfor -%}
+{#- ───── generation prompt ───── -#}
+{%- if add_generation_prompt -%}
+  {{- "<assistant>" -}}
+  {#- ───── Include reasoning mode directive ───── -#}
+  {%- if enable_thinking -%}
+    {{- '<think>' -}}
+  {%- else -%}
+    {{- '</think>' -}}
+  {%- endif -%}
+{%- endif -%}

models/templates/poolside-Laguna-XS-2.1.jinja

@@ -0,0 +1,132 @@
+{#- Copied from laguna_glm_thinking_v4/chat_template.jinja -#}
+{#- Removes prefix that references <think> token, and replaces message.reasoning_content reference with message.reasoning -#}
+{{- "〈|EOS|〉" -}}
+{%- set enable_thinking = enable_thinking | default(false) -%}
+{%- set render_assistant_messages_raw = render_assistant_messages_raw | default(false) -%}
+{%- set add_generation_prompt = add_generation_prompt | default(false) -%}
+
+{#- ───── header (system message) ───── -#}
+{%- set system_message = "" -%}
+{%- if messages and messages[0].role == "system" -%}
+  {%- set system_message = messages[0].content -%}
+{%- endif -%}
+
+{%- if (system_message and system_message.strip()) or tools -%}
+  {{- "<system>\n" -}}
+
+  {%- if system_message and system_message.strip() -%}
+    {{- "\n" -}}
+    {{- system_message.rstrip() -}}
+  {%- endif -%}
+
+  {%- if tools -%}
+    {{- "\n\n### Tools\n\n" -}}
+    {%- set ns = namespace(tool_string="You may call functions to assist with the user query.\n"
+          ~ "All available function signatures are listed below:\n"
+          ~ "<available_tools>\n") -%}
+    {%- for tool in tools -%}
+      {%- set ns.tool_string = ns.tool_string ~ (tool | tojson) ~ "\n" -%}
+    {%- endfor -%}
+    {%- if enable_thinking -%}
+      {%- set tool_string = ns.tool_string + "</available_tools>\n\n" ~
+            "Wrap your thinking in '<think>', '</think>' tags, followed by a function call. For each function call, return an unescaped XML-like object with function name and arguments within '<tool_call>' and '</tool_call>' tags, like here:\n" ~
+            "<think> your thoughts here </think>\n" ~
+            "<tool_call>function-name\n<arg_key>argument-key</arg_key>\n<arg_value>value-of-argument-key</arg_value>\n" ~
+            "</tool_call>" -%}
+    {%- else -%}
+      {%- set tool_string = ns.tool_string + "</available_tools>\n\n" ~
+            "For each function call, return an unescaped XML-like object " ~
+            "with function name and arguments within '<tool_call>' and '</tool_call>' tags, like here:\n" ~
+            "<tool_call>function-name\n<arg_key>argument-key</arg_key>\n<arg_value>value-of-argument-key</arg_value>\n" ~
+            "</tool_call>" -%}
+    {%- endif -%}
+    {{- tool_string -}}
+  {%- endif -%}
+
+  {{- "\n</system>\n" -}}
+{%- endif -%}
+
+{#- ───── main loop ───── -#}
+{%- for message in messages -%}
+  {%- set content = message.content if message.content is string else "" -%}
+  {%- if message.role == "user" -%}
+    {{- "<user>\n" + content + "\n</user>\n" -}}
+  {%- elif message.role == "assistant" -%}
+    {%- generation -%}
+      {{- "<assistant>\n" -}}
+      {%- if render_assistant_messages_raw -%}
+        {#- Raw mode: prepend the generation prompt token, then dump content verbatim. -#}
+        {#- The generation prompt is <think> when enable_thinking, </think> otherwise. -#}
+        {#- Only prepend if content doesn't already start with it. -#}
+        {%- if enable_thinking -%}
+          {%- if not content.startswith('<think>') -%}
+            {{- '<think>' -}}
+          {%- endif -%}
+        {%- else -%}
+          {%- if not content.startswith('</think>') -%}
+            {{- '</think>' -}}
+          {%- endif -%}
+        {%- endif -%}
+        {{- content -}}
+        {#- Append closing tag if content doesn't already end with it. -#}
+        {%- if not content.endswith('</assistant>\n') and not content.endswith('</assistant>') -%}
+          {{- '\n</assistant>' -}}
+        {%- endif -%}
+        {{- "\n" -}}
+      {%- else -%}
+        {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content, or from <think> tags -#}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning is string %}
+          {%- set reasoning_content = message.reasoning %}
+        {%- elif message.reasoning_content is string %}
+          {%- set reasoning_content = message.reasoning_content %}
+        {%- endif %}
+        {#- Always strip <think> tags from content if present to avoid duplication -#}
+        {%- if '</think>' in content %}
+          {%- if not reasoning_content %}
+            {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+          {%- endif %}
+          {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+        {%- endif %}
+        {#- Display reasoning content for all messages -#}
+        {%- if reasoning_content -%}
+          {{- '<think>\n' + reasoning_content.strip() + '\n</think>\n' -}}
+        {%- else -%}
+          {{- '</think>\n' -}}
+        {%- endif -%}
+        {#- Display main content -#}
+        {%- if content.strip() -%}
+          {{- content.strip() ~ "\n" -}}
+        {%- endif -%}
+        {%- if message.tool_calls -%}
+          {%- for tool_call in message.tool_calls -%}
+            {%- set function_data = tool_call.function -%}
+            {{- '<tool_call>' + function_data.name }}
+            {% set _args = function_data.arguments %}
+            {%- for k, v in _args.items() -%}
+              {{- "<arg_key>" ~ k ~ "</arg_key>\n" -}}
+              {{- "<arg_value>"}}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{{ "</arg_value>\n" -}}
+            {%- endfor -%}
+            {{- "</tool_call>\n" -}}
+          {%- endfor -%}
+        {%- endif -%}
+        {{- "</assistant>\n" -}}
+      {%- endif -%}
+    {%- endgeneration -%}
+  {%- elif message.role == "tool" -%}
+    {{- "<tool_response>\n" + content + "\n</tool_response>\n" -}}
+  {%- elif message.role == "system" and loop.index0 != 0 -%}
+    {#- Render additional system messages (skip the first one which is handled separately in the header) -#}
+    {{- "<system>\n" + content + "\n</system>\n" -}}
+  {%- endif -%}
+{%- endfor -%}
+{#- ───── generation prompt ───── -#}
+{%- if add_generation_prompt -%}
+  {{- "<assistant>\n" -}}
+  {#- ───── Include reasoning mode directive ───── -#}
+  {%- if not enable_thinking %}
+    {{- '</think>' -}}
+  {%- else %}
+    {{- '<think>' -}}
+  {%- endif %}
+{%- endif -%}

