common: migrate the deprecated --mmap/--no-mmap to --load-mode (#26934)

Replace the deprecated --mmap, --no-mmap, --mlock, and --direct-io flags with
the unified --load-mode argument across scripts, examples, and documentation.
Internal warning message and env var docs updated accordingly.

Signed-off-by: Fathi Boudra <fathi.boudra@linaro.org>

common/arg.cpp

@@ -3646,6 +3646,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING"));
+    add_opt(common_arg(
+        {"--reasoning-effort"}, "LEVEL",
+        "reasoning effort level given to the chat template: 'default' to keep the template default,\n"
+        "or a level such as 'minimal', 'low', 'medium', 'high', 'xhigh' or 'max' (default: default)",
+        [](common_params & params, const std::string & value) {
+            if (value == "default") {
+                params.default_template_kwargs.erase("reasoning_effort");
+            } else {
+                params.default_template_kwargs["reasoning_effort"] = json(value).dump();
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING_EFFORT"));
     add_opt(common_arg(
         {"--reasoning-budget"}, "N",
         "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)",
@@ -4065,6 +4077,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--spec-draft-n-max"}, "N",
         string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.draft.n_max),
         [](common_params & params, int value) {
+            if (value < 0) {
+                throw std::invalid_argument("invalid value");
+            }
             params.speculative.draft.n_max = value;
         }
     ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_MAX"));

common/chat.cpp

@@ -920,6 +920,10 @@ static std::string common_chat_template_direct_apply_impl(
         bool enabled = inp["preserve_reasoning"].get<bool>();
         jinja::caps_apply_preserve_reasoning(ctx, enabled);
     }
+    if (inp.contains("reasoning_effort") && inp["reasoning_effort"].is_string() && !inp["reasoning_effort"].empty()) {
+        std::string reasoning_effort = inp["reasoning_effort"].get<std::string>();
+        jinja::caps_apply_reasoning_effort(ctx, reasoning_effort);
+    }
 
     jinja::global_from_json(ctx, inp, inputs.mark_input);
 

common/jinja/caps.cpp

@@ -17,7 +17,7 @@ namespace jinja {
 
 using caps_json_fn = std::function<json()>;
 using caps_ctx_fn = std::function<void(context &)>;
-using caps_analyze_fn = std::function<void(bool, value &, value &, const std::string &)>;
+using caps_analyze_fn = std::function<void(context &, bool, value &, value &, const std::string &)>;
 
 void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) {
     ctx.set_val("preserve_thinking",         mk_val<value_bool>(enabled));
@@ -26,6 +26,12 @@ void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) {
     ctx.set_val("drop_thinking",            mk_val<value_bool>(!enabled));
 }
 
+void caps_apply_reasoning_effort(jinja::context & ctx, const std::string & effort) {
+    value var = mk_val<value_string>(effort); // bind to the same value for stats
+    ctx.set_val("reasoning_effort",   var);
+    ctx.set_val("reasoning_strength", var);
+}
+
 static void caps_try_execute(jinja::program & prog,
                              const caps_json_fn & messages_fn,
                              const caps_ctx_fn & ctx_fn,
@@ -62,7 +68,7 @@ static void caps_try_execute(jinja::program & prog,
         // ignore exceptions during capability analysis
     }
 
-    analyze_fn(success, messages, tools, result);
+    analyze_fn(ctx, success, messages, tools, result);
 }
 
 // for debugging only
@@ -87,6 +93,7 @@ std::map<std::string, bool> caps::to_map() const {
         {"supports_parallel_tool_calls", supports_parallel_tool_calls},
         {"supports_system_role", supports_system_role},
         {"supports_preserve_reasoning", supports_preserve_reasoning},
+        {"supports_reasoning_effort", supports_reasoning_effort},
         {"supports_object_arguments", supports_object_arguments},
     };
 }
@@ -124,7 +131,7 @@ caps caps_get(jinja::program & prog) {
         },
         nullptr, // ctx_fn
         nullptr, // tools_fn
-        [&](bool success, value & messages, value &, const std::string &) {
+        [&](context &, bool success, value & messages, value &, const std::string &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (has_op(content, "selectattr") || has_op(content, "array_access")) {
@@ -158,7 +165,7 @@ caps caps_get(jinja::program & prog) {
         },
         nullptr, // ctx_fn
         nullptr, // tools_fn
-        [&](bool, value & messages, value &, const std::string &) {
+        [&](context &, bool, value & messages, value &, const std::string &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (!content->stats.used) {
@@ -234,7 +241,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&](bool success, value & messages, value & tools, const std::string &) {
+        [&](context &, bool success, value & messages, value & tools, const std::string &) {
             if (!success) {
                 return; // Nothing can be inferred
             }
@@ -327,7 +334,7 @@ caps caps_get(jinja::program & prog) {
                     },
                 });
             },
-            [&](bool success, value & messages, value & tools, const std::string &) {
+            [&](context &, bool success, value & messages, value & tools, const std::string &) {
                 if (!success) {
                     result.supports_tool_calls = false;
                     result.supports_tools = false;
@@ -429,7 +436,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&](bool success, value & messages, value &, const std::string &) {
+        [&](context &, bool success, value & messages, value &, const std::string &) {
             if (!success) {
                 result.supports_parallel_tool_calls = false;
                 return;
@@ -486,7 +493,7 @@ caps caps_get(jinja::program & prog) {
             caps_apply_preserve_reasoning(ctx, true);
         },
         nullptr, // tools_fn
-        [&](bool, value &, value &, const std::string & output) {
+        [&](context &, bool, value &, value &, const std::string & output) {
             // note: we cannot use stats here because the reasoning_content may be used for "if" condition test, but not actually outputted in the final result
             if (output.find(reasoning_placeholder) != std::string::npos) {
                 result.supports_preserve_reasoning = true;
@@ -494,6 +501,32 @@ caps caps_get(jinja::program & prog) {
         }
     );
 
+    JJ_DEBUG("%s\n", ">>> Running capability check: reasoning effort");
+
+    // case: reasoning effort level
+    caps_try_execute(
+        prog,
+        [&]() {
+            // messages
+            return json::array({
+                {
+                    {"role", "user"},
+                    {"content", "User message"}
+                },
+            });
+        },
+        [&](context & ctx) {
+            ctx.set_val("enable_thinking", mk_val<value_bool>(true));
+            caps_apply_reasoning_effort(ctx, "low");
+        },
+        nullptr, // tools_fn
+        [&](context & ctx, bool, value &, value &, const std::string &) {
+            value effort = ctx.get_val("reasoning_effort");
+            caps_print_stats(effort, "reasoning_effort");
+            result.supports_reasoning_effort = effort->stats.used;
+        }
+    );
+
     JJ_DEBUG("%s\n", result.to_string().c_str());
 
     return result;

common/jinja/caps.h

@@ -16,6 +16,9 @@ struct caps {
     // supports preserve reasoning trace in the full history, not just the last assistant message
     bool supports_preserve_reasoning = false;
 
+    // supports reasoning effort levels
+    bool supports_reasoning_effort = false;
+
     // one of the 2 content capabilities must be true
     bool supports_string_content = true;
     bool supports_typed_content = false;
@@ -32,5 +35,6 @@ struct caps {
 caps caps_get(jinja::program & prog);
 
 void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled);
+void caps_apply_reasoning_effort(jinja::context & ctx, const std::string & effort);
 
 } // namespace jinja

common/jinja/runtime.cpp

@@ -263,7 +263,7 @@ value binary_expression::execute_impl(context & ctx) {
             return res;
         }
         for (int64_t i = 0; i < repeat; ++i) {
-            res->val_str = res->val_str.append(str);
+            res->val_str.append(str);
         }
         return res;
     }

common/jinja/runtime.h

@@ -763,14 +763,22 @@ struct runtime {
         gather_string_parts_recursive(val, parts);
         // join consecutive parts with the same type
         auto & p = parts->val_str.parts;
-        for (size_t i = 1; i < p.size(); ) {
-            if (p[i].is_input == p[i - 1].is_input) {
-                p[i - 1].val += p[i].val;
-                p.erase(p.begin() + i);
+        if (p.empty()) {
+            return parts;
+        }
+        size_t w = 0;
+        for (size_t r = 1; r < p.size(); r++) {
+            if (p[w].is_input == p[r].is_input) {
+                p[w].val += p[r].val;
             } else {
-                i++;
+                w++;
+                if (w != r) {
+                    // the guard is needed, self-move leaves the string in an unspecified state
+                    p[w] = std::move(p[r]);
+                }
             }
         }
+        p.resize(w + 1);
         return parts;
     }
 

common/jinja/string.cpp

@@ -103,7 +103,7 @@ void string::mark_input_based_on(const string & other) {
     }
 }
 
-string string::append(const string & other) {
+string & string::append(const string & other) {
     for (const auto & part : other.parts) {
         parts.push_back(part);
     }

common/jinja/string.h

@@ -47,7 +47,7 @@ struct string {
     // mark this string as input if other has ALL parts as input
     void mark_input_based_on(const string & other);
 
-    string append(const string & other);
+    string & append(const string & other);
 
     // in-place transformations
 

conversion/__init__.py

@@ -161,6 +161,8 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "MiniCPM3ForCausalLM": "minicpm",
     "MiniCPMForCausalLM": "minicpm",
     "MiniCPMV4_6ForConditionalGeneration": "minicpm",
+    "MiniMaxText01ForCausalLM": "minimax",
+    "MiniMaxM1ForCausalLM": "minimax",
     "MiniMaxM2ForCausalLM": "minimax",
     "MiniMaxM3SparseForCausalLM": "minimax",
     "MiniMaxM3SparseForConditionalGeneration": "minimax",

conversion/minimax.py

@@ -1,13 +1,121 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import Iterable, Sequence, TYPE_CHECKING
 
 import torch
 
 if TYPE_CHECKING:
     from torch import Tensor
 
-from .base import ModelBase, TextModel, MmprojModel, gguf
+from .base import ModelBase, TextModel, MmprojModel, gguf, logger
+
+
+@ModelBase.register("MiniMaxText01ForCausalLM")
+@ModelBase.register("MiniMaxM1ForCausalLM")
+class MiniMaxText01Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.MINIMAX01
+
+    def _get_suppress_tokens(self) -> Sequence[int] | None:
+        import json
+        from transformers import AutoTokenizer
+        from .base import LazyTorchTensor
+
+        # check added tokens embeddings in embeddings tensor for zero-valued embeddings
+        # they get in the way of the token sampling process and must be suppressed
+
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        tokenizer_vocab_size = tokenizer.vocab_size
+
+        with open(self.dir_model / "model.safetensors.index.json", "r", encoding="utf-8") as f:
+            weight_map = json.load(f)["weight_map"]
+
+        embeddings_tensor_name = "model.embed_tokens.weight"
+        embeddings_shard_name = weight_map[embeddings_tensor_name]
+        with gguf.utility.SafetensorsLocal(self.dir_model / embeddings_shard_name) as model_shard:
+            embeddings_data = model_shard[embeddings_tensor_name]
+
+        embeddings_weights_dtype = LazyTorchTensor._dtype_str_map[embeddings_data.dtype]
+        embeddings_weights = torch.from_numpy(embeddings_data.mmap_bytes()).view(embeddings_weights_dtype).reshape(embeddings_data.shape)
+        embeddings_vocab_size = embeddings_weights.shape[0]
+
+        embeddings_added_tokens = embeddings_weights[tokenizer_vocab_size:embeddings_vocab_size]
+        embeddings_zero_rows = torch.all(embeddings_added_tokens == 0, dim=1)
+        tokens_zero_embeddings_ids = (torch.nonzero(embeddings_zero_rows, as_tuple=False).flatten() + tokenizer_vocab_size).tolist()
+
+        return tokens_zero_embeddings_ids
+
+    def set_vocab(self) -> None:
+        from pathlib import Path
+
+        self._set_vocab_gpt2()
+
+        for tmpl_file in [
+            self.dir_model / "chat_template.jinja",
+            Path(__file__).parent.parent / "models" / "templates" / "MiniMax-M1.jinja"
+        ]:
+            if tmpl_file.is_file():
+                self.gguf_writer.add_chat_template(tmpl_file.read_text(encoding="utf-8"))
+                logger.info(f"Chat template overridden with {tmpl_file}.")
+                break
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        suppress_tokens = self._get_suppress_tokens()
+        if suppress_tokens:
+            logger.info(f"Suppressing tokens with zero embeddings {suppress_tokens}")
+            self.gguf_writer.add_suppress_tokens(suppress_tokens)
+
+        layernorm_full_attention_alpha = self.hparams["layernorm_full_attention_alpha"]
+        layernorm_full_attention_beta = self.hparams["layernorm_full_attention_beta"]
+        layernorm_linear_attention_alpha = self.hparams["layernorm_linear_attention_alpha"]
+        layernorm_linear_attention_beta = self.hparams["layernorm_linear_attention_beta"]
+        layernorm_mlp_alpha = self.hparams["layernorm_mlp_alpha"]
+        layernorm_mlp_beta = self.hparams["layernorm_mlp_beta"]
+        assert layernorm_full_attention_alpha == layernorm_linear_attention_alpha == layernorm_mlp_alpha
+        assert layernorm_full_attention_beta == layernorm_linear_attention_beta == layernorm_mlp_beta == 1.0
+        # we do not store the layernorm betas as they are all 1.0
+        # layernorm alphas are stored as single residual_scale hparam
+        self.gguf_writer.add_residual_scale(layernorm_full_attention_alpha)
+
+        self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.hparams["num_local_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
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for wid in ["w1", "w2", "w3"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    yield from super().modify_tensors(data_torch, new_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
 
 
 @ModelBase.register("MiniMaxM2ForCausalLM")

docs/backend/SYCL.md

@@ -428,13 +428,13 @@ Examples:
 - Use device 0:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0 --load-mode auto
 ```
 
 - Use multiple devices:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer --mmap
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer --load-mode auto
 ```
 
