model: support for mistral-small in the ollama runner

Mistral is a popular research lab making open source models. This updates
the forward pass of llama architecture models to support both llama models
and mistral models by accounting for additional metadata present in mistral
models, and finding the correct dimensions for the output projection.

convert/convert.go

@@ -182,8 +182,10 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
 
 	var conv ModelConverter
 	switch p.Architectures[0] {
-	case "LlamaForCausalLM", "MistralForCausalLM":
+	case "LlamaForCausalLM":
 		conv = &llamaModel{}
+	case "Mistral3ForConditionalGeneration":
+		conv = &mistral3Model{}
 	case "MixtralForCausalLM":
 		conv = &mixtralModel{}
 	case "GemmaForCausalLM":

convert/convert_mistral.go

+package convert
+
+import (
+	"cmp"
+	"fmt"
+	"strings"
+
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+
+	"github.com/ollama/ollama/fs/ggml"
+)
+
+type mistral3Model struct {
+	ModelParameters
+	ImageTokenIndex    uint32 `json:"image_token_index"`
+	SpatialMergeSize   uint32 `json:"spatial_merge_size"`
+	VisionFeatureLayer int32  `json:"vision_feature_layer"`
+	TextModel          struct {
+		NumHiddenLayers       uint32  `json:"num_hidden_layers"`
+		MaxPositionEmbeddings uint32  `json:"max_position_embeddings"`
+		HiddenSize            uint32  `json:"hidden_size"`
+		IntermediateSize      uint32  `json:"intermediate_size"`
+		NumAttentionHeads     uint32  `json:"num_attention_heads"`
+		NumKeyValueHeads      uint32  `json:"num_key_value_heads"`
+		RopeTheta             float32 `json:"rope_theta"`
+		RMSNormEPS            float32 `json:"rms_norm_eps"`
+		HeadDim               uint32  `json:"head_dim"`
+		SlidingWindow         *uint32 `json:"sliding_window"`
+		HiddenAct             string  `json:"hidden_act"`
+		VocabSize             uint32  `json:"vocab_size"`
+	} `json:"text_config"`
+	VisionModel struct {
+		NumAttentionHeads uint32  `json:"num_attention_heads"`
+		NumHiddenLayers   uint32  `json:"num_hidden_layers"`
+		HiddenSize        uint32  `json:"hidden_size"`
+		IntermediateSize  uint32  `json:"intermediate_size"`
+		ImageSize         uint32  `json:"image_size"`
+		NumChannels       uint32  `json:"num_channels"`
+		PatchSize         uint32  `json:"patch_size"`
+		HeadDim           uint32  `json:"head_dim"`
+		HiddenAct         string  `json:"hidden_act"`
+		RopeTheta         float32 `json:"rope_theta"`
+	} `json:"vision_config"`
+	MultiModalProjectorBias bool   `json:"multimodal_projector_bias"`
+	ProjectorHiddenAct      string `json:"projector_hidden_act"`
+}
+
+func (p *mistral3Model) KV(t *Tokenizer) ggml.KV {
+	kv := p.ModelParameters.KV(t)
+	kv["general.architecture"] = "mistral3"
+	kv["mistral3.vocab_size"] = p.TextModel.VocabSize
+
+	// Text configuration
+	kv["mistral3.block_count"] = p.TextModel.NumHiddenLayers
+	kv["mistral3.context_length"] = p.TextModel.MaxPositionEmbeddings
+	kv["mistral3.embedding_length"] = p.TextModel.HiddenSize
+	kv["mistral3.feed_forward_length"] = p.TextModel.IntermediateSize
+	kv["mistral3.attention.head_count"] = p.TextModel.NumAttentionHeads
+	kv["mistral3.attention.head_count_kv"] = p.TextModel.NumKeyValueHeads
+	kv["mistral3.attention.layer_norm_rms_epsilon"] = p.TextModel.RMSNormEPS
+	kv["mistral3.attention.key_length"] = p.TextModel.HeadDim
+	kv["mistral3.attention.value_length"] = p.TextModel.HeadDim
+	kv["mistral3.rope.dimension_count"] = p.TextModel.HiddenSize / p.TextModel.NumHiddenLayers
+	kv["mistral3.rope.freq_base"] = p.TextModel.RopeTheta