models/templates/poolside-Laguna-XS.2.jinja

@@ -0,0 +1,132 @@
+{#- Iteration on laguna_glm_thinking_v5/chat_template.jinja -#}
+{#- Adds a default system message (used when no system message is provided in `messages`). -#}
+{{- "〈|EOS|〉" -}}
+{%- set enable_thinking = enable_thinking | default(false) -%}
+{%- set render_assistant_messages_raw = render_assistant_messages_raw | default(false) -%}
+{%- set add_generation_prompt = add_generation_prompt | default(false) -%}
+
+{#- ───── header (system message) ───── -#}
+{%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%}
+{%- if messages and messages[0].role == "system" -%}
+  {%- set system_message = messages[0].content -%}
+{%- endif -%}
+
+{%- if (system_message and system_message.strip()) or tools -%}
+  {{- "<system>\n" -}}
+
+  {%- if system_message and system_message.strip() -%}
+    {{- "\n" -}}
+    {{- system_message.rstrip() -}}
+  {%- endif -%}
+
+  {%- if tools -%}
+    {{- "\n\n### Tools\n\n" -}}
+    {%- set ns = namespace(tool_string="You may call functions to assist with the user query.\n"
+          ~ "All available function signatures are listed below:\n"
+          ~ "<available_tools>\n") -%}
+    {%- for tool in tools -%}
+      {%- set ns.tool_string = ns.tool_string ~ (tool | tojson) ~ "\n" -%}
+    {%- endfor -%}
+    {%- if enable_thinking -%}
+      {%- set tool_string = ns.tool_string + "</available_tools>\n\n" ~
+            "Wrap your thinking in '<think>', '</think>' tags, followed by a function call. For each function call, return an unescaped XML-like object with function name and arguments within '<tool_call>' and '</tool_call>' tags, like here:\n" ~
+            "<think> your thoughts here </think>\n" ~
+            "<tool_call>function-name\n<arg_key>argument-key</arg_key>\n<arg_value>value-of-argument-key</arg_value>\n" ~
+            "</tool_call>" -%}
+    {%- else -%}
+      {%- set tool_string = ns.tool_string + "</available_tools>\n\n" ~
+            "For each function call, return an unescaped XML-like object " ~
+            "with function name and arguments within '<tool_call>' and '</tool_call>' tags, like here:\n" ~
+            "<tool_call>function-name\n<arg_key>argument-key</arg_key>\n<arg_value>value-of-argument-key</arg_value>\n" ~
+            "</tool_call>" -%}
+    {%- endif -%}
+    {{- tool_string -}}
+  {%- endif -%}
+
+  {{- "\n</system>\n" -}}
+{%- endif -%}
+
+{#- ───── main loop ───── -#}
+{%- for message in messages -%}
+  {%- set content = message.content if message.content is string else "" -%}
+  {%- if message.role == "user" -%}
+    {{- "<user>\n" + content + "\n</user>\n" -}}
+  {%- elif message.role == "assistant" -%}
+    {%- generation -%}
+      {{- "<assistant>\n" -}}
+      {%- if render_assistant_messages_raw -%}
+        {#- Raw mode: prepend the generation prompt token, then dump content verbatim. -#}
+        {#- The generation prompt is <think> when enable_thinking, </think> otherwise. -#}
+        {#- Only prepend if content doesn't already start with it. -#}
+        {%- if enable_thinking -%}
+          {%- if not content.startswith('<think>') -%}
+            {{- '<think>' -}}
+          {%- endif -%}
+        {%- else -%}
+          {%- if not content.startswith('</think>') -%}
+            {{- '</think>' -}}
+          {%- endif -%}
+        {%- endif -%}
+        {{- content -}}
+        {#- Append closing tag if content doesn't already end with it. -#}
+        {%- if not content.endswith('</assistant>\n') and not content.endswith('</assistant>') -%}
+          {{- '\n</assistant>' -}}
+        {%- endif -%}
+        {{- "\n" -}}
+      {%- else -%}
+        {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content, or from <think> tags -#}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning is string %}
+          {%- set reasoning_content = message.reasoning %}
+        {%- elif message.reasoning_content is string %}
+          {%- set reasoning_content = message.reasoning_content %}
+        {%- endif %}
+        {#- Always strip <think> tags from content if present to avoid duplication -#}
+        {%- if '</think>' in content %}
+          {%- if not reasoning_content %}
+            {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+          {%- endif %}
+          {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+        {%- endif %}
+        {#- Display reasoning content for all messages -#}
+        {%- if reasoning_content -%}
+          {{- '<think>\n' + reasoning_content.strip() + '\n</think>\n' -}}
+        {%- else -%}
+          {{- '</think>\n' -}}
+        {%- endif -%}
+        {#- Display main content -#}
+        {%- if content.strip() -%}
+          {{- content.strip() ~ "\n" -}}
+        {%- endif -%}
+        {%- if message.tool_calls -%}
+          {%- for tool_call in message.tool_calls -%}
+            {%- set function_data = tool_call.function -%}
+            {{- '<tool_call>' + function_data.name }}
+            {% set _args = function_data.arguments %}
+            {%- for k, v in _args.items() -%}
+              {{- "<arg_key>" ~ k ~ "</arg_key>\n" -}}
+              {{- "<arg_value>"}}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{{ "</arg_value>\n" -}}
+            {%- endfor -%}
+            {{- "</tool_call>\n" -}}
+          {%- endfor -%}
+        {%- endif -%}
+        {{- "</assistant>\n" -}}
+      {%- endif -%}
+    {%- endgeneration -%}
+  {%- elif message.role == "tool" -%}
+    {{- "<tool_response>\n" + content + "\n</tool_response>\n" -}}
+  {%- elif message.role == "system" and loop.index0 != 0 -%}
+    {#- Render additional system messages (skip the first one which is handled separately in the header) -#}
+    {{- "<system>\n" + content + "\n</system>\n" -}}
+  {%- endif -%}
+{%- endfor -%}
+{#- ───── generation prompt ───── -#}
+{%- if add_generation_prompt -%}
+  {{- "<assistant>\n" -}}
+  {#- ───── Include reasoning mode directive ───── -#}
+  {%- if not enable_thinking %}
+    {{- '</think>' -}}
+  {%- else %}
+    {{- '<think>' -}}
+  {%- endif %}
+{%- endif -%}