 *Notes:*
@@ -741,13 +741,13 @@ Examples:
 - Use device 0:
 
 ```
-build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap
+build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0 --load-mode auto
 ```
 
 - Use multiple devices:
 
 ```
-build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer --mmap
+build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer --load-mode auto
 ```
 
 

docs/backend/snapdragon/developer.md

@@ -53,7 +53,7 @@ M=gpt-oss-20b-Q4_0.gguf NDEV=4 D=HTP0,HTP1,HTP2,HTP3 P=surfing.txt scripts/snapd
 ...
 LD_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib
 ADSP_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib
-GGML_HEXAGON_NDEV=4 ./bin/llama-cli --no-mmap -m /data/local/tmp/llama.cpp/../gguf/gpt-oss-20b-Q4_0.gguf
+GGML_HEXAGON_NDEV=4 ./bin/llama-cli --load-mode none -m /data/local/tmp/llama.cpp/../gguf/gpt-oss-20b-Q4_0.gguf
       -t 4 --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on -ngl 99 --device HTP0,HTP1,HTP2,HTP3 -no-cnv -f surfing.txt
 ...
 llama_model_loader: - type  f32:  289 tensors

examples/sycl/run-llama2.sh

@@ -18,7 +18,7 @@ CONTEXT=4096
 #support malloc device memory more than 4GB.
 export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
 
-LOAD_MODE='--mmap'
+LOAD_MODE='--load-mode auto'
 if [ $# -gt 0 ]; then
     GGML_SYCL_DEVICE=$1
     echo "use $GGML_SYCL_DEVICE as main GPU"

examples/sycl/start-svr.sh

@@ -124,7 +124,7 @@ else
     GPUS_SETTING="-sm ${SPLIT_MODE}"
 fi
 
-echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap --host 0.0.0.0 --port 8000"
-ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap --host 0.0.0.0 --port 8000
+echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --load-mode auto --host 0.0.0.0 --port 8000"
+ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --load-mode auto --host 0.0.0.0 --port 8000
 
 

examples/sycl/test.sh

@@ -133,6 +133,6 @@ else
     GPUS_SETTING="-sm ${SPLIT_MODE}"
  fi
 
-echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap "
-ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap
+echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --load-mode auto "
+ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --load-mode auto
 

examples/sycl/win-run-llama2.bat

@@ -7,5 +7,5 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 
 :: support malloc device memory more than 4GB.
 set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
-set LOAD_MODE="--mmap"
+set LOAD_MODE="--load-mode auto"
 .\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0 %LOAD_MODE%

examples/sycl/win-start-svr.bat

@@ -188,9 +188,9 @@ if not "%GGML_SYCL_DEVICE%"=="-1" (
   set "GPUS_SETTING=-sm %SPLIT_MODE%"
 )
 
-echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --mmap --host 0.0.0.0 --port 8000
+echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --load-mode auto --host 0.0.0.0 --port 8000
 set "ZES_ENABLE_SYSMAN=1"
-%BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --mmap --host 0.0.0.0 --port 8000
+%BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --load-mode auto --host 0.0.0.0 --port 8000
 
 endlocal
 

examples/sycl/win-test.bat

@@ -211,9 +211,9 @@ else (
   set "GPUS_SETTING=-sm %SPLIT_MODE%"
 )
 
-echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m %MODEL_FILE% -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --mmap
+echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m %MODEL_FILE% -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --load-mode auto
 set "ZES_ENABLE_SYSMAN=1"
-%BIN_FILE% -m "%MODEL_FILE%" -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --mmap
+%BIN_FILE% -m "%MODEL_FILE%" -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --load-mode auto
 
 endlocal
 

ggml/CMakeLists.txt

@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)
 
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 19)
+set(GGML_VERSION_MINOR 20)
 set(GGML_VERSION_PATCH 0)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -2066,7 +2066,7 @@ struct ggml_vk_garbage_collector {
 static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_context subctx);
 static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested = nullptr);
 static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx);
-static bool ggml_vk_intel_windows_driver_equals_or_newer_than(uint32_t driver_version, uint32_t threshold_major, uint32_t threshold_minor);
+static bool ggml_vk_intel_windows_driver_in_range(uint32_t driver_version, uint32_t lower_major, uint32_t lower_minor, uint32_t upper_major, uint32_t upper_minor);
 
 static bool vk_memory_logger_enabled = false;
 
@@ -5742,10 +5742,9 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
     ggml_vk_create_pipeline(device, device->pipeline_argmax_f32, "argmax_f32", argmax_f32_len, argmax_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    // Intel Windows driver older than 32.0.101.8860 will crash when using fwht kernels on Xe2+ GPUS so we gate that here
+    // Intel Windows driver in range [32.0.101.8509, 32.0.101.8860) will crash when using fwht kernels so we gate that here
     const bool can_use_fwht = device->driver_id != vk::DriverId::eIntelProprietaryWindows ||
-        device->architecture != vk_device_architecture::INTEL_XE2 ||
-        (device->architecture == vk_device_architecture::INTEL_XE2 && ggml_vk_intel_windows_driver_equals_or_newer_than(device->properties.driverVersion, 101, 8860));
+        !ggml_vk_intel_windows_driver_in_range(device->properties.driverVersion, 101, 8509, 101, 8860);
     if (can_use_fwht && device->subgroup_basic && device->subgroup_shuffle) {
         int idx = 0;
         for (uint32_t n : {64, 128, 256, 512}) {
@@ -18871,17 +18870,23 @@ static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev)
     }
 }
 
-static bool ggml_vk_intel_windows_driver_equals_or_newer_than(uint32_t driver_version, uint32_t threshold_major, uint32_t threshold_minor) {
+// checks whether lower <= driver_version < upper, with each bound given as xxx.yyyy
+static bool ggml_vk_intel_windows_driver_in_range(uint32_t driver_version, uint32_t lower_major, uint32_t lower_minor, uint32_t upper_major, uint32_t upper_minor) {
 #if defined(_WIN32)
     // Intel Windows encodes xxx.yyyy as [31:14].[13:0].
     const uint32_t major = driver_version >> 14;
     const uint32_t minor = driver_version & 0x3fff;
 
-    return major > threshold_major || (major == threshold_major && minor >= threshold_minor);
+    const bool ge_lower = major > lower_major || (major == lower_major && minor >= lower_minor);
+    const bool lt_upper = major < upper_major || (major == upper_major && minor < upper_minor);
+
+    return ge_lower && lt_upper;
 #else
     GGML_UNUSED(driver_version);
-    GGML_UNUSED(threshold_major);
-    GGML_UNUSED(threshold_minor);
+    GGML_UNUSED(lower_major);
+    GGML_UNUSED(lower_minor);
+    GGML_UNUSED(upper_major);
+    GGML_UNUSED(upper_minor);
     return true;
 #endif
 }

gguf-py/gguf/constants.py

@@ -565,6 +565,7 @@ class MODEL_ARCH(IntEnum):
     GROVEMOE         = auto()
     APERTUS          = auto()
     COGVLM           = auto()
+    MINIMAX01        = auto()
     MINIMAXM2        = auto()
     MINIMAXM3        = auto()
     RND1             = auto()
@@ -1271,6 +1272,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.SEED_OSS:         "seed_oss",
     MODEL_ARCH.GROVEMOE:         "grovemoe",
     MODEL_ARCH.APERTUS:          "apertus",
+    MODEL_ARCH.MINIMAX01:        "minimax-01",
     MODEL_ARCH.MINIMAXM2:        "minimax-m2",
     MODEL_ARCH.MINIMAXM3:        "minimax-m3",
     MODEL_ARCH.COGVLM:           "cogvlm",
@@ -4592,6 +4594,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_CHEXP,
         MODEL_TENSOR.FFN_UP_CHEXP,
     ],
+    MODEL_ARCH.MINIMAX01: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_NORM_2,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
     MODEL_ARCH.MINIMAXM2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

gguf-py/gguf/tensor_mapping.py

@@ -225,6 +225,7 @@ class TensorNameMap:
             "rwkv.blocks.{bid}.ln2",                        # rwkv6
             "model.layers.{bid}.ln2",                       # rwkv7
             "model.layers.{bid}.post_attention_layernorm",  # cogvlm
+            "model.layers.{bid}.self_attn.norm",            # minimax-01
         ),
 
         # Attention query-key-value
@@ -321,7 +322,7 @@ class TensorNameMap:
             "h.{bid}.self_attention.dense",                                 # bloom
             "model.layers.{bid}.self_attn.o_proj",                          # llama-hf nemotron olmoe olmo2 phimoe
             "layers.{bid}.self_attn.o_proj",                                # embeddinggemma
-            "model.layers.{bid}.self_attn.out_proj",                        # lfm2
+            "model.layers.{bid}.self_attn.out_proj",                        # lfm2 minimax-01
             "model.layers.{bid}.self_attn.linear_attn",                     # deci
             "layers.{bid}.attention.wo",                                    # llama-pth
             "encoder.layer.{bid}.attention.output.dense",                   # bert
@@ -385,6 +386,7 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.gate_proj", # afmoe muse-glimmer
             "model.layers.{bid}.linear_attn.in_proj_z",  # qwen3.5
             "model.layers.{bid}.self_attn.g_proj",    # step3.5 head-wise attention gate
+            "model.layers.{bid}.self_attn.output_gate",  # minimax-01
         ),
 
         # Feed-forward norm

models/templates/MiniMax-M1.jinja

@@ -0,0 +1,91 @@
+{{ '<begin_of_document>' -}}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- Extract system message #}
+{% set ns = namespace(system_prompt='') -%}
+{%- if messages[0]['role'] == 'system' %}
+    {%- if messages[0]['content'] is string %}
+        {%- set ns.system_prompt = messages[0]['content']|trim %}
+    {%- else %}
+        {%- set ns.system_prompt = messages[0]['content'][0]['text']|trim %}
+    {%- endif %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- if tools is not none %}
+        {%- set ns.system_prompt = "You are a helpful assistant created by Minimax based on MiniMax-M1 model." %}
+    {%- else %}
+        {%- set ns.system_prompt = "You are a helpful assistant created by Minimax based on MiniMax-M1 model." %}
+    {%- endif %}
+{%- endif %}
+
+{#- System message #}
+{%- if ns.system_prompt != '' %}
+{{ '<beginning_of_sentence>system ai_setting=assistant\n' + ns.system_prompt + '<end_of_sentence>\n' -}}
+{%- endif %}
+
+{#- Tools configuration #}
+{%- if tools is not none %}
+{{ '<beginning_of_sentence>system tool_setting=tools\nYou are provided with these tools:\n<tools>\n' -}}
+{%- for tool in tools %}
+{{ tool | tojson ~ '\n' -}}
+{%- endfor %}
+{{ '</tools>\n\nIf you need to call tools, please respond with <tool_calls></tool_calls> XML tags, and provide tool-name and json-object of arguments, following the format below:\n<tool_calls>\n{"name": <tool-name>, "arguments": <args-json-object>}\n...\n</tool_calls><end_of_sentence>\n' -}}
+{%- endif %}
+
+{#- Process messages #}
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {%- if message['role'] == 'user' %}
+{{ '<beginning_of_sentence>user name=user\n' -}}
+{%- if message['content'] is string %}
+{{ message['content']|trim -}}
+{%- else %}
+{%- for content in message['content'] %}
+{%- if content['type'] == 'text' %}
+{{ content['text']|trim -}}
+{%- endif %}
+{%- endfor %}
+{%- endif %}
+{{ '<end_of_sentence>\n' -}}
+        {%- elif message['role'] == 'assistant' %}
+{{ '<beginning_of_sentence>ai name=assistant\n' -}}
+{%- if message['content'] is string %}
+{{ message['content']|trim -}}
+{%- else %}
+{%- for content in message['content'] | selectattr('type', 'equalto', 'text') %}
+{{ content['text']|trim -}}
+{%- endfor %}
+{%- endif %}
+{{ '<end_of_sentence>\n' -}}
+        {%- endif %}
+    {%- elif 'tool_calls' in message %}
+{{ '<beginning_of_sentence>ai name=assistant\n<tool_calls>\n' -}}
+{%- for tool_call in message.tool_calls %}
+{{ '{"name": "' + tool_call.function.name + '", "arguments": ' + tool_call.function.arguments | tojson + '}\n' -}}
+{%- endfor %}
+{{ '</tool_calls><end_of_sentence>\n' -}}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+{{ '<beginning_of_sentence>tool name=tools\n' -}}
+{%- if message.content is string %}
+{{ 'tool result: ' + message.content + '\n\n' -}}
+{%- else %}
+{%- for content in message['content'] %}
+{%- if content['type'] == 'text' %}
+{{ 'tool result: ' + content['text'] + '\n\n' -}}
+{%- elif content.get('name') %}
+{{ 'tool name: ' + content['name'] + '\ntool result: ' + content['text'] + '\n\n' -}}
+{%- endif %}
+{%- endfor %}
+{%- endif %}
+{{ '<end_of_sentence>\n' -}}
+    {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+{{ '<beginning_of_sentence>ai name=assistant\n' -}}
+{%- endif %}

scripts/bench-models.sh

@@ -22,8 +22,8 @@ if (( QUICK )); then
 fi
 
 if (( DIO )); then
-    ARGS_BB="${ARGS_BB} --no-mmap --direct-io"
-    ARGS_B="${ARGS_B} -mmp 0 -dio 1"
+    ARGS_BB="${ARGS_BB} --load-mode dio"
+    ARGS_B="${ARGS_B} --load-mode dio"
 fi
 