+
+	// Vision configuration
+	kv["mistral3.vision.block_count"] = p.VisionModel.NumHiddenLayers
+	kv["mistral3.vision.embedding_length"] = p.VisionModel.HiddenSize
+	kv["mistral3.vision.feed_forward_length"] = p.VisionModel.IntermediateSize
+	kv["mistral3.vision.attention.head_count"] = p.VisionModel.NumAttentionHeads
+	kv["mistral3.vision.attention.key_length"] = p.VisionModel.HeadDim
+	kv["mistral3.vision.image_size"] = p.VisionModel.ImageSize
+	kv["mistral3.vision.patch_size"] = p.VisionModel.PatchSize
+	kv["mistral3.vision.num_channels"] = p.VisionModel.NumChannels
+	// kv["mistral3.vision.attention.layer_norm_epsilon"] = 1e-05 // Default value
+	kv["mistral3.vision.rope.freq_base"] = p.VisionModel.RopeTheta
+
+	// Multimodal configuration
+	kv["mistral3.image_token_index"] = p.ImageTokenIndex
+	kv["mistral3.spatial_merge_size"] = p.SpatialMergeSize
+
+	kv["mistral3.mm.projector_bias"] = p.MultiModalProjectorBias
+
+	if p.ProjectorHiddenAct != "" {
+		kv["mistral3.mm.projector_hidden_act"] = p.ProjectorHiddenAct
+	}
+
+	return kv
+}
+
+func (p *mistral3Model) Tensors(ts []Tensor) []ggml.Tensor {
+	var out []ggml.Tensor
+
+	for _, t := range ts {
+		if !strings.HasPrefix(t.Name(), "v.") {
+			if strings.HasSuffix(t.Name(), ".attn_q.weight") ||
+				strings.HasSuffix(t.Name(), ".attn_k.weight") {
+				t.SetRepacker(p.repack)
+			}
+		}
+
+		out = append(out, ggml.Tensor{
+			Name:     t.Name(),
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (p *mistral3Model) Replacements() []string {
+	return []string{
+		"language_model.model.norm", "output_norm",
+		"language_model.model.", "",
+		"language_model.", "",
+		"layers", "blk",
+		"transformer.layers", "blk",
+		"vision_tower", "v",
+		"ln_pre", "encoder_norm",
+		"input_layernorm", "attn_norm",
+		"post_attention_layernorm", "ffn_norm",
+		"embed_tokens", "token_embd",
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_output",
+		"mlp.down_proj", "ffn_down",
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.up_proj", "ffn_up",
+		"attention.q_proj", "attn_q",
+		"attention.k_proj", "attn_k",
+		"attention.v_proj", "attn_v",
+		"attention.o_proj", "attn_output",
+		"attention_norm", "attn_norm",
+		"feed_forward.gate_proj", "ffn_gate",
+		"feed_forward.down_proj", "ffn_down",
+		"feed_forward.up_proj", "ffn_up",
+		"multi_modal_projector", "mm",
+		"ffn_norm", "ffn_norm",
+		"lm_head", "output",
+	}
+}
+
+func (p *mistral3Model) repack(name string, data []float32, shape []uint64) ([]float32, error) {
+	var dims []int
+	for _, dim := range shape {
+		dims = append(dims, int(dim))
+	}
+
+	var heads uint32
+	if strings.HasSuffix(name, ".attn_q.weight") {
+		heads = p.TextModel.NumAttentionHeads
+	} else if strings.HasSuffix(name, ".attn_k.weight") {
+		heads = cmp.Or(p.TextModel.NumKeyValueHeads, p.TextModel.NumAttentionHeads)
+	} else {
+		return nil, fmt.Errorf("unknown tensor for repack: %s", name)
+	}
+
+	n := tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+	if err := n.Reshape(append([]int{int(heads), 2, dims[0] / int(heads) / 2}, dims[1:]...)...); err != nil {
+		return nil, err
+	}
+
+	if err := n.T(0, 2, 1, 3); err != nil {
+		return nil, err
+	}
+
+	if err := n.Reshape(dims...); err != nil {
+		return nil, err
+	}
+
+	if err := n.Transpose(); err != nil {
+		return nil, err
+	}
+
+	ts, err := native.SelectF32(n, 1)
+	if err != nil {
+		return nil, err
+	}
+
+	var f32s []float32
+	for _, t := range ts {
+		f32s = append(f32s, t...)
+	}
+
+	return f32s, nil
+}