src/llama-arch.cpp

@@ -108,6 +108,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_DOTS1,            "dots1"            },
     { LLM_ARCH_ARCEE,            "arcee"            },
     { LLM_ARCH_AFMOE,            "afmoe"            },
+    { LLM_ARCH_LAGUNA,           "laguna"           },
     { LLM_ARCH_ERNIE4_5,         "ernie4_5"         },
     { LLM_ARCH_ERNIE4_5_MOE,     "ernie4_5-moe"     },
     { LLM_ARCH_HUNYUAN_MOE,      "hunyuan-moe"      },
@@ -665,7 +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}},
@@ -832,7 +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}},

src/llama-arch.h

@@ -113,6 +113,7 @@ enum llm_arch {
     LLM_ARCH_DOTS1,
     LLM_ARCH_ARCEE,
     LLM_ARCH_AFMOE,
+    LLM_ARCH_LAGUNA,
     LLM_ARCH_ERNIE4_5,
     LLM_ARCH_ERNIE4_5_MOE,
     LLM_ARCH_HUNYUAN_MOE,

src/llama-model-saver.cpp

@@ -28,6 +28,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) {
         case LLM_ARCH_MIMO2:
         case LLM_ARCH_STEP35:
         case LLM_ARCH_MELLUM:
+        case LLM_ARCH_LAGUNA:
             return false;
         default:
             return true;

src/llama-model.cpp

@@ -250,6 +250,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_arcee(params);
         case LLM_ARCH_AFMOE:
             return new llama_model_afmoe(params);
+        case LLM_ARCH_LAGUNA:
+            return new llama_model_laguna(params);
         case LLM_ARCH_ERNIE4_5:
             return new llama_model_ernie4_5(params);
         case LLM_ARCH_ERNIE4_5_MOE:
@@ -2549,6 +2551,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_COGVLM:
         case LLM_ARCH_PANGU_EMBED:
         case LLM_ARCH_AFMOE:
+        case LLM_ARCH_LAGUNA:
         case LLM_ARCH_QWEN3NEXT:
         case LLM_ARCH_MIMO2:
         case LLM_ARCH_STEP35:

src/llama-vocab.cpp

@@ -496,6 +496,12 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\\r\\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_LAGUNA:
+                regex_exprs = {
+                    "[^\\n]+|[\\n]+",
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
             case LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE:
                 regex_exprs = {
                     // original regex from tokenizer.json
@@ -2342,6 +2348,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 tokenizer_pre == "afmoe") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_AFMOE;
                 clean_spaces = false;
+            } else if (
+                tokenizer_pre == "laguna") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_LAGUNA;
+                clean_spaces = false;
             } else if (
                 tokenizer_pre == "minimax-m2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2;

src/llama-vocab.h

@@ -64,6 +64,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_WHITESPACE        = 53,
     LLAMA_VOCAB_PRE_TYPE_GRANITE_EMB_MULTI = 54,
     LLAMA_VOCAB_PRE_TYPE_MELLUM2           = 55,
+    LLAMA_VOCAB_PRE_TYPE_LAGUNA            = 56,
 };
 
 struct LLM_KV;