 run_model() {

scripts/snapdragon/adb/run-bench.sh

@@ -43,7 +43,7 @@ adb $adbserial $adbhost shell " \
   cd $basedir;         \
   LD_LIBRARY_PATH=$basedir/$branch/lib   \
   ADSP_LIBRARY_PATH=$basedir/$branch/lib \
-    $ndev $nhvx $opmask $verbose $profile $hb ./$branch/bin/llama-bench --device $device --mmap 0 -m $basedir/../gguf/$model \
+    $ndev $nhvx $opmask $verbose $profile $hb ./$branch/bin/llama-bench --device $device --load-mode none -m $basedir/../gguf/$model \
         --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \
         --ubatch-size 1024 -fa 1 -ngl 99 $cli_opts $@   \
 "

scripts/snapdragon/adb/run-cli.sh

@@ -71,7 +71,7 @@ adb $adbserial $adbhost shell " \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
     $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $opflt $vmem $mbuf \
-      ./$branch/bin/llama-cli --no-mmap -m $basedir/../gguf/$model \
+      ./$branch/bin/llama-cli --load-mode none -m $basedir/../gguf/$model \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1           \
          --ctx-size 8192 --ubatch-size 1024 -fa on                 \
          -ngl 99 --device $device $cli_opts $@                     \

scripts/snapdragon/adb/run-completion.sh

@@ -79,7 +79,7 @@ adb $adbserial $adbhost shell " \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
     $verbose $sched $opmask $profile $nhvx $hmx $ndev $hb $opbatch $opqueue $oppoll $opflt $opfuse $vmem $mbuf $mmsel $fasel \
-      ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model \
+      ./$branch/bin/llama-completion --load-mode none -m $basedir/../gguf/$model \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1                  \
          --ctx-size 8192 --ubatch-size 1024 -fa on                        \
          -ngl 99 --device $device $cli_opts $@                            \

scripts/snapdragon/adb/run-mtmd.sh

@@ -62,7 +62,7 @@ adb $adbserial $adbhost shell " \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
     $verbose $experimental $sched $opmask $profile $hmx $nhvx $ndev $mtmd_backend \
-      ./$branch/bin/llama-mtmd-cli --no-mmap -m $basedir/../gguf/$model      \
+      ./$branch/bin/llama-mtmd-cli --load-mode none -m $basedir/../gguf/$model      \
          --mmproj $basedir/../gguf/$mmproj                                   \
          --image $basedir/../gguf/$image                                     \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1                     \

scripts/snapdragon/windows/run-bench.ps1

@@ -43,6 +43,6 @@ if ($null -ne $env:HB) {
 $env:ADSP_LIBRARY_PATH="$basedir\lib"
 
 & "$basedir\bin\llama-bench.exe" `
-    --mmap 0 -m $basedir\..\..\gguf\$model `
+    --load-mode none -m $basedir\..\..\gguf\$model `
     --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
     --ubatch-size 1024 -ngl 99 --device $device $cli_opts

scripts/snapdragon/windows/run-cli.ps1

@@ -47,7 +47,7 @@ if ($null -ne $env:HB) {
 $env:ADSP_LIBRARY_PATH="$basedir\lib"
 
 & "$basedir\bin\llama-cli.exe" `
-    --no-mmap -m $basedir\..\..\gguf\$model `
+    --load-mode none -m $basedir\..\..\gguf\$model `
     --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
     --ctx-size 8192 --ubatch-size 1024 -fa on `
     -ngl 99 --device $device $cli_opts

scripts/snapdragon/windows/run-completion.ps1

@@ -47,7 +47,7 @@ if ($null -ne $env:HB) {
 $env:ADSP_LIBRARY_PATH="$basedir\lib"
 
 & "$basedir\bin\llama-completion.exe" `
-    --no-mmap -m $basedir\..\..\gguf\$model `
+    --load-mode none -m $basedir\..\..\gguf\$model `
     --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `
     --ctx-size 8192 --ubatch-size 1024 -fa on `
     -ngl 99 -no-cnv --device $device $cli_opts

scripts/snapdragon/windows/run-mtmd.ps1

@@ -60,7 +60,7 @@ if ($null -ne $env:MTMD_DEVICE) {
 $env:ADSP_LIBRARY_PATH="$basedir\lib"
 
 & "$basedir\bin\llama-mtmd-cli.exe" `
-    --no-mmap -m $basedir\..\..\gguf\$model `
+    --load-mode none -m $basedir\..\..\gguf\$model `
     --mmproj $basedir\..\..\gguf\$mmproj `
     --image $basedir\..\..\gguf\$image `
     --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 `

scripts/sync-ggml.last

@@ -1 +1 @@
-8846b79e66747bb9f68597420e95114c177315ce
+2d191b5dee1a591c41ee8a653ce42bfcd9c8716d

src/llama-arch.cpp

@@ -128,6 +128,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_SEED_OSS,         "seed_oss"         },
     { LLM_ARCH_GROVEMOE,         "grovemoe"         },
     { LLM_ARCH_APERTUS,          "apertus"          },
+    { LLM_ARCH_MINIMAX_01,       "minimax-01"       },
     { LLM_ARCH_MINIMAX_M2,       "minimax-m2"       },
     { LLM_ARCH_MINIMAX_M3,       "minimax-m3"       },
     { LLM_ARCH_COGVLM,           "cogvlm"           },
@@ -978,6 +979,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_QWEN35:
         case LLM_ARCH_QWEN35MOE:
         case LLM_ARCH_DEEPSEEK4:
+        case LLM_ARCH_MINIMAX_01:
             return true;
         default:
             return false;
@@ -1033,6 +1035,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
         case LLM_ARCH_GRANITE_HYBRID:
         case LLM_ARCH_LFM2:
         case LLM_ARCH_LFM2MOE:
+        case LLM_ARCH_MINIMAX_01:
         case LLM_ARCH_MINIMAX_M2:
         case LLM_ARCH_MINIMAX_M3:
         case LLM_ARCH_MISTRAL4:

src/llama-arch.h

@@ -153,6 +153,7 @@ enum llm_arch {
     LLM_ARCH_NANBEIGE,
     LLM_ARCH_QWEN3TTS,
     LLM_ARCH_POCKETTTS,
+    LLM_ARCH_MINIMAX_01,
     LLM_ARCH_UNKNOWN,
 };
 

src/llama-context.cpp

@@ -2300,6 +2300,7 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
         model.arch == LLM_ARCH_DEEPSEEK4 ||
         (model.arch == LLM_ARCH_DFLASH && model.hparams.dsv4_hc_mult > 0) ||
         model.arch == LLM_ARCH_NANBEIGE ||
+        model.arch == LLM_ARCH_MINIMAX_01 ||
         model.arch == LLM_ARCH_MINIMAX_M3) {
         res = std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
     } else {

src/llama-hparams.cpp

@@ -217,6 +217,13 @@ uint32_t llama_hparams::n_embd_s() const {
         return n_embd_head_kda * n_embd_head_kda * n_head();  // 128 * 128 * 32 = 524288
     }
 
+    if (n_embd_head_la != 0) {
+        // for MiniMax-Text-01 linear attention layers
+        // Full recurrent state: head_dim * head_dim * n_head
+        // tensor shape for linear attention: [head_dim, head_dim, n_head]
+        return n_embd_head_la * n_embd_head_la * n_head();  // 128 * 128 * 64 = 1048576
+    }
+
     // corresponds to Mamba's ssm_states size
     return ssm_d_state * ssm_d_inner;
 }

src/llama-hparams.h

@@ -164,6 +164,9 @@ struct llama_hparams {
     uint32_t ssm_dt_rank = 0;
     uint32_t ssm_n_group = 0;
 
+    // for MiniMax-Text-01 linear attention
+    uint32_t n_embd_head_la = 0;
+
     // for Kimi Linear KDA
     uint32_t n_embd_head_kda = 0;
 

src/llama-model-loader.cpp

@@ -1178,7 +1178,7 @@ struct ggml_tensor * llama_model_loader::create_tensor(
                         if (use_mmap) {
                             static std::once_flag once;
                             std::call_once(once, [] {
-                                LLAMA_LOG_WARN("llama_model_loader: tensor overrides to CPU are used with mmap enabled - consider using --no-mmap for better performance\n");
+                                LLAMA_LOG_WARN("llama_model_loader: tensor overrides to CPU are used with mmap enabled - consider using --load-mode none for better performance\n");
                             });
                         }
                     } else {

src/llama-model.cpp

@@ -296,6 +296,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_grovemoe(params);
         case LLM_ARCH_APERTUS:
             return new llama_model_apertus(params);
+        case LLM_ARCH_MINIMAX_01:
+            return new llama_model_minimax_01(params);
         case LLM_ARCH_MINIMAX_M2:
             return new llama_model_minimax_m2(params);
         case LLM_ARCH_MINIMAX_M3:
@@ -798,6 +800,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_290B:          return "290B";
         case LLM_TYPE_314B:          return "314B";
         case LLM_TYPE_405B:          return "405B";
+        case LLM_TYPE_456B:          return "456B";
         case LLM_TYPE_671B:          return "671B";
         case LLM_TYPE_SMALL:         return "0.1B";
         case LLM_TYPE_MEDIUM:        return "0.4B";
@@ -2283,7 +2286,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                         filter_recr = [&](uint32_t il) {
                             return hparams.is_recr(il) && hparams.n_ff(il) == 0;
                         };
-                    } else if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE) {
+                    } else if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE || arch == LLM_ARCH_MINIMAX_01) {
                         filter_attn = [&](uint32_t il) {
                             return il < hparams.n_layer() && !hparams.is_recr(il);
                         };
@@ -2704,6 +2707,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_SEED_OSS:
         case LLM_ARCH_GROVEMOE:
         case LLM_ARCH_APERTUS:
+        case LLM_ARCH_MINIMAX_01:
         case LLM_ARCH_MINIMAX_M2:
         case LLM_ARCH_MINIMAX_M3:
         case LLM_ARCH_COGVLM:

src/llama-model.h

@@ -99,6 +99,7 @@ enum llm_type {
     LLM_TYPE_290B,
     LLM_TYPE_314B,
     LLM_TYPE_405B,
+    LLM_TYPE_456B,
     LLM_TYPE_671B,
     LLM_TYPE_SMALL,
     LLM_TYPE_MEDIUM,
@@ -271,6 +272,7 @@ struct llama_layer {
     struct ggml_tensor * wv        = nullptr;
     struct ggml_tensor * wo        = nullptr;
     struct ggml_tensor * wqkv      = nullptr;
+    struct ggml_tensor * wg        = nullptr;
     struct ggml_tensor * wq_a      = nullptr;
     struct ggml_tensor * wq_b      = nullptr;
     struct ggml_tensor * wkv_a_mqa = nullptr;

src/models/dflash.cpp

@@ -43,6 +43,8 @@ void llama_model_dflash::load_arch_hparams(llama_model_loader & ml) {
         ml.get_key(LLM_KV_HYPER_CONNECTION_EPSILON,             hparams.dsv4_hc_eps);
         ml.get_arr(LLM_KV_ATTENTION_COMPRESS_RATIOS,            hparams.dsv4_compress_ratios, false);
 
+        GGML_ASSERT(hparams.dsv4_o_group_count > 0); // avoid div by zero
+
         if (hparams.expert_gating_func != LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS) {
             throw std::runtime_error("DSpark DSV4 draft expects sqrtsoftplus MoE scoring");
         }