convert/reader.go

@@ -62,10 +62,7 @@ func parseTensors(fsys fs.FS, replacer *strings.Replacer) ([]Tensor, error) {
 		Pattern string
 		Func    func(fs.FS, *strings.Replacer, ...string) ([]Tensor, error)
 	}{
-		{"model-*-of-*.safetensors", parseSafetensors},
-		{"model.safetensors", parseSafetensors},
-		{"adapters.safetensors", parseSafetensors},
-		{"adapter_model.safetensors", parseSafetensors},
+		{"*.safetensors", parseSafetensors},
 		{"pytorch_model-*-of-*.bin", parseTorch},
 		{"pytorch_model.bin", parseTorch},
 		{"consolidated.*.pth", parseTorch},

fs/ggml/ggml.go

@@ -134,7 +134,10 @@ func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 {
 }
 
 func (kv KV) OllamaEngineRequired() bool {
-	return kv.Architecture() == "gemma3"
+	return slices.Contains([]string{
+		"gemma3",
+		"mistral3",
+	}, kv.Architecture())
 }
 
 func keyValue[T string | uint32 | uint64 | float32 | *array | bool](kv KV, key string, defaultValue ...T) T {
@@ -638,7 +641,7 @@ func (llm GGML) VisionGraphSize() (weights, graphSize uint64) {
 			embeddingLength*numPatches*maxNumTiles +
 			9*embeddingLength*numPaddedPatches*maxNumTiles +
 			numPaddedPatches*maxNumTiles*numPaddedPatches*maxNumTiles*headCount)
-	case "gemma3":
+	case "gemma3", "mistral3":
 		graphSize = 4 * (imageSize*imageSize*numChannels +
 			embeddingLength*patchSize +
 			numPatches*numPatches*headCount)

kvcache/causal_test.go

@@ -484,6 +484,14 @@ func (t *testTensor) Floats() []float32 {
 	return out
 }
 
+func (t *testTensor) Neg(ctx ml.Context) ml.Tensor {
+	out := ctx.Empty(t.DType(), t.Shape()...).(*testTensor)
+	for i := range out.data {
+		out.data[i] = -t.data[i]
+	}
+	return out
+}
+
 func (t *testTensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	out := ctx.Empty(t.DType(), t.Shape()...).(*testTensor)
 
@@ -538,17 +546,15 @@ func (t *testTensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, di
 	panic("not implemented")
 }
 
-func (t *testTensor) Tanh(ctx ml.Context) ml.Tensor {
+func (t *testTensor) IM2Col(ctx ml.Context, weight ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
 	panic("not implemented")
 }
 
-func (t *testTensor) GELU(ctx ml.Context) ml.Tensor {
-	panic("not implemented")
-}
-
-func (t *testTensor) SILU(ctx ml.Context) ml.Tensor {
-	panic("not implemented")
-}
+func (t *testTensor) Cos(ctx ml.Context) ml.Tensor  { panic("not implemented") }
+func (t *testTensor) Sin(ctx ml.Context) ml.Tensor  { panic("not implemented") }
+func (t *testTensor) Tanh(ctx ml.Context) ml.Tensor { panic("not implemented") }
+func (t *testTensor) GELU(ctx ml.Context) ml.Tensor { panic("not implemented") }
+func (t *testTensor) SILU(ctx ml.Context) ml.Tensor { panic("not implemented") }
 
 func (t *testTensor) Reshape(ctx ml.Context, shape ...int) ml.Tensor {
 	panic("not implemented")
@@ -600,6 +606,8 @@ func (t *testTensor) Stack(ctx ml.Context, dim int, s ...ml.Tensor) ml.Tensor {
 	panic("not implemented")
 }
 
+func (t *testTensor) Repeat(ctx ml.Context, dim, n int) ml.Tensor { panic("not implemented") }
+
 func (t *testTensor) Concat(ctx ml.Context, t2 ml.Tensor, dim int) ml.Tensor {
 	panic("not implemented")
 }
@@ -612,3 +620,5 @@ func (t *testTensor) Copy(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	copy(t2.(*testTensor).data, t.data)
 	return nil
 }
+
+func (t *testTensor) Duplicate(ctx ml.Context) ml.Tensor { panic("not implemented") }