src/models/laguna.cpp

@@ -0,0 +1,332 @@
+// Laguna (poolside): sigmoid-routed MoE with a score-correction bias, one shared
+// expert, a softplus attention output gate, QK-norm, and per-layer-type RoPE
+// (YaRN on full-attention layers, plain RoPE on sliding-window layers). XS.2 is
+// hybrid full/SWA with a per-head gate; M.1 is full-attention with a per-element
+// gate. Shares the MoE/gate structure with afmoe.
+
+#include "models.h"
+
+void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+    ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead);
+    ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+    ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+    ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+
+    // Laguna ships one shared expert and stores its size directly (routed and
+    // shared experts may differ), so read the size from expert_shared_feed_forward_length.
+    // The count is not in the config; default to 1 but read the key if present.
+    hparams.n_expert_shared = 1;
+    ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared, false);
+    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+    if (hparams.n_ff_shexp == 0) {
+        // Weightless fixtures (test-llama-archs) omit this key; derive a nonzero
+        // size so the shared expert is still built. Real GGUFs always carry the
+        // exact value (routed and shared FF lengths may differ).
+        hparams.n_ff_shexp = hparams.n_ff_exp * hparams.n_expert_shared;
+    }
+
+    // Sliding-window attention is OPTIONAL. XS.2 is hybrid (full / SWA / SWA /
+    // SWA repeating, period 4 starting with full); M.1 has no sliding window
+    // (all layers full attention). When sliding_window is absent or zero we
+    // leave swa_type = NONE and skip the SWA-specific per-layer-type RoPE.
+    hparams.n_swa = 0;
+    ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+    if (hparams.n_swa > 0) {
+        hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+
+        uint32_t swa_period = 4;
+        ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false);
+        hparams.set_swa_pattern(swa_period, /*dense_first=*/true);  // XS.2: FULL at il%4==0
+
+        // Per-layer-type RoPE: full layers use YaRN θ=500000 over 64 dims;
+        // SWA layers use default RoPE θ=10000 over 128 dims. Base load_hparams
+        // already reads ROPE_FREQ_BASE and ROPE_DIMENSION_COUNT into the
+        // non-SWA fields; we explicitly pull the SWA mirrors here.
+        hparams.rope_freq_base_train_swa  = hparams.rope_freq_base_train;
+        hparams.rope_freq_scale_train_swa = 1.0f;  // SWA uses plain RoPE (no YaRN scaling); do NOT inherit full layers 1/factor
+        ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
+        ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false);
+    }
+
+    // Default the expert gating function to SIGMOID when the key is absent
+    // (matches the HF reference).
+    if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+        hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+    }
+
+    switch (hparams.n_layer()) {
+        case 40: type = LLM_TYPE_30B_A3B;   break;  // Laguna-XS.2
+        case 70: type = LLM_TYPE_230B_A10B; break;  // Laguna-M.1
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_laguna::load_arch_tensors(llama_model_loader & ml) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    if (output == NULL) {
+        // tied embeddings fallback
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    const int64_t n_ff_exp   = hparams.n_ff_exp;
+    const int64_t n_ff_shexp = hparams.n_ff_shexp;
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        // Per-layer head count — Laguna varies n_head between full and SWA
+        // layers (48 vs 64 in XS.2). KV head count is uniform.
+        const int64_t n_head_il    = hparams.n_head(i);
+        const int64_t n_head_kv_il = hparams.n_head_kv(i);
+        const int64_t n_embd_q_il  = n_embd_head_k * n_head_il;
+        const int64_t n_embd_k_il  = n_embd_head_k * n_head_kv_il;
+        const int64_t n_embd_v_il  = n_embd_head_v * n_head_kv_il;
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        create_tensor_qkv(layer, i, n_embd, n_embd_q_il, n_embd_k_il, n_embd_v_il, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_q_il, n_embd}, 0);
+
+        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+
+        // Attention output gate. XS.2 is per-head (g_proj -> n_head, one scalar
+        // per head broadcast over head_dim at multiply time); M.1 is per-element
+        // (g_proj -> n_head*head_dim, like afmoe). Detect from the stored tensor
+        // shape so a single arch handles both; the graph mirrors this check.
+        // Gate width selects per-head vs per-element. Real GGUFs always carry the
+        // gate tensor, so read the width from it and require EXACTLY one of the two
+        // valid widths -- never guess between them. Weightless fixtures
+        // (test-llama-archs) have no gate tensor; fall back to the per-head layout so
+        // the per-head reshape path is still exercised.
+        const int64_t n_gate_per_head = n_head_il;
+        const int64_t n_gate_per_elem = n_embd_head_k * n_head_il;
+        const ggml_tensor * gate_meta = ml.get_tensor_meta(tn(LLM_TENSOR_ATTN_GATE, "weight", i).str().c_str());
+        int64_t n_gate_out;
+        if (gate_meta != nullptr) {
+            n_gate_out = gate_meta->ne[1];
+            if (n_gate_out != n_gate_per_head && n_gate_out != n_gate_per_elem) {
+                GGML_ABORT("Laguna: unexpected attention gate width %lld at layer %d "
+                           "(expected %lld per-head or %lld per-element)",
+                           (long long) n_gate_out, i, (long long) n_gate_per_head, (long long) n_gate_per_elem);
+            }
+        } else {
+            n_gate_out = n_gate_per_head;
+        }
+        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_gate_out}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        if ((uint32_t)i >= hparams.n_layer_dense_lead) {
+            // MoE layer
+            layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,    "weight", i), {n_embd, n_expert}, 0);
+            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias",   i), {n_expert}, 0);
+
+            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff_exp, n_expert}, 0);
+            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd,   n_expert}, 0);
+
+            // Always-on shared expert.
+            layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd,    n_ff_shexp}, 0);
+            layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd,    n_ff_shexp}, 0);
+            layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd},    0);
+        } else {
+            // Dense layer (the leading n_layer_dense_lead layers)