src/models/minimax-01.cpp

@@ -0,0 +1,520 @@
+#include "models.h"
+#include "llama-memory-recurrent.h"
+
+void llama_model_minimax_01::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_RESIDUAL_SCALE,              hparams.f_residual_scale);
+
+    // we use n_embd_head_la to set recurrent memory n_embd_s
+    hparams.n_embd_head_la = hparams.n_embd_head_k_full;
+
+    // Mark recurrent layers (lightning attention layers).
+    if (!ml.get_key_or_arr(LLM_KV_ATTENTION_RECURRENT_LAYERS, hparams.is_recr_impl, hparams.n_layer_all, false)) {
+        uint32_t full_attn_interval = 8;
+        ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
+        for (uint32_t i = 0; i < hparams.n_layer_all; ++i) {
+            hparams.is_recr_impl[i] = (i < hparams.n_layer()) && ((i + 1) % full_attn_interval != 0);
+        }
+    }
+
+    switch (hparams.n_layer()) {
+        case 80: type = LLM_TYPE_456B; break;
+        default: type = LLM_TYPE_UNKNOWN;
+    }
+}
+
+void llama_model_minimax_01::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+    // output
+    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 is NULL, init from the input tok embed
+    if (output == NULL) {
+        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+    }
+
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        if (!hparams.is_recr(i)) {
+            create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
+        } else {
+            layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd_head_k * n_head}, 0);
+            layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd_head_k * n_head}, 0);
+            layer.wg = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+        }
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);
+        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);
+        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);
+        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_minimax_01::build_arch_graph(const llm_graph_params & params) const {
+    return std::make_unique<graph>(*this, params);
+}
+
+class llm_graph_input_la : public llm_graph_input_i {
+public:
+    llm_graph_input_la(const llama_hparams & hparams) : hparams(hparams) {}
+
+    void set_input(const llama_ubatch * ubatch) override {
+        // this operates on assumption that we have an equal ubatch split
+
+        const int64_t n_head = hparams.n_head();
+        const int32_t n_seqs = ubatch->n_seqs;
+        const int32_t n_seqs_unq = ubatch->n_seqs_unq;
+        const int32_t n_tokens = ubatch->n_tokens;
+        const int32_t n_seq_tokens = ubatch->n_seq_tokens;
+
+        std::vector<llama_pos> p0(n_seqs_unq);
+        std::fill(p0.begin(), p0.end(), std::numeric_limits<llama_pos>::max());
+
+        // get lowest token position in a ubatch for each stream
+        for (int i = 0; i < n_tokens; ++i) {
+            llama_seq_id seq_id = ubatch->seq_id[i][0];
+            int32_t seq_idx = ubatch->seq_idx[seq_id];
+            llama_pos pos = ubatch->pos[i];
+            if (p0[seq_idx] > pos) {
+                p0[seq_idx] = pos;
+            }
+        }
+
+        if (inp_slopes) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(inp_slopes->buffer));
+
+            float * data = (float *) inp_slopes->data;
+
+            float start = powf(2, -powf(2, -(log2f(n_head) - 3)));
+            float ratio = start;
+
+            for (int h = 0; h < n_head; ++h) {
+                data[h] = start * powf(ratio, h);
+            }
+        }
+
+        if (inp_q_decay) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(inp_q_decay->buffer));
+
+            float * slopes = (float *) inp_slopes->data;
+            float * data = (float *) inp_q_decay->data;
+
+            for (int s = 0; s < n_seqs; ++s) {
+                for (int i = 0; i < n_seq_tokens; ++i) {
+                    llama_seq_id seq_id = ubatch->seq_id[s * n_seq_tokens + i][0];
+                    int32_t seq_idx = ubatch->seq_idx[seq_id];
+                    llama_pos pos = ubatch->pos[s * n_seq_tokens + i];
+                    int pos_rel = pos - p0[seq_idx];
+
+                    for (int h = 0; h < n_head; ++h) {
+                        data[seq_idx * n_head * n_seq_tokens + i * n_head + h] = -slopes[h] * (pos_rel + 1);
+                    }
+                }
+            }
+        }
+
+        if (inp_k_decay) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(inp_k_decay->buffer));
+
+            float * slopes = (float *) inp_slopes->data;
+            float * data = (float *) inp_k_decay->data;
+
+            for (int s = 0; s < n_seqs; ++s) {
+                for (int i = 0; i < n_seq_tokens; ++i) {
+                    llama_seq_id seq_id = ubatch->seq_id[s * n_seq_tokens + i][0];
+                    int32_t seq_idx = ubatch->seq_idx[seq_id];
+                    llama_pos pos = ubatch->pos[s * n_seq_tokens + i];
+                    int pos_rel = pos - p0[seq_idx];
+
+                    for (int h = 0; h < n_head; ++h) {
+                        data[seq_idx * n_head * n_seq_tokens + i * n_head + h] = -slopes[h] * (n_seq_tokens - pos_rel - 1);
+                    }
+                }
+            }
+        }
+
+        if (inp_diag_decay) {
+            GGML_ASSERT(ggml_backend_buffer_is_host(inp_diag_decay->buffer));
+
+            float * slopes = (float *) inp_slopes->data;
+            float * data = (float *) inp_diag_decay->data;
+
+            for (int s = 0; s < n_seqs; ++s) {
+                for (int h = 0; h < n_head; ++h) {
+                    for (int j = 0; j < n_seq_tokens; ++j) {
+                        llama_seq_id seq_id = ubatch->seq_id[s * n_seq_tokens + j][0];
+                        int32_t seq_idx = ubatch->seq_idx[seq_id];
+                        llama_pos pos_j = ubatch->pos[s * n_seq_tokens + j];
+                        int pos_rel_j = pos_j - p0[seq_idx];
+
+                        for (int i = 0; i < n_seq_tokens; ++i) {
+                            llama_pos pos_i = ubatch->pos[s * n_seq_tokens + i];
+                            int pos_rel_i = pos_i - p0[seq_idx];
+
+                            int index = pos_rel_j - pos_rel_i;
+                            float s_index = index >= 0 ? -slopes[h] * index : -INFINITY;
+                            data[seq_idx * n_head * n_seq_tokens * n_seq_tokens + h * n_seq_tokens * n_seq_tokens + j * n_seq_tokens + i] = s_index;
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    bool can_reuse(const llm_graph_params & params) override {
+        bool res = true;
+
+        if (params.ubatch.n_seq_tokens > 1) {
+            res &= (   inp_q_decay &&    inp_q_decay->ne[2] == params.ubatch.n_seq_tokens);
+            res &= (   inp_k_decay &&    inp_k_decay->ne[2] == params.ubatch.n_seq_tokens);
+            res &= (inp_diag_decay && inp_diag_decay->ne[1] == params.ubatch.n_seq_tokens);
+        }
+
+        return res;
+    }
+
+    const llama_hparams & hparams;
+
+    ggml_tensor * inp_slopes     = nullptr; // F32 [n_head]
+    ggml_tensor * inp_q_decay    = nullptr; // F32 [1, n_head, n_batch]
+    ggml_tensor * inp_k_decay    = nullptr; // F32 [1, n_head, n_batch]
+    ggml_tensor * inp_diag_decay = nullptr; // F32 [n_batch, n_batch, n_head]
+};
+
+llama_model_minimax_01::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_ASSERT(n_embd_head == n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
+
+    const int64_t n_seqs  = ubatch.n_seqs;
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    auto * inp_hybrid = build_inp_mem_hybrid();
+    auto * inp_rs = inp_hybrid->get_recr();
+
+    ggml_tensor * inp_pos = build_inp_pos();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    llm_graph_input_la * la = nullptr;
+
+    auto inp = std::make_unique<llm_graph_input_la>(hparams);
+
+    inp->inp_slopes = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_head);
+    ggml_set_input(inp->inp_slopes);
+    cb(inp->inp_slopes, "slopes", -1);
+
+    if (n_seq_tokens != 1) {
+        inp->inp_q_decay = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, n_head, n_seq_tokens, n_seqs);
+        ggml_set_input(inp->inp_q_decay);
+        cb(inp->inp_q_decay, "q_decay_exp", -1);
+
+        inp->inp_k_decay = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 1, n_head, n_seq_tokens, n_seqs);
+        ggml_set_input(inp->inp_k_decay);
+        cb(inp->inp_k_decay, "k_decay_exp", -1);
+
+        inp->inp_diag_decay = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_seq_tokens, n_seq_tokens, n_head, n_seqs);
+        ggml_set_input(inp->inp_diag_decay);
+        cb(inp->inp_diag_decay, "diag_decay_exp", -1);
+    }
+
+    la = (llm_graph_input_la *) res->add_input(std::move(inp));
+
+    ggml_tensor * slopes = la->inp_slopes;
+
+    for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        ggml_tensor * residual = cur;
+
+        // self_attention
+        if (!hparams.is_recr(il)) {
+            // softmax attention layer
+
+            auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+                    n_embd_head, n_head, n_head_kv, il);
+
+            Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+            Kcur = ggml_rope_ext(
+                ctx0, Kcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_hybrid->get_attn(),
+                    model.layers[il].wo, NULL, model.layers[il].wo_s,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        } else {
+            // lightning attention layer
+
+            const auto * mctx_cur = inp_rs->mctx;
+            const auto kv_head = mctx_cur->get_head();
+
+            // TODO unneeded - any way to make conv states optional in recurrent memory?
+            ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+            ggml_tensor * conv_state_all  = build_rs(inp_rs, conv_states_all, hparams.n_embd_r(), n_seqs);
+            ggml_build_forward_expand(gf, conv_state_all);
+
+            float slope_scale = 1.0 - 1.0 * il / (n_layer - 1) + 1e-5;
+            ggml_tensor * slope_rate = ggml_scale(ctx0, slopes, slope_scale);
+            cb(slope_rate, "slope_rate", il);
+
+            cur = ggml_reshape_4d(ctx0, cur, cur->ne[0], n_seq_tokens, 1, n_seqs);
+
+            ggml_tensor * QKVcur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(QKVcur, "QKVcur", il);
+
+            QKVcur = ggml_silu(ctx0, QKVcur);
+            cb(QKVcur, "QKVcur_silu", il);
+
+            QKVcur = ggml_reshape_4d(ctx0, QKVcur, n_embd_head * 3, n_head, n_seq_tokens, n_seqs);
+
+            ggml_tensor * Qcur = ggml_view_4d(ctx0, QKVcur, n_embd_head, n_head, n_seq_tokens, n_seqs, QKVcur->nb[1], QKVcur->nb[2], QKVcur->nb[3], 0*ggml_element_size(QKVcur)*n_embd_head);
+            ggml_tensor * Kcur = ggml_view_4d(ctx0, QKVcur, n_embd_head, n_head, n_seq_tokens, n_seqs, QKVcur->nb[1], QKVcur->nb[2], QKVcur->nb[3], 1*ggml_element_size(QKVcur)*n_embd_head);
+            ggml_tensor * Vcur = ggml_view_4d(ctx0, QKVcur, n_embd_head, n_head, n_seq_tokens, n_seqs, QKVcur->nb[1], QKVcur->nb[2], QKVcur->nb[3], 2*ggml_element_size(QKVcur)*n_embd_head);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            // get previous KV
+            ggml_tensor * la_states_all = mctx_cur->get_s_l(il);
+            ggml_tensor * state = build_rs(inp_rs, la_states_all, hparams.n_embd_s(), n_seqs);
+
+            ggml_tensor * kv_old = ggml_reshape_4d(ctx0, state, n_embd_head, n_embd_head, n_head, n_seqs);
+            cb(kv_old, "kv_old", il);
+
+            ggml_tensor * qkv = nullptr;
+            ggml_tensor * kv_new = nullptr;
+
+            if (n_seq_tokens == 1) {
+                // lightning attention - optimized single token case for TG
+
+                ggml_tensor * slopes_neg = ggml_scale(ctx0, slope_rate, -1.0);
+                cb(slopes_neg, "slopes_neg", il);
+
+                ggml_tensor * ratio = ggml_exp(ctx0, slopes_neg);
+                cb(ratio, "ratio", il);
+
+                ggml_tensor * ratio_3d = ggml_reshape_3d(ctx0, ratio, 1, 1, n_head);
+                cb(ratio_3d, "ratio3d", il);
+
+                ggml_tensor * v_trans = ggml_cont(ctx0, ggml_permute(ctx0, Vcur, 1, 2, 0, 3));
+                cb(v_trans, "v_trans", il);
+
+                ggml_tensor * k_trans = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 1, 2, 0, 3));
+                cb(k_trans, "k_trans", il);
+
+                ggml_tensor * kv_cur = ggml_mul_mat(ctx0, k_trans, v_trans);
+                cb(kv_cur, "kv_cur", il);
+
+                ggml_tensor * kv_old_s = ggml_mul(ctx0, kv_old, ratio_3d);
+                cb(kv_old_s, "kv_old_s", il);
+
+                kv_new = ggml_add(ctx0, kv_old_s, kv_cur);
+                cb(kv_new, "kv_new", il);
+
+                ggml_tensor * q_trans = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+                cb(q_trans, "q_trans", il);
+
+                qkv = ggml_mul_mat(ctx0, kv_new, q_trans);
+                cb(qkv, "qkv", il);
+            } else if(n_seq_tokens > 1) {
+                // lightning attention - general multi token case for PP
+
+                ggml_tensor *    q_decay_exp = la->inp_q_decay;
+                ggml_tensor *    k_decay_exp = la->inp_k_decay;
+                ggml_tensor * diag_decay_exp = la->inp_diag_decay;
+
+                ggml_tensor *    q_decay = ggml_exp(ctx0, ggml_scale(ctx0, q_decay_exp, slope_scale));
+                cb(q_decay, "q_decay", il);
+                ggml_tensor *    k_decay = ggml_exp(ctx0, ggml_scale(ctx0, k_decay_exp, slope_scale));
+                cb(k_decay, "k_decay", il);
+                ggml_tensor * diag_decay = ggml_exp(ctx0, ggml_scale(ctx0, diag_decay_exp, slope_scale));
+                cb(diag_decay, "diag_decay", il);
+
+                ggml_tensor * q_s = ggml_mul(ctx0, Qcur, q_decay);
+                cb(q_s, "q_s", il);
+
+                ggml_tensor * q_s_trans = ggml_permute(ctx0, q_s, 0, 2, 1, 3);
+                cb(q_s_trans, "q_s_trans", il);
+
+                ggml_tensor * qkv_none_diag = ggml_mul_mat(ctx0, kv_old, q_s_trans);
+                cb(qkv_none_diag, "qkv_none_diag", il);
+
+                ggml_tensor * q_trans = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+                cb(q_trans, "q_trans", il);
+
+                ggml_tensor * k_trans = ggml_permute(ctx0, Kcur, 0, 2, 1, 3);
+                cb(k_trans, "k_trans", il);
+
+                ggml_tensor * qk = ggml_mul_mat(ctx0, k_trans, q_trans);
+                cb(qk, "qk", il);
+
+                qk = ggml_mul(ctx0, qk, diag_decay);
+                cb(qk, "qk_s", il);
+
+                ggml_tensor * v_trans = ggml_cont(ctx0, ggml_permute(ctx0, Vcur, 1, 2, 0, 3));
+                cb(v_trans, "v_trans", il);
+
+                ggml_tensor * qkv_diag = ggml_mul_mat(ctx0, v_trans, qk);
+                cb(qkv_diag, "qkv_diag", il);
+
+                qkv = ggml_add(ctx0, qkv_none_diag, qkv_diag);
+                cb(qkv, "qkv", il);
+
+                ggml_build_forward_expand(gf, qkv);
+
+                ggml_tensor * slopes_neg = ggml_scale(ctx0, slope_rate, -1.0*n_seq_tokens);
+                cb(slopes_neg, "slopes_neg", il);
+
+                ggml_tensor * block_decay = ggml_exp(ctx0, slopes_neg);
+                cb(block_decay, "block_decay", il);
+
+                ggml_tensor * block_decay_3d = ggml_reshape_3d(ctx0, block_decay, 1, 1, n_head);
+                cb(block_decay_3d, "block_decay_3d", il);
+
+                ggml_tensor * kv_old_s = ggml_mul(ctx0, kv_old, block_decay_3d);
+                cb(kv_old_s, "kv_old_s", il);
+
+                ggml_tensor * k_after_decay = ggml_mul(ctx0, Kcur, k_decay);
+                cb(k_after_decay, "k_after_decay", il);
+
+                ggml_tensor * k_after_decay_trans = ggml_cont(ctx0, ggml_permute(ctx0, k_after_decay, 1, 2, 0, 3));
+                cb(k_after_decay_trans, "k_after_decay_trans", il);
+
+                ggml_tensor * kv_cur = ggml_mul_mat(ctx0, k_after_decay_trans, v_trans);
+                cb(kv_cur, "kv_cur", il);
+
+                kv_new = ggml_add(ctx0, kv_old_s, kv_cur);
+                cb(kv_new, "kv_new", il);
+            }
+
+            // store new KV
+            ggml_build_forward_expand(gf,
+                                     ggml_cpy(ctx0, kv_new,
+                                              ggml_view_1d(ctx0, la_states_all, hparams.n_embd_s() * n_seqs,
+                                                           kv_head * hparams.n_embd_s() * ggml_element_size(la_states_all))));
+
+            qkv = ggml_cont(ctx0, ggml_permute(ctx0, qkv, 0, 2, 1, 3));
+            cb(qkv, "qkv_permuted", il);
+
+            qkv = ggml_reshape_4d(ctx0, qkv, qkv->ne[0]*qkv->ne[1], qkv->ne[2], 1, qkv->ne[3]);
+
+            // norm
+            ggml_tensor * qkv_norm = build_norm(qkv,
+                    model.layers[il].attn_norm_2, NULL,
+                    LLM_NORM_RMS, il);
+            cb(qkv_norm, "qkv_norm", il);
+
+            ggml_tensor * g = build_lora_mm(model.layers[il].wg, cur);
+            cb(g, "g", il);
+
+            g = ggml_sigmoid(ctx0, g);
+            cb(g, "g_sigm", il);
+
+            cur = ggml_mul(ctx0, g, qkv_norm);
+
+            cur = build_lora_mm(model.layers[il].wo, cur);
+            cb(cur, "attn_out", il);
+
+            cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens*n_seqs);
+            cb(cur, "attn_out", 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);
+            residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+        }
+
+        residual = ggml_scale(ctx0, residual, hparams.f_residual_scale);
+        cb(residual, "residual_scaled_attn", il);
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, residual);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // MoE branch
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        residual = cur;
+
+        cur = 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, true,
+                hparams.expert_weights_scale,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                il);
+        cb(cur, "ffn_moe_out", il);
+
+        residual = ggml_scale(ctx0, residual, hparams.f_residual_scale);
+        cb(residual, "residual_scaled_ffn", il);
+
+        cur = ggml_add(ctx0, cur, residual);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        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;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur, model.output_s);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