+            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff,   n_embd}, 0);
+        }
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_laguna::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+llama_model_laguna::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v();
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+    // No MuP embedding scale (laguna omits this; afmoe scales by sqrt(hidden)).
+
+    ggml_tensor * inp_pos = build_inp_pos();
+    // XS.2 is hybrid SWA -> interleaved-SWA KV input; M.1 is all-full -> plain
+    // KV input. Pick the matching input (and build_attn overload) per swa_type.
+    const bool has_swa = hparams.swa_type != LLAMA_SWA_TYPE_NONE;
+    llm_graph_input_attn_kv      * inp_attn_kv   = has_swa ? nullptr : build_attn_inp_kv();
+    llm_graph_input_attn_kv_iswa * inp_attn_iswa = has_swa ? build_attn_inp_kv_iswa() : nullptr;
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
+
+    for (int il = 0; il < n_layer; ++il) {
+        const bool    is_swa_il   = hparams.is_swa(il);
+        const int64_t n_head_il   = hparams.n_head(il);
+        const int64_t n_head_kv_il = hparams.n_head_kv(il);
+
+        // Per-layer-type RoPE config. SWA layers run plain rope (no YaRN),
+        // achieved by zeroing the YaRN ext/beta params for those layers.
+        const int   n_rot_l       = is_swa_il ? hparams.n_rot_swa : n_rot;
+        const float freq_base_l   = is_swa_il ? hparams.rope_freq_base_train_swa : freq_base;
+        const float freq_scale_l  = is_swa_il ? hparams.rope_freq_scale_train_swa : freq_scale;
+        const float ext_factor_l  = is_swa_il ? 0.0f : ext_factor;
+        // YaRN magnitude scaling (mscale) is already handled by the framework:
+        // llama_context pre-divides cparams.yarn_attn_factor by (1 + 0.1*ln(factor))
+        // to cancel ggml rope_yarn's internal mscale *= 1 + 0.1*ln(1/freq_scale).
+        // Pass attn_factor straight through (like every other arch); SWA layers run
+        // plain RoPE (ext_factor 0, no mscale) so force 1.0 there.
+        const float attn_factor_l = is_swa_il ? 1.0f : attn_factor;
+        const float beta_fast_l   = is_swa_il ? 0.0f : beta_fast;
+        const float beta_slow_l   = is_swa_il ? 0.0f : beta_slow;
+        const int   n_ctx_orig_l  = is_swa_il ? hparams.n_ctx_train : n_ctx_orig;
+
+        ggml_tensor * inpSA = inpL;
+
+        // Pre-norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // Self-attention
+        {
+            ggml_tensor * attn_inp = cur;  // saved for the gate projection
+
+            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+                    n_embd_head, n_head_il, n_head_kv_il, il);
+
+            // g_proj on the *pre-attention* hidden state (matches HF
+            // reference: gate is computed from the same `hidden_states`
+            // input as q/k/v, not from the attn output).
+            ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
+            cb(gate, "attn_gate_proj", il);
+
+            // QK RMSNorm at head_dim level (Qwen3 style)
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+            cb(Kcur, "Kcur_normed", il);
+
+            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
+                    n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l,
+                    ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
+            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
+                    n_rot_l, rope_type, n_ctx_orig_l, freq_base_l, freq_scale_l,
+                    ext_factor_l, attn_factor_l, beta_fast_l, beta_slow_l);
+            cb(Qcur, "Qcur_rope", il);
+            cb(Kcur, "Kcur_rope", il);
+
+            cur = has_swa
+                ? build_attn(inp_attn_iswa,
+                        NULL, NULL, NULL,    // o_proj deferred until after gating
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il)
+                : build_attn(inp_attn_kv,
+                        NULL, NULL, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+
+            // Softplus output gate (the unary kernel computes softplus in fp32
+            // and casts back). Two shapes, distinguished by the g_proj output
+            // dim (matching the load-time detection):
+            //   XS.2 per-head     : gate [n_head_il, n_tokens] -> reshape to
+            //                       [1, n_head_il, n_tokens] and broadcast over
+            //                       head_dim against cur [head_dim, n_head, T].
+            //   M.1  per-element  : gate [n_head_il*head_dim, n_tokens] spans the
+            //                       full attention output -> direct ggml_mul.
+            gate = ggml_softplus(ctx0, gate);
+            cb(gate, "attn_gate_softplus", il);
+
+            const int64_t n_tokens = cur->ne[1];
+            if (model.layers[il].wqkv_gate->ne[1] == n_head_il) {
+                cur  = ggml_reshape_3d(ctx0, cur,  n_embd_head, n_head_il, n_tokens);
+                gate = ggml_reshape_3d(ctx0, gate, 1,           n_head_il, n_tokens);
+                cur  = ggml_mul(ctx0, cur, gate);
+                cur  = ggml_reshape_2d(ctx0, cur, n_embd_head * n_head_il, n_tokens);
+            } else {
+                cur = ggml_mul(ctx0, cur, gate);
+            }
+            cb(cur, "attn_gated", il);
+
+            cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
+            cb(cur, "attn_o_proj", il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // Pre-norm only (no post-attn norm)
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        if ((uint32_t)il >= hparams.n_layer_dense_lead) {
+            // MoE: sigmoid routing + score-correction bias + sum-norm +
+            // routed_scaling_factor (all handled by build_moe_ffn).
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    model.layers[il].ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU,
+                    hparams.expert_weights_norm,
+                    hparams.expert_weights_scale,
+                    (llama_expert_gating_func_type) hparams.expert_gating_func,
+                    il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // Always-on shared expert, summed in parallel.
+            ggml_tensor * ffn_shexp = build_ffn(cur,
+                    model.layers[il].ffn_up_shexp,   NULL, NULL,
+                    model.layers[il].ffn_gate_shexp, NULL, NULL,
+                    model.layers[il].ffn_down_shexp, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(ffn_shexp, "ffn_shexp", il);
+
+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            cb(cur, "ffn_out", il);
+        } else {
+            // Dense FFN for the leading n_layer_dense_lead layers (XS.2: 1, M.1: 3)
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        // No post-ffn norm
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        inpL = cur;
+    }
+
+    cur = inpL;
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