src/models/minimax-m3.cpp

@@ -25,6 +25,8 @@ void llama_model_minimax_m3::load_arch_hparams(llama_model_loader & ml) {
     ml.get_key(LLM_KV_ATTENTION_INDEXER_LOCAL_BLOCKS,  hparams.indexer_local_blocks);
     msa_p = { (int) hparams.indexer_block_size, (int) hparams.indexer_top_k, (int) hparams.indexer_local_blocks };
 
+    GGML_ASSERT(hparams.indexer_block_size > 0); // avoid div by zero
+
     switch (hparams.n_layer()) {
         case 60: type = LLM_TYPE_428B_A23B; break;
         default: type = LLM_TYPE_UNKNOWN;

src/models/models.h

@@ -2043,6 +2043,19 @@ struct llama_model_apertus : public llama_model_base {
 };
 
 
+struct llama_model_minimax_01 : public llama_model_base {
+    llama_model_minimax_01(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_minimax_m2 : public llama_model_base {
     llama_model_minimax_m2(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;

tests/test-chat.cpp

@@ -6955,6 +6955,24 @@ static void test_reasoning_budget_message_per_request() {
     }
 }
 
+static void test_reasoning_effort_caps() {
+    LOG_DBG("%s\n", __func__);
+
+    auto assert_supports_effort = [](const std::string & path, bool expected) {
+        auto tmpls = read_templates(path);
+        assert_equals(expected, common_chat_templates_get_caps(tmpls.get()).at("supports_reasoning_effort"));
+    };
+
+    assert_supports_effort("models/templates/deepseek-ai-DeepSeek-V4.jinja", true);
+    assert_supports_effort("models/templates/muse-glimmer.jinja", true);
+    assert_supports_effort("models/templates/tencent-Hy3.jinja", true);
+    assert_supports_effort("models/templates/openai-gpt-oss-120b.jinja", true);
+    assert_supports_effort("models/templates/upstage-Solar-Open-100B.jinja", true);
+    assert_supports_effort("models/templates/Cohere2MoE.jinja", true);
+    assert_supports_effort("models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja", false);
+    assert_supports_effort("models/templates/Qwen-Qwen3-0.6B.jinja", false);
+}
+
 static void test_msg_diffs_compute() {
     LOG_DBG("%s\n", __func__);
     {
@@ -7114,6 +7132,7 @@ int main(int argc, char ** argv) {
         test_deepseek_v4_thinking_retention();
         test_deepseek_v4_tool_result_ordering();
         test_template_generation_prompt();
+        test_reasoning_effort_caps();
         test_reasoning_budget_tokens_per_request();
         test_reasoning_budget_message_per_request();
         test_template_output_peg_parsers(detailed_debug);

tests/test-jinja.cpp

@@ -33,6 +33,7 @@ static void test_array_methods(testing & t);
 static void test_object_methods(testing & t);
 static void test_hasher(testing & t);
 static void test_stats(testing & t);
+static void test_string_parts(testing & t);
 static void test_fuzzing(testing & t);
 
 static bool g_python_mode = false;
@@ -72,6 +73,7 @@ int main(int argc, char *argv[]) {
     if (!g_python_mode) {
         t.test("hasher", test_hasher);
         t.test("stats", test_stats);
+        t.test("string parts", test_string_parts);
         t.test("fuzzing", test_fuzzing);
     }
 
@@ -2057,6 +2059,36 @@ static void test_stats(testing & t) {
     });
 }
 
+static void test_string_parts(testing & t) {
+    static auto render = [](const std::string & tmpl, const json & vars) -> jinja::string {
+        jinja::lexer lexer;
+        auto lexer_res = lexer.tokenize(tmpl);
+
+        jinja::program ast = jinja::parse_from_tokens(lexer_res);
+
+        jinja::context ctx(tmpl);
+        jinja::global_from_json(ctx, vars, true);
+
+        jinja::runtime runtime(ctx);
+        return runtime.gather_string_parts(runtime.execute(ast))->as_string();
+    };
+
+    t.test("merge joins only the neighbours with the same type", [](testing & t) {
+        // "AB" comes from the input and merges, "-" comes from the template and must not
+        jinja::string res = render("{{ val.a }}{{ val.b }}-{{ val.c }}",
+                                   json{{"val", json{{"a", "A"}, {"b", "B"}, {"c", "C"}}}});
+
+        if (t.assert_true("3 parts after the merge", res.parts.size() == 3)) {
+            t.assert_true("part 0 is the merged input", res.parts[0].val == "AB" && res.parts[0].is_input);
+            t.assert_true("part 1 is from the template", res.parts[1].val == "-" && !res.parts[1].is_input);
+            t.assert_true("part 2 is input",             res.parts[2].val == "C" && res.parts[2].is_input);
+        } else {
+            t.log("parts: " + std::to_string(res.parts.size()) + ", rendered: " + json(res.str()).dump());
+        }
+    });
+
+}
+
 static void test_template_cpp(testing & t, const std::string & name, const std::string & tmpl, const json & vars, const std::string & expect) {
     t.test(name, [&tmpl, &vars, &expect](testing & t) {
         jinja::lexer lexer;

tests/test-llama-archs.cpp

@@ -243,6 +243,7 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
     ms.add_kv(LLM_KV_KDA_HEAD_DIM,              uint32_t(128));
     ms.add_kv(LLM_KV_WKV_HEAD_SIZE,             n_embd/n_head);
     ms.add_kv(LLM_KV_SHORTCONV_L_CACHE,         uint32_t(3));
+    ms.add_kv(LLM_KV_RESIDUAL_SCALE,            3.5565588200778455f);
 
     for (uint32_t il = 0; il < n_layer; il++) {
         ggml_tensor t;
@@ -364,6 +365,7 @@ static bool moe_mandatory(const llm_arch arch) {
         case LLM_ARCH_SMALLTHINKER:
         case LLM_ARCH_LLADA_MOE:
         case LLM_ARCH_GROVEMOE:
+        case LLM_ARCH_MINIMAX_01:
         case LLM_ARCH_MINIMAX_M2:
         case LLM_ARCH_MINIMAX_M3:
         case LLM_ARCH_RND1:
@@ -436,7 +438,7 @@ static bool arch_supported(const llm_arch arch) {
 
     // FIXME: these hit scheduler/view-backed-output issues with WebGPU on CI.
 #ifdef GGML_USE_WEBGPU
-    if (arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA) {
+    if (arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MINIMAX_01) {
         return false;
     }
 #endif // GGML_USE_WEBGPU

tools/cli/README.md

@@ -170,6 +170,7 @@
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
+| `--reasoning-effort LEVEL` | reasoning effort level given to the chat template: 'default' to keep the template default,<br/>or a level such as 'minimal', 'low', 'medium', 'high', 'xhigh' or 'max' (default: default)<br/>(env: LLAMA_ARG_REASONING_EFFORT) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
 | `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)<br/>compatible with certain templates having 'supports_preserve_reasoning' capability<br/>example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking<br/>(env: LLAMA_ARG_REASONING_PRESERVE) |

tools/completion/README.md

@@ -251,6 +251,7 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: disabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
+| `--reasoning-effort LEVEL` | reasoning effort level given to the chat template: 'default' to keep the template default,<br/>or a level such as 'minimal', 'low', 'medium', 'high', 'xhigh' or 'max' (default: default)<br/>(env: LLAMA_ARG_REASONING_EFFORT) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
 | `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)<br/>compatible with certain templates having 'supports_preserve_reasoning' capability<br/>example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking<br/>(env: LLAMA_ARG_REASONING_PRESERVE) |
@@ -523,13 +524,15 @@ These options help improve the performance and memory usage of the LLaMA models.
 -   `-t N, --threads N`: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Using the correct number of threads can greatly improve performance.
 -   `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. In some systems, it is beneficial to use a higher number of threads during batch processing than during generation. If not specified, the number of threads used for batch processing will be the same as the number of threads used for generation.
 
-### Mlock
+### Model Loading Mode
 
--   `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. This can improve performance but trades away some of the advantages of memory-mapping by requiring more RAM to run and potentially slowing down load times as the model loads into RAM.
-
-### No Memory Mapping
-
--   `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance. Disabling mmap results in slower load times but may reduce pageouts if you're not using `--mlock`. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all.
+-   `-lm MODE, --load-mode MODE`: Specify the model loading mode (default: `auto`).
+    -   `auto`: Memory-map the model, unless the device does not support it.
+    -   `none`: No special loading mode. Disabling mmap results in slower load times but may reduce pageouts if you're not using `mlock`. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all.
+    -   `mmap`: Memory-map the model.
+    -   `mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. This can improve performance but trades away some of the advantages of memory-mapping by requiring more RAM to run and potentially slowing down load times as the model loads into RAM.
+    -   `mmap+mlock`: Memory-map the model and lock it in memory.
+    -   `dio`: Use DirectIO if available.
 
 ### NUMA support
 

tools/llama-bench/README.md

@@ -67,8 +67,8 @@ test parameters:
   -nkvo, --no-kv-offload <0|1>              (default: 0)
   -fa, --flash-attn <on|off|auto>           (default: auto)
   -dev, --device <dev0/dev1/...>            (default: auto)
-  -mmp, --mmap <0|1>                        (default: 1)
-  -dio, --direct-io <0|1>                   (default: 0)
+  -mmp, --mmap <0|1>                        (DEPRECATED IN FAVOUR OF --load-mode)
+  -dio, --direct-io <0|1>                   (DEPRECATED IN FAVOUR OF --load-mode)
   -embd, --embeddings <0|1>                 (default: 0)
   -ts, --tensor-split <ts0/ts1/..>          (default: 0)
   -ot --override-tensor <tensor name pattern>=<buffer type>;...

tools/mtmd/clip-impl.h

@@ -6,6 +6,7 @@
 
 #include <array>
 #include <climits>
+#include <cmath>
 #include <cstdarg>
 #include <cinttypes>
 #include <string>
@@ -603,7 +604,7 @@ struct clip_image_u8 {
             // return a dummy value, so that legacy code can still process image without errors
             return { 0, 0, 0 };
         }
-        int idx = (y * nx + x) * 3;
+        size_t idx = ((size_t) y * (size_t) nx + (size_t) x) * 3;
         return { buf[idx], buf[idx + 1], buf[idx + 2] };
     }
 
@@ -611,8 +612,8 @@ struct clip_image_u8 {
         if (is_placeholder()) {
             return; // no-op
         }
-        int idx = (y * nx + x) * 3;
-        buf[idx] = rgb[0];
+        size_t idx = ((size_t) y * (size_t) nx + (size_t) x) * 3;
+        buf[idx]     = rgb[0];
         buf[idx + 1] = rgb[1];
         buf[idx + 2] = rgb[2];
     }
@@ -642,9 +643,25 @@ struct mtmd_serialization; // forward declaration
 struct clip_image_f32 {
     // marks the global view in e.g., DeepSeek-OCR Models
     bool add_viewsep = false;
-    // whether a learned newline (or EOI) token should be appended after the image (eg Granite4 Vision)
+    // appends a learned newline (or EOI) token after the image
+    // no model uses it now (Granite4 Vision moved to anyres), kept for future models
     bool add_newline = false;
 