src/models/models.h

@@ -1681,6 +1681,19 @@ struct llama_model_afmoe : public llama_model_base {
 };
 
 
+struct llama_model_laguna : public llama_model_base {
+    llama_model_laguna(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
+
 struct llama_model_ernie4_5 : public llama_model_base {
     llama_model_ernie4_5(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;

tests/test-backend-ops.cpp

@@ -5960,6 +5960,7 @@ enum MoeGatingFunc {
     GATING_FUNC_SOFTMAX,
     GATING_FUNC_SIGMOID,
     GATING_FUNC_SOFTMAX_WEIGHT,
+    GATING_FUNC_SQRT_SOFTPLUS,
 };
 
 struct test_topk_moe : public test_case {
@@ -6003,7 +6004,8 @@ struct test_topk_moe : public test_case {
         ggml_tensor * logits = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne.data());
         ggml_tensor * probs            =
             (gating_func == GATING_FUNC_SOFTMAX) ? ggml_soft_max(ctx, logits) :
-            (gating_func == GATING_FUNC_SIGMOID) ? ggml_sigmoid(ctx, logits) : logits;
+            (gating_func == GATING_FUNC_SIGMOID) ? ggml_sigmoid(ctx, logits) :
+            (gating_func == GATING_FUNC_SQRT_SOFTPLUS) ? ggml_sqrt(ctx, ggml_softplus(ctx, logits)) : logits;
         ggml_set_name(probs, "probs");
 
         ggml_tensor * selection_probs = probs;
@@ -9584,7 +9586,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         }
     }
 
-    for (auto gate : {GATING_FUNC_SOFTMAX, GATING_FUNC_SIGMOID, GATING_FUNC_SOFTMAX_WEIGHT}) {
+    for (auto gate : {GATING_FUNC_SOFTMAX, GATING_FUNC_SIGMOID, GATING_FUNC_SOFTMAX_WEIGHT, GATING_FUNC_SQRT_SOFTPLUS}) {
         for (bool with_norm : {false, true}) {
             for (bool bias_probs : {false, true}) {
                 for (float scale_w : {0.0f, 2.0f}) {
@@ -9596,6 +9598,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                     test_cases.emplace_back(new test_topk_moe({128, 1, 1, 1}, 128, with_norm, bias_probs, gate, scale_w));
                     test_cases.emplace_back(new test_topk_moe({129, 1, 1, 1}, 128, with_norm, bias_probs, gate, scale_w));
                     test_cases.emplace_back(new test_topk_moe({160, 4, 1, 1}, 160, with_norm, bias_probs, gate, scale_w));
+                    test_cases.emplace_back(new test_topk_moe({256, 22, 1, 1}, 6, with_norm, bias_probs, gate, scale_w)); // Used by DeepSeek-V4
                     test_cases.emplace_back(new test_topk_moe({288, 22, 1, 1}, 8, with_norm, bias_probs, gate, scale_w)); // Used by StepFun 3.7
                 }
             }

tests/test-chat-auto-parser.cpp

@@ -57,6 +57,15 @@ static void test_seed_oss_tool_with_reasoning(testing & t);
 static void test_nemotron_analysis(testing & t);
 static void test_nemotron_reasoning_detection(testing & t);
 static void test_nemotron_tool_format(testing & t);
+static void test_laguna_analysis(testing & t);
+static void test_laguna_reasoning_detection(testing & t);
+static void test_laguna_tool_format(testing & t);
+static void test_laguna_s_analysis(testing & t);
+static void test_laguna_s_reasoning_detection(testing & t);
+static void test_laguna_s_tool_format(testing & t);
+static void test_laguna_xs2_analysis(testing & t);
+static void test_laguna_xs2_reasoning_detection(testing & t);
+static void test_laguna_xs2_tool_format(testing & t);
 
 // CohereForAI template analysis tests
 static void test_cohere_reasoning_detection(testing & t);
@@ -101,6 +110,9 @@ int main(int argc, char * argv[]) {
     t.test("seed_oss_diffs", test_seed_oss_tool_analysis);
     t.test("cohere", test_cohere_analysis);
     t.test("nemotron", test_nemotron_analysis);
+    t.test("laguna", test_laguna_analysis);
+    t.test("laguna-s", test_laguna_s_analysis);
+    t.test("laguna-xs2", test_laguna_xs2_analysis);
     t.test("smollm3", test_smollm3_analysis);
     t.test("standard_json_tools", test_standard_json_tools_formats);
     t.test("normalize_quotes_to_json", test_normalize_quotes_to_json);
@@ -1378,6 +1390,94 @@ static void test_nemotron_tool_format(testing & t) {
     t.assert_true("should support tools", analysis.jinja_caps.supports_tools);
 }
 