+    // llava-next "anyres" tiling, used by Granite4 Vision
+    // the whole grid is encoded and assembled in a single graph
+    // NOTE: excluded from serialized: a deserialized image is always a placeholder, which is never encoded
+    struct anyres_info {
+        int grid_x = 0; // tiles per row, 0 means the image is not tiled
+        int grid_y = 0; // tiles per column
+        int orig_nx = 0; // size of the source image, used to drop the padding tokens
+        int orig_ny = 0;
+
+        bool is_tiled() const {
+            return grid_x > 0 && grid_y > 0;
+        }
+    };
+    anyres_info anyres;
+
     clip_image_size get_size() const {
         return { nx_, ny_ };
     }
@@ -726,6 +743,25 @@ struct clip_image_f32 {
     }
 };
 
+// token area kept after removing the padding added by the anyres resize
+// ref: https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/llava_next/modeling_llava_next.py#L109
+static inline void clip_anyres_unpad(int cur_w, int cur_h, int orig_w, int orig_h,
+                                     int & off_x, int & off_y, int & out_w, int & out_h) {
+    off_x = 0;
+    off_y = 0;
+    out_w = cur_w;
+    out_h = cur_h;
+    if ((float) orig_w / orig_h > (float) cur_w / cur_h) {
+        const int new_h = (int) std::floor((double) orig_h * cur_w / orig_w + 1e-7);
+        off_y = (cur_h - new_h) / 2;
+        out_h = cur_h - 2 * off_y;
+    } else {
+        const int new_w = (int) std::floor((double) orig_w * cur_h / orig_h + 1e-7);
+        off_x = (cur_w - new_w) / 2;
+        out_w = cur_w - 2 * off_x;
+    }
+}
+
 //
 // logging
 //

tools/mtmd/clip.cpp

@@ -1595,6 +1595,9 @@ struct clip_model_loader {
                         hparams.image_resize_algo = RESIZE_ALGO_BICUBIC_PILLOW;
                         hparams.image_resize_pad  = PAD_NONE;
                         get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
+                        // n_merge is used as a divisor in clip_image_batch_encode
+                        // (gh / n_merge); reject 0 to avoid int div-by-zero (DoS).
+                        GGML_ASSERT(hparams.n_merge > 0);
                         hparams.rope_theta = 10000.0f; // vision_config.rope_theta
                         // MiniMax-M3: max_pixels 451584 (=672^2) -> 576 merged tokens (image_seq_length)
                         hparams.set_limit_image_tokens(8, 576);
@@ -1823,7 +1826,9 @@ struct clip_model_loader {
                         // unlimited-ocr shares the v1 projector but tiles up to 32
                         get_u32(KEY_PREPROC_MIN_TILES, hparams.preproc_min_tiles, false);
                         get_u32(KEY_PREPROC_MAX_TILES, hparams.preproc_max_tiles, false);
-                        GGML_ASSERT(hparams.preproc_min_tiles <= hparams.preproc_max_tiles);
+                        GGML_ASSERT(hparams.preproc_min_tiles >= 0
+                                    && hparams.preproc_min_tiles <= hparams.preproc_max_tiles
+                                    && hparams.preproc_max_tiles <= 256);
                      } break;
                 case PROJECTOR_TYPE_HUNYUANVL:
                     {
@@ -1888,6 +1893,9 @@ struct clip_model_loader {
                         hparams.audio_window_len       = 400;
                         hparams.audio_hop_len          = 160;
                         get_u32(KEY_A_CHUNK_SIZE,           hparams.audio_chunk_size);
+                        // context_size is squared for the attn_dists/mask buffers; cap to prevent int32 overflow
+                        // (legitimate values are small, e.g. 12-200; 8192^2 = 67M still fits int32)
+                        GGML_ASSERT(hparams.audio_chunk_size > 0 && hparams.audio_chunk_size <= 8192);
                         get_u32(KEY_A_CONV_KERNEL_SIZE,     hparams.audio_conv_kernel_size);
                         get_u32(KEY_A_MAX_POS_EMB,          hparams.audio_max_pos_emb);
                         get_u32(KEY_A_PROJ_WINDOW_SIZE,     hparams.audio_proj_window_size);
@@ -1927,8 +1935,9 @@ struct clip_model_loader {
                     // note: some models having hparams.image_size == 0, which means the image size is dynamic
                     throw std::runtime_error(string_format("%s: image_size (%d) cannot be negative\n", __func__, hparams.image_size));
                 }
-                if (hparams.image_size > 65536) {
-                    throw std::runtime_error(string_format("%s: image_size (%d) is too large (max 65536)\n", __func__, hparams.image_size));
+                if (hparams.image_size > 8192) {
+                    // cap prevents int32 overflow in n_patches = (image_size/patch_size)^2
+                    throw std::runtime_error(string_format("%s: image_size (%d) is too large (max 8192)\n", __func__, hparams.image_size));
                 }
                 if (hparams.patch_size <= 0 || hparams.patch_size >= 65536) {
                     throw std::runtime_error(string_format("%s: patch_size (%d) must be positive and less than 65536\n", __func__, hparams.patch_size));
@@ -1939,9 +1948,12 @@ struct clip_model_loader {
                 if (hparams.image_max_pixels < hparams.image_min_pixels) {
                     throw std::runtime_error(string_format("%s: image_max_pixels (%d) is less than image_min_pixels (%d)\n", __func__, hparams.image_max_pixels, hparams.image_min_pixels));
                 }
-                if (hparams.n_merge < 0 || hparams.n_merge >= 65536) {
+                if (hparams.n_merge <= 0 || hparams.n_merge >= 65536) {
                     throw std::runtime_error(string_format("%s: n_merge (%d) must be greater than 0 and less than 65536\n", __func__, hparams.n_merge));
                 }
+                if (hparams.attn_window_size > 4096) {
+                    throw std::runtime_error(string_format("%s: attn_window_size (%d) is too large (max 4096)\n", __func__, hparams.attn_window_size));
+                }
             }
 
             LOG_INF("%s: projector:          %s\n", __func__, proj_type.c_str());
@@ -4217,18 +4229,20 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) {
         case PROJECTOR_TYPE_GRANITE4_VISION:
             {
                 // Per-tile output token count: each projector block outputs
-                // query_side^2 tokens per window × n^2 windows.
-                // For 384×384 input: n = 24/8 = 3, query_side = 4 → 144.
+                // query_side^2 tokens per window x n^2 windows.
+                // For 384x384 input: n = 24/8 = 3, query_side = 4 -> 144.
                 const int window_side = ctx->model.hparams.downsample_window_side;
                 const int query_side  = ctx->model.hparams.downsample_query_side;
                 const int side        = img->nx() / params.patch_size;
                 const int n           = side / window_side;
-                n_patches             = (query_side * n) * (query_side * n);
-                if (img->add_newline) {
-                    // For single-tile case: append 1 newline row.
-                    // For multi-tile rowwise: handled by caller, but here we
-                    // report the per-tile count including one trailing newline.
-                    n_patches += 1;
+                const int out_side    = query_side * n;
+                n_patches             = out_side * out_side;
+                if (img->anyres.is_tiled()) {
+                    // overview tile, then the unpadded tile grid with one newline per row
+                    int off_x, off_y, w, h;
+                    clip_anyres_unpad(img->anyres.grid_x * out_side, img->anyres.grid_y * out_side,
+                                      img->anyres.orig_nx, img->anyres.orig_ny, off_x, off_y, w, h);
+                    n_patches += h * (w + 1);
                 }
             } break;
         default:
@@ -5408,13 +5422,13 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
                 const int context_size = ctx->model.hparams.audio_chunk_size;
                 const int max_pos_emb  = ctx->model.hparams.audio_max_pos_emb;
 
-                std::vector<int32_t> dists(context_size * context_size);
+                std::vector<int32_t> dists((size_t) context_size * (size_t) context_size);
                 for (int i = 0; i < context_size; i++) {
                     for (int j = 0; j < context_size; j++) {
                         int d = i - j;
                         if (d < -context_size) d = -context_size;
                         if (d >  context_size) d =  context_size;
-                        dists[i * context_size + j] = d + max_pos_emb;
+                        dists[(size_t) i * (size_t) context_size + (size_t) j] = d + max_pos_emb;
                     }
                 }
                 set_input_i32("attn_dists", dists);
@@ -5423,13 +5437,13 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
                 const int remainder  = n_frames % context_size;
                 if (remainder > 0) {
                     const int num_blocks = (n_frames + context_size - 1) / context_size;
-                    std::vector<float> mask(context_size * context_size * num_blocks, 0.0f);
+                    std::vector<float> mask((size_t) context_size * (size_t) context_size * (size_t) num_blocks, 0.0f);
                     const float neg_inf = -INFINITY;
-                    const int last_block_offset = (num_blocks - 1) * context_size * context_size;
+                    const size_t last_block_offset = (size_t) (num_blocks - 1) * (size_t) context_size * (size_t) context_size;
                     for (int q = 0; q < context_size; q++) {
                         for (int k = 0; k < context_size; k++) {
                             if (q >= remainder || k >= remainder) {
-                                mask[last_block_offset + q * context_size + k] = neg_inf;
+                                mask[last_block_offset + (size_t) q * (size_t) context_size + (size_t) k] = neg_inf;
                             }
                         }
                     }
@@ -5493,10 +5507,18 @@ bool clip_encode(struct clip_ctx * ctx, struct clip_encode_params * params) {
                     return idx;
                 };
 
+                // the same permutation is applied to every tile of the stacked image
                 auto upload = [&](const std::string & name, const std::vector<int32_t> & idx) {
                     ggml_tensor * t = ggml_graph_get_tensor(gf, name.c_str());
                     GGML_ASSERT(t);
-                    ggml_backend_tensor_set(t, idx.data(), 0, idx.size() * sizeof(int32_t));
+                    GGML_ASSERT(ggml_nelements(t) % (int64_t) idx.size() == 0);
+                    const int n_rep = ggml_nelements(t) / idx.size();
+                    std::vector<int32_t> buf;
+                    buf.reserve(idx.size() * n_rep);
+                    for (int i = 0; i < n_rep; ++i) {
+                        buf.insert(buf.end(), idx.begin(), idx.end());
+                    }
+                    ggml_backend_tensor_set(t, buf.data(), 0, ggml_nbytes(t));
                 };
 
                 // Stage 1b only uses block 0's permutations; future stages

tools/mtmd/models/granite4-vision.cpp

@@ -14,18 +14,39 @@
  *   Stage 1a: SigLIP vision tower (N layers, post-norm)
  *   Stage 1b: WindowQFormer blocks (deepstack + spatial)
  *   Stage 1c: Concatenate and pack outputs
- *   Stage 1d: Append newline tokens if add_newline is set
+ *   Stage 1d: Assemble the anyres tiles into one token sequence
  */
 
 // ---------------------------------------------------------------------------
 // Member method implementations
 // ---------------------------------------------------------------------------
 
+// split the stacked tiles into the batch axis, then run the usual patch embedding
+ggml_tensor * clip_graph_granite4_vision::build_tile_inp() {
+    ggml_tensor * inp_raw = build_inp_raw();
+
+    if (n_tiles > 1) {
+        const int px = img.nx();
+        inp_raw = ggml_reshape_4d(ctx0, inp_raw, px * px, n_tiles, 3, 1);
+        inp_raw = ggml_cont(ctx0, ggml_permute(ctx0, inp_raw, 0, 2, 1, 3));
+        inp_raw = ggml_reshape_4d(ctx0, inp_raw, px, px, 3, n_tiles);
+    }
+
+    ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+    inp = ggml_reshape_3d(ctx0, inp, tile_side * tile_side, n_embd, n_tiles);
+    inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
+    if (model.patch_bias) {
+        inp = ggml_add(ctx0, inp, model.patch_bias);
+    }
+    return inp;
+}
+
 ggml_tensor * clip_graph_granite4_vision::gather(
         ggml_tensor * src,
         const std::string & name,
         int idx_len) {
-    ggml_tensor * idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, idx_len);
+    // one index row per tile, all rows hold the same permutation
+    ggml_tensor * idx = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, idx_len, n_tiles);
     ggml_set_name(idx, name.c_str());
     ggml_set_input(idx);
     return ggml_get_rows(ctx0, src, idx);
@@ -36,12 +57,15 @@ ggml_tensor * clip_graph_granite4_vision::interp_down(
         int side,
         int new_side) {
     const int n_embd = src->ne[0];
-    ggml_tensor * t = ggml_reshape_4d(ctx0, src, n_embd, side, side, 1);
+    ggml_tensor * t = ggml_reshape_4d(ctx0, src, n_embd, side, side, n_tiles);
     t = ggml_cont(ctx0, ggml_permute(ctx0, t, 2, 0, 1, 3));
+    // fold the tile axis into the channel axis, ggml_pool_2d only pools the first two axes
+    t = ggml_reshape_3d(ctx0, t, side, side, n_embd * n_tiles);
     const int kernel = side / new_side;
     t = ggml_pool_2d(ctx0, t, GGML_OP_POOL_AVG, kernel, kernel, kernel, kernel, 0, 0);
+    t = ggml_reshape_4d(ctx0, t, new_side, new_side, n_embd, n_tiles);
     t = ggml_cont(ctx0, ggml_permute(ctx0, t, 1, 2, 0, 3));
-    return ggml_reshape_2d(ctx0, t, n_embd, new_side * new_side);
+    return ggml_reshape_3d(ctx0, t, n_embd, new_side * new_side, n_tiles);
 }
 
 // ---------------------------------------------------------------------------
@@ -63,6 +87,7 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
     const int n = image_side / window_side;
     const int new_side = n * query_side;
     const int n_windows = n * n;
+    const int n_win_all = n_windows * n_tiles; // windows of every tile, batched together
     const int enc_len = window_side * window_side;
     const int query_len = query_side * query_side;
 
@@ -82,7 +107,7 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
         ggml_tensor * enc_flat = gather(x,
             "g4v_blk" + std::to_string(bid) + "_win_idx",
             image_side * image_side);
-        enc = ggml_reshape_3d(ctx0, enc_flat, n_embd, enc_len, n_windows);
+        enc = ggml_reshape_3d(ctx0, enc_flat, n_embd, enc_len, n_win_all);
     }
     cbx(enc, "enc");
 