+// ============================================================================
+// Laguna Template Analysis Tests
+// ============================================================================
+static common_chat_template load_laguna_template(testing & t) {
+    return load_template(t, "models/templates/poolside-Laguna-XS-2.1.jinja");
+}
+
+static void test_laguna_reasoning_detection(testing & t) {
+    common_chat_template tmpl = load_laguna_template(t);
+    struct autoparser analysis;
+    analysis.analyze_template(tmpl);
+    // Laguna's template renders reasoning delimiters with formatting whitespace
+    // ("<think>\n") that the model does not emit; the Laguna patch trims them.
+    t.assert_equal("reasoning_start should be '<think>'", "<think>", analysis.reasoning.start);
+    t.assert_equal("reasoning_end should be '</think>'", "</think>", analysis.reasoning.end);
+    t.assert_equal("reasoning should be TAG_BASED", reasoning_mode::TAG_BASED, analysis.reasoning.mode);
+}
+
+static void test_laguna_tool_format(testing & t) {
+    common_chat_template tmpl = load_laguna_template(t);
+    struct autoparser analysis;
+    analysis.analyze_template(tmpl);
+    t.assert_equal("arg_value_suffix should be '</arg_value>'", "</arg_value>", analysis.tools.arguments.value_suffix);
+}
+
+static void test_laguna_stop_string(testing & t) {
+    // The </assistant> turn terminator can be emitted as ordinary text tokens
+    // (not the single eot token), so it must also be a literal stop string.
+    common_chat_template tmpl = load_laguna_template(t);
+    struct autoparser analysis;
+    analysis.analyze_template(tmpl);
+    bool has_stop = false;
+    for (const auto & stop : analysis.additional_stops) {
+        if (stop == "</assistant>") { has_stop = true; break; }
+    }
+    t.assert_true("Laguna additional_stops contains </assistant>", has_stop);
+}
+
+static void test_laguna_analysis(testing & t) {
+    t.test("Laguna reasoning detection", test_laguna_reasoning_detection);
+    t.test("Laguna tool format", test_laguna_tool_format);
+    t.test("Laguna stop string", test_laguna_stop_string);
+}
+
+static common_chat_template load_laguna_s_template(testing & t) {
+    return load_template(t, "models/templates/poolside-Laguna-S-2.1.jinja");
+}
+static void test_laguna_s_reasoning_detection(testing & t) {
+    common_chat_template tmpl = load_laguna_s_template(t);
+    struct autoparser analysis;
+    analysis.analyze_template(tmpl);
+    t.assert_equal("Laguna-S(v8) reasoning_start should be '<think>'", "<think>", analysis.reasoning.start);
+    t.assert_equal("Laguna-S(v8) reasoning_end should be '</think>'", "</think>", analysis.reasoning.end);
+    t.assert_equal("Laguna-S(v8) reasoning should be TAG_BASED", reasoning_mode::TAG_BASED, analysis.reasoning.mode);
+}
+static void test_laguna_s_tool_format(testing & t) {
+    common_chat_template tmpl = load_laguna_s_template(t);
+    struct autoparser analysis;
+    analysis.analyze_template(tmpl);
+    t.assert_equal("Laguna-S(v8) arg_value_suffix should be '</arg_value>'", "</arg_value>", analysis.tools.arguments.value_suffix);
+}
+static void test_laguna_s_analysis(testing & t) {
+    t.test("Laguna-S(v8) reasoning detection", test_laguna_s_reasoning_detection);
+    t.test("Laguna-S(v8) tool format", test_laguna_s_tool_format);
+}
+
+static common_chat_template load_laguna_xs2_template(testing & t) {
+    return load_template(t, "models/templates/poolside-Laguna-XS.2.jinja");
+}
+static void test_laguna_xs2_reasoning_detection(testing & t) {
+    common_chat_template tmpl = load_laguna_xs2_template(t);
+    struct autoparser analysis;
+    analysis.analyze_template(tmpl);
+    t.assert_equal("Laguna-XS.2(v5) reasoning_start should be '<think>'", "<think>", analysis.reasoning.start);
+    t.assert_equal("Laguna-XS.2(v5) reasoning_end should be '</think>'", "</think>", analysis.reasoning.end);
+    t.assert_equal("Laguna-XS.2(v5) reasoning should be TAG_BASED", reasoning_mode::TAG_BASED, analysis.reasoning.mode);
+}
+static void test_laguna_xs2_tool_format(testing & t) {
+    common_chat_template tmpl = load_laguna_xs2_template(t);
+    struct autoparser analysis;
+    analysis.analyze_template(tmpl);
+    t.assert_equal("Laguna-XS.2(v5) arg_value_suffix should be '</arg_value>'", "</arg_value>", analysis.tools.arguments.value_suffix);
+}
+static void test_laguna_xs2_analysis(testing & t) {
+    t.test("Laguna-XS.2(v5) reasoning detection", test_laguna_xs2_reasoning_detection);
+    t.test("Laguna-XS.2(v5) tool format", test_laguna_xs2_tool_format);
+}
+
 static common_chat_template load_cohere_template(testing & t) {
     return load_template(t, "models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja");
 }

tests/test-chat.cpp

@@ -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();

tests/test-llama-archs.cpp

@@ -362,6 +362,7 @@ static bool moe_mandatory(const llm_arch arch) {
         case LLM_ARCH_STEP35:
         case LLM_ARCH_MISTRAL4:
         case LLM_ARCH_MELLUM:
+        case LLM_ARCH_LAGUNA:
             return true;
         default:
             return false;

tools/mtmd/models/qwen3vl.cpp

@@ -37,7 +37,7 @@ ggml_cgraph * clip_graph_qwen3vl::build() {
     }
 
     // calculate absolute position embedding and apply
-    ggml_tensor * learned_pos_embd = resize_position_embeddings();
+    ggml_tensor * learned_pos_embd = resize_position_embeddings(GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS);
     learned_pos_embd = ggml_cont_4d(
         ctx0, learned_pos_embd,
         n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);

tools/mtmd/mtmd-helper.cpp

@@ -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;
 }
 

tools/server/server-context.cpp

@@ -1152,6 +1152,11 @@ private:
             return false;
         }
 
+        if (ctx_tgt == nullptr) {
+            SRV_ERR("failed to create_context with model '%s'\n", params_base.model.path.c_str());
+            return false;
+        }
+
         vocab = llama_model_get_vocab(model_tgt);
 
         n_ctx = llama_n_ctx(ctx_tgt);

tools/server/server-stream.cpp

@@ -632,6 +632,13 @@ void server_res_spipe::on_complete() {
     if (!spipe || next_finished) {
         return;
     }
+    // an empty next_orig means set_next() never ran: the request failed before streaming
+    // started, typically a params validation throw. evict the session installed by set_req()
+    // so the failed request leaves nothing behind for discovery or replay
+    if (!next_orig) {
+        g_stream_sessions.evict(server_stream_conv_id_from_headers(req->headers));
+        return;
+    }
     std::string chunk;
     while (!spipe->is_cancelled()) {
         chunk.clear();

tools/ui/.gitignore

@@ -36,3 +36,7 @@ static/favicon*
 *storybook.log
 storybook-static
 *.code-workspace
+
+# Vitest browser mode failure artifacts
+.vitest-attachments/
+tests/**/__screenshots__/

tools/ui/.prettierignore

@@ -16,3 +16,6 @@ build/
 /build/
 /.svelte-kit/
 test-results
+
+# Vendored third party sources, kept byte identical to upstream
+src/lib/vendors/

tools/ui/eslint.config.js

@@ -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']

tools/ui/scripts/vite-plugin-nerdamer.ts

@@ -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)};`;
+		}
+	};
+}

tools/ui/src/lib/constants/sandbox.ts

@@ -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);

tools/ui/src/lib/constants/settings-keys.ts

@@ -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'

tools/ui/src/lib/constants/settings-registry.ts

@@ -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',

tools/ui/src/lib/services/index.ts

@@ -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';

tools/ui/src/lib/services/sandbox-harness.ts

@@ -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>`;
+}

tools/ui/src/lib/services/sandbox-worker.js

@@ -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);

tools/ui/src/lib/services/sandbox.service.ts

@@ -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;
 

tools/ui/src/lib/stores/chat.svelte.ts

@@ -2428,7 +2428,8 @@ class ChatStore {
 
 		if (currentConfig.samplers) apiOptions.samplers = currentConfig.samplers;
 