@@ -104,7 +129,7 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
         ggml_tensor * dw_flat = gather(d,
             "g4v_blk" + std::to_string(bid) + "_qwin_idx",
             new_side * new_side);
-        ggml_tensor * dw = ggml_reshape_3d(ctx0, dw_flat, n_embd, query_len, n_windows);
+        ggml_tensor * dw = ggml_reshape_3d(ctx0, dw_flat, n_embd, query_len, n_win_all);
         q_in = ggml_add(ctx0, dw, blk.qf_proj_query);
     }
     cbx(q_in, "query_embeds");
@@ -140,12 +165,12 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
         ggml_tensor * K = linear(q, pl.k_w, pl.k_b);
         ggml_tensor * V = linear(q, pl.v_w, pl.v_b);
 
-        Q = ggml_reshape_4d(ctx0, Q, d_h, n_head, nq, n_windows);
-        K = ggml_reshape_4d(ctx0, K, d_h, n_head, nq, n_windows);
-        V = ggml_reshape_4d(ctx0, V, d_h, n_head, nq, n_windows);
+        Q = ggml_reshape_4d(ctx0, Q, d_h, n_head, nq, n_win_all);
+        K = ggml_reshape_4d(ctx0, K, d_h, n_head, nq, n_win_all);
+        V = ggml_reshape_4d(ctx0, V, d_h, n_head, nq, n_win_all);
 
         sa_out = build_attn(pl.o_w, pl.o_b, Q, K, V, nullptr, scale, bid);
-        sa_out = ggml_reshape_3d(ctx0, sa_out, n_embd, nq, n_windows);
+        sa_out = ggml_reshape_3d(ctx0, sa_out, n_embd, nq, n_win_all);
 
         sa_out = ggml_add(ctx0, sa_out, q);
         sa_out = build_norm(sa_out, pl.ln_1_w, pl.ln_1_b,
@@ -166,13 +191,13 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
         ggml_tensor * K = linear(e_in, pl.cross_attn_k_w, pl.cross_attn_k_b);
         ggml_tensor * V = linear(e_in, pl.cross_attn_v_w, pl.cross_attn_v_b);
 
-        Q = ggml_reshape_4d(ctx0, Q, d_h, n_head, nq, n_windows);
-        K = ggml_reshape_4d(ctx0, K, d_h, n_head, nkv, n_windows);
-        V = ggml_reshape_4d(ctx0, V, d_h, n_head, nkv, n_windows);
+        Q = ggml_reshape_4d(ctx0, Q, d_h, n_head, nq, n_win_all);
+        K = ggml_reshape_4d(ctx0, K, d_h, n_head, nkv, n_win_all);
+        V = ggml_reshape_4d(ctx0, V, d_h, n_head, nkv, n_win_all);
 
         ca_out = build_attn(pl.cross_attn_o_w, pl.cross_attn_o_b,
                             Q, K, V, nullptr, scale, bid);
-        ca_out = ggml_reshape_3d(ctx0, ca_out, n_embd, nq, n_windows);
+        ca_out = ggml_reshape_3d(ctx0, ca_out, n_embd, nq, n_win_all);
 
         ca_out = ggml_add(ctx0, ca_out, sa_out);
         ca_out = build_norm(ca_out, pl.cross_attn_norm_w, pl.cross_attn_norm_b,
@@ -183,13 +208,13 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
     // 6c. FFN
     ggml_tensor * ffn;
     {
-        ggml_tensor * t = ggml_reshape_2d(ctx0, ca_out, n_embd, query_len * n_windows);
+        ggml_tensor * t = ggml_reshape_2d(ctx0, ca_out, n_embd, query_len * n_win_all);
         t = build_mm(pl.ff_up_w, t);
         if (pl.ff_up_b) t = ggml_add(ctx0, t, pl.ff_up_b);
         t = ggml_gelu_erf(ctx0, t);
         t = build_mm(pl.ff_down_w, t);
         if (pl.ff_down_b) t = ggml_add(ctx0, t, pl.ff_down_b);
-        t = ggml_reshape_3d(ctx0, t, n_embd, query_len, n_windows);
+        t = ggml_reshape_3d(ctx0, t, n_embd, query_len, n_win_all);
         ffn = ggml_add(ctx0, t, ca_out);
         ffn = build_norm(ffn, pl.ln_2_w, pl.ln_2_b, NORM_TYPE_NORMAL, qformer_eps, bid);
     }
@@ -198,7 +223,7 @@ ggml_tensor * clip_graph_granite4_vision::build_block(
     // 7. _unwin back to raster
     ggml_tensor * unwinned;
     {
-        ggml_tensor * flat = ggml_reshape_2d(ctx0, ffn, n_embd, query_len * n_windows);
+        ggml_tensor * flat = ggml_reshape_3d(ctx0, ffn, n_embd, query_len * n_windows, n_tiles);
         unwinned = gather(flat,
             "g4v_blk" + std::to_string(bid) + "_unwin_idx",
             new_side * new_side);
@@ -244,13 +269,42 @@ ggml_tensor * clip_graph_granite4_vision::build_newline_row(ggml_context * ctx0)
     return ggml_reshape_2d(ctx0, nl_row_2d, n_mmproj_embd, 1);
 }
 
-// Append a single newline row at the end of the tile output.
-ggml_tensor * clip_graph_granite4_vision::append_rowwise_newlines(ggml_context * ctx0, ggml_tensor * tile_output) {
-    // For the single-tile case, append one newline row at the end.
-    // For the multi-tile rowwise case, this will be called per-tile
-    // (though currently only the single-tile path uses it).
-    ggml_tensor * nl_row = build_newline_row(ctx0);
-    return ggml_concat(ctx0, tile_output, nl_row, 1);
+// Assemble [overview, tile(0,0), tile(0,1), ...] into one token sequence:
+// the overview tokens first, then the tile grid read in raster order with one newline per row.
+// ref: https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/llava_next/modeling_llava_next.py#L266
+ggml_tensor * clip_graph_granite4_vision::build_anyres_assembly(ggml_tensor * cur, int out_side) {
+    const int n_dim  = cur->ne[0];
+    const int grid_x = anyres.grid_x;
+    const int grid_y = anyres.grid_y;
+    const int cur_w  = grid_x * out_side;
+    const int cur_h  = grid_y * out_side;
+    GGML_ASSERT(cur->ne[1] == out_side * out_side);
+    GGML_ASSERT(cur->ne[2] == 1 + grid_x * grid_y);
+
+    ggml_tensor * base = ggml_view_2d(ctx0, cur, n_dim, out_side * out_side, cur->nb[1], 0);
+
+    ggml_tensor * tiles = ggml_view_3d(ctx0, cur, n_dim, out_side * out_side, grid_x * grid_y,
+                                       cur->nb[1], cur->nb[2], cur->nb[2]);
+
+    // (n_dim*out_side, out_side, grid_x, grid_y) -> interleave the tiles of a grid row
+    tiles = ggml_reshape_4d(ctx0, tiles, n_dim * out_side, out_side, grid_x, grid_y);
+    tiles = ggml_cont(ctx0, ggml_permute(ctx0, tiles, 0, 2, 1, 3));
+    tiles = ggml_reshape_3d(ctx0, tiles, n_dim, cur_w, cur_h);
+
+    // drop the tokens that only cover the padding added when resizing to the grid
+    int off_x, off_y, w, h;
+    clip_anyres_unpad(cur_w, cur_h, anyres.orig_nx, anyres.orig_ny, off_x, off_y, w, h);
+    if (w != cur_w || h != cur_h) {
+        tiles = ggml_cont(ctx0, ggml_view_3d(ctx0, tiles, n_dim, w, h,
+                                             tiles->nb[1], tiles->nb[2],
+                                             off_x * tiles->nb[1] + off_y * tiles->nb[2]));
+    }
+
+    ggml_tensor * nl = ggml_repeat_4d(ctx0, build_newline_row(ctx0), n_dim, 1, h, 1);
+    tiles = ggml_concat(ctx0, tiles, nl, 1);
+    tiles = ggml_reshape_2d(ctx0, tiles, n_dim, (w + 1) * h);
+
+    return ggml_concat(ctx0, base, tiles, 1);
 }
 
 ggml_cgraph * clip_graph_granite4_vision::build() {
@@ -260,10 +314,12 @@ ggml_cgraph * clip_graph_granite4_vision::build() {
     GGML_ASSERT(!model.qf_proj_blocks.empty());
 
     // --- Stage 1a: SigLIP encoder producing intermediate hidden states ---
-    ggml_tensor * inp = build_inp();
+    ggml_tensor * inp = build_tile_inp();
     inp = ggml_add(ctx0, inp, model.position_embeddings);
     cb(inp, "pos_embed", -1);
 
+    const int tile_n_patches = tile_side * tile_side;
+
     ggml_tensor * inpL = inp;
     std::vector<ggml_tensor *> layer_outs(n_layer, nullptr);
 
@@ -281,12 +337,13 @@ ggml_cgraph * clip_graph_granite4_vision::build() {
         ggml_tensor * Vcur = build_mm(layer.v_w, cur);
         if (layer.v_b) Vcur = ggml_add(ctx0, Vcur, layer.v_b);
 
-        Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches);
-        Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches);
-        Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches);
+        Qcur = ggml_reshape_4d(ctx0, Qcur, d_head, n_head, tile_n_patches, n_tiles);
+        Kcur = ggml_reshape_4d(ctx0, Kcur, d_head, n_head, tile_n_patches, n_tiles);
+        Vcur = ggml_reshape_4d(ctx0, Vcur, d_head, n_head, tile_n_patches, n_tiles);
 
         cur = build_attn(layer.o_w, layer.o_b,
                          Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+        cur = ggml_reshape_3d(ctx0, cur, n_embd, tile_n_patches, n_tiles);
 
         cur = ggml_add(ctx0, cur, inpL);
         inpL = cur;
@@ -318,7 +375,7 @@ ggml_cgraph * clip_graph_granite4_vision::build() {
         ggml_tensor * stream = build_block(
             blk, h, bid,
             hparams.proj_spatial_offsets[bid],
-            n_patches_x,
+            tile_side,
             hparams.downsample_window_side,
             hparams.downsample_query_side,
             qformer_eps);
@@ -326,10 +383,11 @@ ggml_cgraph * clip_graph_granite4_vision::build() {
         mmproj = mmproj ? ggml_concat(ctx0, mmproj, stream, 0) : stream;
     }
 
-    // --- Stage 1d: Append newline tokens if add_newline is set ---
-    if (add_newline) {
-        mmproj = append_rowwise_newlines(ctx0, mmproj);
-        ggml_set_name(mmproj, "g4v_mmproj_out_nl");
+    // --- Stage 1d: assemble the tiles and weave in the newline tokens ---
+    if (anyres.is_tiled()) {
+        const int out_side = tile_side / hparams.downsample_window_side * hparams.downsample_query_side;
+        mmproj = build_anyres_assembly(mmproj, out_side);
+        ggml_set_name(mmproj, "g4v_mmproj_out_anyres");
     } else {
         ggml_set_name(mmproj, "g4v_mmproj_out");
     }

tools/mtmd/models/models.h

@@ -402,16 +402,19 @@ struct clip_graph_exaone4_5 : clip_graph {
 struct clip_graph_granite4_vision : clip_graph {
     clip_graph_granite4_vision(clip_ctx * ctx, const clip_image_f32 & img)
         : clip_graph(ctx, img),
-          add_newline(img.add_newline) {}
+          anyres(img.anyres),
+          n_tiles(img.ny() / img.nx()),
+          tile_side(img.nx() / patch_size) {}
 
     ggml_cgraph * build() override;
 
 private:
-    // The graph is per-tile since only batch-size 1 is supported in clip. As
-    // such, this value is set at construct time based on the tile that will be
-    // encoded, then used during build to determine how to handle newlines.
-    const bool add_newline;
+    // the input image is a stack of tiles on the Y axis: [overview, tile(0,0), tile(0,1), ...]
+    const clip_image_f32::anyres_info anyres;
+    const int n_tiles;
+    const int tile_side; // patches per tile side
 
+    ggml_tensor * build_tile_inp();
     ggml_tensor * gather(ggml_tensor * src, const std::string & name, int idx_len);
     ggml_tensor * interp_down(ggml_tensor * src, int side, int new_side);
     ggml_tensor * build_block(const qf_block & blk, ggml_tensor * h, int bid,
@@ -419,7 +422,7 @@ private:
                               int query_side, float qformer_eps);
 
     ggml_tensor * build_newline_row(ggml_context * ctx0);
-    ggml_tensor * append_rowwise_newlines(ggml_context * ctx0, ggml_tensor * tile_output);
+    ggml_tensor * build_anyres_assembly(ggml_tensor * cur, int out_side);
 };
 
 struct clip_graph_muse_glimmer : clip_graph {

tools/mtmd/mtmd-helper-common.h

@@ -82,7 +82,7 @@ struct decode_embd_batch {
     llama_batch batch;
     decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) {
         GGML_ASSERT(n_tokens > 0 && n_pos_per_embd > 0 && n_mmproj_embd > 0);
-        pos     .resize(n_tokens * n_pos_per_embd);
+        pos     .resize((size_t) n_tokens * (size_t) n_pos_per_embd);
         n_seq_id.resize(n_tokens);
         seq_ids .resize(n_tokens + 1);
         logits  .resize(n_tokens);
@@ -115,10 +115,12 @@ struct decode_embd_batch {
         GGML_ASSERT(!rel_pos.empty() && (int32_t)rel_pos.size() == batch.n_tokens);
         seq_id_0[0] = seq_id;
         for (int32_t i = 0; i < batch.n_tokens; i++) {
-            pos[i                     ] = rel_pos[i].t;
-            pos[i + batch.n_tokens    ] = rel_pos[i].y;
-            pos[i + batch.n_tokens * 2] = rel_pos[i].x;
-            pos[i + batch.n_tokens * 3] = rel_pos[i].z;
+            const size_t idx = (size_t) i;
+            const size_t n_tokens = (size_t) batch.n_tokens;
+            pos[idx                    ] = rel_pos[i].t;
+            pos[idx + n_tokens         ] = rel_pos[i].y;
+            pos[idx + n_tokens * 2     ] = rel_pos[i].x;
+            pos[idx + n_tokens * 3     ] = rel_pos[i].z;
         }
         for (int i = 0; i < batch.n_tokens; i++) {
             batch.n_seq_id[i] = 1;
@@ -132,10 +134,12 @@ struct decode_embd_batch {
         GGML_ASSERT(n_pos_per_embd == 4);
         seq_id_0[0] = seq_id;
         for (int i = 0; i < batch.n_tokens; i++) {
-            pos[i                     ] = pos_0 + i;
-            pos[i + batch.n_tokens    ] = pos_0 + i;
-            pos[i + batch.n_tokens * 2] = pos_0 + i;
-            pos[i + batch.n_tokens * 3] = pos_0 + i;
+            const size_t idx = (size_t) i;
+            const size_t n_tokens = (size_t) batch.n_tokens;
+            pos[idx                    ] = pos_0 + i;
+            pos[idx + n_tokens         ] = pos_0 + i;
+            pos[idx + n_tokens * 2     ] = pos_0 + i;
+            pos[idx + n_tokens * 3     ] = pos_0 + i;
         }
         for (int i = 0; i < batch.n_tokens; i++) {
             batch.n_seq_id[i] = 1;
@@ -148,7 +152,7 @@ struct decode_embd_batch {
         GGML_ASSERT(offset >= 0 && n_tokens > 0 && offset + n_tokens <= batch.n_tokens);
         llama_pos * pos_ptr;
         pos_view.clear();
-        pos_view.reserve(n_tokens * n_pos_per_embd);
+        pos_view.reserve((size_t) n_tokens * (size_t) n_pos_per_embd);
         if (n_pos_per_embd > 1) {
             // mrope
             // for example, with layout of src: 1234...1234...1234...1234...
@@ -157,7 +161,7 @@ struct decode_embd_batch {
                 // assume n_tokens is less than or equal to batch.n_tokens
                 // batch.n_tokens is number of **total** tokens
                 // n_tokens is number of viewed token
-                size_t src_idx = i * batch.n_tokens + offset;
+                size_t src_idx = (size_t) i * (size_t) batch.n_tokens + (size_t) offset;
                 pos_view.insert(pos_view.end(),
                     pos.data() + src_idx,
                     pos.data() + src_idx + n_tokens);

tools/mtmd/mtmd-image.cpp

@@ -1317,7 +1317,7 @@ void mtmd_image_preprocessor_step3vl::img_u8_resize_bilinear_to_f32(
     const float scale_x = static_cast<float>(src_size.width)  / target_width;
     const float scale_y = static_cast<float>(src_size.height) / target_height;
 
-    std::vector<float> local_buf(3 * target_width * target_height);
+    std::vector<float> local_buf((size_t) 3 * (size_t) target_width * (size_t) target_height);
 
     for (int y = 0; y < target_height; ++y) {
         const float src_y = (static_cast<float>(y) + 0.5f) * scale_y - 0.5f;
@@ -1338,7 +1338,7 @@ void mtmd_image_preprocessor_step3vl::img_u8_resize_bilinear_to_f32(
             const auto p10 = src.get_pixel(x0, y1);
             const auto p11 = src.get_pixel(x1, y1);
 
-            const size_t idx_dst = 3 * (y * target_width + x);
+            const size_t idx_dst = (size_t) 3 * ((size_t) y * (size_t) target_width + (size_t) x);
             for (int c = 0; c < 3; ++c) {
                 const float v00 = (static_cast<float>(p00[c]) / 255.0f - mean[c]) / std[c];
                 const float v01 = (static_cast<float>(p01[c]) / 255.0f - mean[c]) / std[c];
@@ -1602,17 +1602,50 @@ mtmd_image_preproc_out mtmd_image_preprocessor_youtuvl::preprocess(const clip_im
 }
 
 mtmd_image_preproc_out mtmd_image_preprocessor_granite::preprocess(const clip_image_u8 & img) {
-    auto output = mtmd_image_preprocessor_llava_uhd::preprocess(img);
-    if (output.entries.size() == 0) {
-        // Single-tile (overview only): append one newline row.
-        output.overview.add_newline = true;
-    } else {
-        // Multi-tile: overview gets no newline, grid tiles get one.
-        output.overview.add_newline = false;
-        for (size_t i = 0; i < output.entries.size(); ++i) {
-            output.entries[i].add_newline = true;
+    GGML_ASSERT(!hparams.image_res_candidates.empty());
+
+    const clip_image_size orig_size = img.get_size();
+    const int             tile_size = hparams.image_size;
+
+    // llava-next always encodes an overview plus a grid of tiles, even for small images
+    const clip_image_size refined_size = select_best_resolution(orig_size, hparams.image_res_candidates);
+    const int             grid_x       = refined_size.width  / tile_size;
+    const int             grid_y       = refined_size.height / tile_size;
+
+    clip_image_u8 overview;
+    img_tool::resize(img, overview, {tile_size, tile_size}, hparams.image_resize_algo_ov,
+                        hparams.image_pad_ov, hparams.image_pad_color_ov);
+
+    clip_image_u8 refined;
+    img_tool::resize(img, refined, refined_size, hparams.image_resize_algo_rf,
+                        hparams.image_pad_rf, hparams.image_pad_color_rf);
+
+    // stack the overview and the tiles on the Y axis, so the whole grid goes through one graph
+    clip_image_u8 stacked;
+    stacked.set_size({tile_size, tile_size * (1 + grid_x * grid_y)}, false);
+    auto copy_tile = [&](const clip_image_u8 & src, int src_x, int src_y, int dst_idx) {
+        for (int py = 0; py < tile_size; py++) {
+            for (int px = 0; px < tile_size; px++) {
+                stacked.set_pixel(px, dst_idx * tile_size + py, src.get_pixel(src_x + px, src_y + py));
+            }
+        }
+    };
+    copy_tile(overview, 0, 0, 0);
+    for (int ty = 0; ty < grid_y; ty++) {
+        for (int tx = 0; tx < grid_x; tx++) {
+            copy_tile(refined, tx * tile_size, ty * tile_size, 1 + ty * grid_x + tx);
         }
     }
+
+    LOG_DBG("%s: grid size: %d x %d (%d tiles) + overview\n", __func__, grid_x, grid_y, grid_x * grid_y);
+
+    mtmd_image_preproc_out output;
+    output.append(hparams, stacked, true);
+    auto & entry = output.entries.back();
+    entry.anyres.grid_x  = grid_x;
+    entry.anyres.grid_y  = grid_y;
+    entry.anyres.orig_nx = orig_size.width;
+    entry.anyres.orig_ny = orig_size.height;
     return output;
 }
 

tools/mtmd/mtmd-image.h

@@ -85,9 +85,6 @@ struct mtmd_image_preprocessor_llava_uhd : mtmd_image_preprocessor {
 protected:
     clip_image_size get_best_resize(const clip_image_size & original_size, int scale_resolution, int patch_size, bool allow_upscale = false);
 
-private:
-    clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max);
-
     /**
      * Selects the best resolution from a list of possible resolutions based on the original size.
      *
@@ -104,6 +101,9 @@ private:
      * @return The best fit resolution
      */
     clip_image_size select_best_resolution(const clip_image_size & original_size, const std::vector<clip_image_size> & possible_resolutions);
+
+private:
+    clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max);
     int ensure_divide(int length, int patch_size);
     clip_image_size get_refine_size(const clip_image_size & original_size, const clip_image_size & grid, int scale_resolution, int patch_size, bool allow_upscale = false);
     clip_image_size get_best_grid(const int max_slice_nums, const int multiple, const float log_ratio);
@@ -225,7 +225,7 @@ struct mtmd_image_preprocessor_youtuvl : mtmd_image_preprocessor {
     mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;
 };
 
-// similar to llava_uhd, but has add_newline
+// llava-next "anyres": stacks the overview and all tiles into one image, assembled by clip in a single graph
 struct mtmd_image_preprocessor_granite : mtmd_image_preprocessor_llava_uhd {
     mtmd_image_preprocessor_granite(const clip_ctx * ctx) : mtmd_image_preprocessor_llava_uhd(ctx) {}
     mtmd_image_preproc_out preprocess(const clip_image_u8 & img) override;

tools/mtmd/mtmd.cpp

@@ -891,10 +891,10 @@ struct mtmd_context {
                 } break;
             case PROJECTOR_TYPE_GRANITE4_VISION:
                 {
-                    img_beg = "<image>";
-                    img_end = "";
+                    // ... (image embeddings) \n ...
+                    img_beg = "";
+                    img_end = "\n";
                     image_preproc = std::make_unique<mtmd_image_preprocessor_granite>(ctx_v);
-                    ov_img_first = true;
                 } break;
             default:
                 throw std::runtime_error(string_format("%s: unexpected vision projector type %d\n", __func__, proj));

tools/server/README.md

@@ -226,6 +226,7 @@ For the full list of features, please refer to [server's changelog](https://gith
 | `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `-rea, --reasoning [on\|off\|auto]` | Use reasoning/thinking in the chat ('on', 'off', or 'auto', default: 'auto' (detect from template))<br/>(env: LLAMA_ARG_REASONING) |
+| `--reasoning-effort LEVEL` | reasoning effort level given to the chat template: 'default' to keep the template default,<br/>or a level such as 'minimal', 'low', 'medium', 'high', 'xhigh' or 'max' (default: default)<br/>(env: LLAMA_ARG_REASONING_EFFORT) |
 | `--reasoning-budget N` | token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--reasoning-budget-message MESSAGE` | message injected before the end-of-thinking tag when reasoning budget is exhausted (default: none)<br/>(env: LLAMA_ARG_THINK_BUDGET_MESSAGE) |
 | `--reasoning-preserve, --no-reasoning-preserve` | preserve reasoning trace in the full history, not just the last assistant message (default: template default)<br/>compatible with certain templates having 'supports_preserve_reasoning' capability<br/>example: https://docs.z.ai/guides/capabilities/thinking-mode#preserved-thinking<br/>(env: LLAMA_ARG_REASONING_PRESERVE) |
@@ -295,10 +296,17 @@ For the full list of features, please refer to [server's changelog](https://gith
 
 Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.
 
-For boolean options like `--mmap` or `--kv-offload`, the environment variable is handled as shown in this example:
-- `LLAMA_ARG_MMAP=true` means enabled, other accepted values are: `1`, `on`, `enabled`
-- `LLAMA_ARG_MMAP=false` means disabled, other accepted values are: `0`, `off`, `disabled`
-- If `LLAMA_ARG_NO_MMAP` is present (no matter the value), it means disabling mmap
+For string options like `--load-mode`, the environment variable is handled as shown in this example:
+- `LLAMA_ARG_LOAD_MODE=auto` sets the loading mode to auto (default)
+- `LLAMA_ARG_LOAD_MODE=none` disables special loading
+- `LLAMA_ARG_LOAD_MODE=mmap` enables memory-mapping
+- `LLAMA_ARG_LOAD_MODE=mlock` locks the model in RAM
+- `LLAMA_ARG_LOAD_MODE=mmap+mlock` enables memory-mapping and locks in RAM
+- `LLAMA_ARG_LOAD_MODE=dio` uses DirectIO if available
+
+For boolean options like `--kv-offload`:
+- `LLAMA_ARG_KV_OFFLOAD=true` means enabled, other accepted values are: `1`, `on`, `enabled`
+- `LLAMA_ARG_KV_OFFLOAD=false` means disabled, other accepted values are: `0`, `off`, `disabled`
 
 Example usage of docker compose with environment variables:
 
@@ -1250,7 +1258,7 @@ The `response_format` parameter supports both plain JSON output (e.g. `{"type":
 
 `chat_template_kwargs`: Allows sending additional parameters to the json templating system. For example: `{"enable_thinking": false}`
 
-`reasoning_effort`: If set to `none`, reasoning will be disabled for this request. Other values (e.g., `low`, `max`) have no effect on reasoning.
+`reasoning_effort`: If `none`, reasoning/thinking is disabled. Otherwise, the value is made available to the jinja template.
 
 `reasoning_format`: The reasoning format to be parsed. If set to `none`, it will output the raw generated text.
 
@@ -1892,7 +1900,7 @@ Example events:
 }
 // note for "loading" status:
 // - subsequent events will follow the same order of "stages" list
-// - mmap is may report incorrect progress on some platforms; if you need exact progress, use --no-mmap
+// - mmap may report incorrect progress on some platforms; if you need exact progress, use --load-mode none
 
 {
   "model": "...",

tools/server/server-common.cpp

@@ -1292,12 +1292,15 @@ json oaicompat_chat_params_parse(
         throw std::invalid_argument("invalid type for \"enable_thinking\" (expected boolean, got string)");
     }
 
-    // Parse also the OAI "reasoning_effort": "none" specific value
+    // Parse the OAI "reasoning_effort" field; "none" disables reasoning.
     if (body.contains("reasoning_effort")) {
         auto reasoning_effort = json_value(body, "reasoning_effort", std::string(""));
         if (reasoning_effort == "none") {
             inputs.enable_thinking = false;
-        } // other reasoning_effort values are model-specific and not yet handled
+            inputs.chat_template_kwargs.erase("reasoning_effort");
+        } else if (!reasoning_effort.empty()) {
+            inputs.chat_template_kwargs["reasoning_effort"] = json(reasoning_effort).dump();
+        }
     }
 
     inputs.force_pure_content = opt.force_pure_content;