-		apiOptions.backend_sampling = currentConfig.backend_sampling;
+		if (hasValue(currentConfig.backend_sampling))
+			apiOptions.backend_sampling = currentConfig.backend_sampling;
 
 		if (currentConfig.customJson) apiOptions.custom = currentConfig.customJson;
 

tools/ui/src/lib/stores/tools.svelte.ts

@@ -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[] {

tools/ui/src/lib/vendors/big-integer/LICENSE

@@ -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>

tools/ui/src/lib/vendors/decimal.js/LICENCE.md

@@ -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.
+

tools/ui/src/lib/vendors/nerdamer-prime/Extra.js

@@ -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;
+}

tools/ui/src/lib/vendors/nerdamer-prime/LICENSE

@@ -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.

tools/ui/src/lib/vendors/nerdamer-prime/all.js

@@ -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;

tools/ui/src/lib/vendors/nerdamer-prime/constants.js

@@ -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',
+    '76631772943534985713873427262830314617912556928476573358548256872141516989538374761909611879922349479420014771499018155447198112155515453671128814488139633810493264352294560043912066253026059140653027326566801398784/36852092933388988649396042883218509607503204211148493545892849595498822817623842579026942621098851631842754395231561679671400197056377380063233740202370686144673585955581403046886083948450136247134308381940165804875',
+    '3159076083816399509754948610929467278257473888282947311280653574634802580912280940686954763313882823327077171624015737719617373932318151594325834524000275847475866299387913048/1437757485694188822758304467756419845842037623148461107362957994816554782989250555362514354661961482939226272309026092009962414616417412938087494467254146002233028411865234375',
+    '22266067259907364984531611601870291368272674573653403965630628996687370994139884833897773468149149664829922302484782423514167405397665098388400450149078982462318781750661005833037235183394221496186539779712428265837926417581952/9670030144664428565128962309657100138096047028794689249320859276197340398920725569428532293373676415359965773460364494998334259893079003125373872108770534788283842907318071170285038777091588292539102269617376180390982915567375',
+    '14604654564989239958569331443385369522850975185358647132770022716433280072271007767111036877803328768910274400515590151934676819262085211828028638417329558229123989556376108454497813055/6090614019162516693013973409650613208227889078878781039105047015752493519149314227721984436973374032279421344818329285207124280297611253861173835238379831004010748379874393292231671808',
+    '1901241885407696031217292877862925220917660047127261026827869027159993239567933534052663335498281439239753018507182016153657409777749792228538380379703411298411623469292891476969894084838876001545818141543890273256985768690847587711270930688/765116019778838839812655402103512685695769161212360553099732689795578904762091216998790589926057819838537805856579109910198553330075924857419395160755642371550113347465300208422126945265887065434116781678702741657275181694851670325469434625',
+    '139459806786604751793737926146840623607010208216289543036026206208962059593900745886202214788747453279179283344350478734275973878932538430194363355795823581315329311220701640235653288975569812161436/54371368534412517053056101353618694718215711767266376573138772968257303578467926450212293233332401067673270853953399269852376592855992724934941173346260129257754416412476202526978443681584633116375',
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+    '13240077436443988749179508462267267187169441948722358165090554769250505713747934643200804819418670147225695324432684266924694524337920816452346599774452681831320005286326986675907899608537972384924882996757503264622991355949039882526389342174307168805166215838138277557052303430492669193939212362638263582899713198716541723383138016564027766560215944409353427176135895982596327685665844815618402881202645610620284792793420780517248/2544223084468158291883698813309541801455311468982232546872485444308211415529998472787377800559884210837213042932180479090277285630234238711851480232520137856848809986631784843528381778520727465146661792797924458540957133423665746229799675650290296217658444899605236550972043549278128087645211909479009099766619355677984218929672461506691980442071860591767266913041147587815452007726513853820116629482732060593116624596368806566625',
+    '1953999166296955830935495158735359200362904181792947794529339487489730042568305997099959302322956898299616194932283060554261566410988618045107398092345476532371402134206635235570281738377188438407703089325315446371127042537576093536896282955524842632708645655481028161471313608974238110718242273935956977555610147714316158486553633871312187084618154014921190595222799283957140353/375191165084882521037046014569185165885459082629136124177286500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000',
+];
+
+if (typeof module !== 'undefined') {
+    module.exports = {
+        PRIMES,
+        PRIMES_SET,
+        LONG_PI,
+        LONG_E,
+        BIG_LOG_CACHE,
+    };
+}

tools/ui/src/lib/vendors/package.json

@@ -0,0 +1,3 @@
+{
+	"type": "commonjs"
+}

tools/ui/src/virtual-nerdamer.d.ts

@@ -0,0 +1,4 @@
+declare module 'virtual:nerdamer' {
+	const code: string;
+	export default code;
+}

tools/ui/tests/client/sandbox.service.svelte.test.ts

@@ -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');
+	});
+});

tools/ui/tsconfig.json

@@ -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
 	//

tools/ui/vite.config.ts

@@ -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()
 	],