llama/llama.cpp/src/llama-arch.cpp

@@ -65,6 +65,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_CHAMELEON,        "chameleon"        },
     { LLM_ARCH_SOLAR,            "solar"            },
     { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
+    { LLM_ARCH_MISTRAL3,         "mistral3"         },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -1371,6 +1372,22 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_POS_NET_ATTN_OUT,  "posnet.%d.attn_output" },
         },
     },
+    {
+        LLM_ARCH_MISTRAL3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,  "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,   "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,      "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,      "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,      "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,    "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,    "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,    "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_UP,      "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_DOWN,    "blk.%d.ffn_down" },
+        }
+    },
     {
         LLM_ARCH_UNKNOWN,
         {

llama/llama.cpp/src/llama-arch.h

@@ -69,6 +69,7 @@ enum llm_arch {
     LLM_ARCH_CHAMELEON,
     LLM_ARCH_SOLAR,
     LLM_ARCH_WAVTOKENIZER_DEC,
+    LLM_ARCH_MISTRAL3,
     LLM_ARCH_UNKNOWN,
 };
 

llama/llama.cpp/src/llama-model.cpp

@@ -1277,6 +1277,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
             } break;
+        case LLM_ARCH_MISTRAL3: break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -3537,6 +3538,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
                     output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {n_embd}, 0);
                 } break;
+            case LLM_ARCH_MISTRAL3: break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -4015,6 +4017,7 @@ enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
         case LLM_ARCH_GRANITE_MOE:
         case LLM_ARCH_CHAMELEON:
         case LLM_ARCH_SOLAR:
+        case LLM_ARCH_MISTRAL3:
             return LLAMA_ROPE_TYPE_NORM;
 
         // the pairs of head values are offset by n_rot/2

llama/llama.cpp/src/llama-quant.cpp

@@ -738,13 +738,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
         bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
 
         // don't quantize vision stuff
-        quantize &= name.find("v.blk.") == std::string::npos;
-
-        quantize &= name.find("mm.mm_input_projection.weight") == std::string::npos;
-        quantize &= name.find("mm.mm_soft_emb_norm.weight") == std::string::npos;
-        quantize &= name.find("v.patch_embedding.weight") == std::string::npos;
-        quantize &= name.find("v.position_embedding.weight") == std::string::npos;
-        quantize &= name.find("v.post_layernorm.weight") == std::string::npos;
+        quantize &= name.find("v.") == std::string::npos;
+        quantize &= name.find("mm.") == std::string::npos;
 
         // quantize only 2D and 3D tensors (experts)
         quantize &= (ggml_n_dims(tensor) >= 2);

llama/patches/0021-gemma3-quantization.patch → llama/patches/0021-add-model-quantizations.patch

 From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
 From: Patrick Devine <patrick@infrahq.com>
 Date: Fri, 14 Mar 2025 16:33:23 -0700
-Subject: [PATCH] gemma3 quantization
+Subject: [PATCH] add model quantizations
 
+- gemma3
+- mistral3
 ---
- src/llama-arch.cpp  | 19 +++++++++++++++++++
- src/llama-arch.h    |  1 +
- src/llama-model.cpp |  7 +++++++
- src/llama-quant.cpp |  9 +++++++++
- 4 files changed, 36 insertions(+)
+ src/llama-arch.cpp  | 36 ++++++++++++++++++++++++++++++++++++
+ src/llama-arch.h    |  2 ++
+ src/llama-model.cpp | 10 ++++++++++
+ src/llama-quant.cpp |  4 ++++
+ 4 files changed, 52 insertions(+)
 
 diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
-index b6f20286..b443fcd3 100644
+index b6f20286..13a0a988 100644
 --- a/src/llama-arch.cpp
 +++ b/src/llama-arch.cpp
 @@ -37,6 +37,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
@@ -22,7 +24,15 @@ index b6f20286..b443fcd3 100644
      { LLM_ARCH_STARCODER2,       "starcoder2"       },
      { LLM_ARCH_MAMBA,            "mamba"            },
      { LLM_ARCH_XVERSE,           "xverse"           },
-@@ -804,6 +805,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
+@@ -64,6 +65,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+     { LLM_ARCH_CHAMELEON,        "chameleon"        },
+     { LLM_ARCH_SOLAR,            "solar"            },
+     { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
++    { LLM_ARCH_MISTRAL3,         "mistral3"         },
+     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
+ };
+ 
+@@ -804,6 +806,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
              { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
          },
      },
@@ -47,8 +57,31 @@ index b6f20286..b443fcd3 100644
      {
          LLM_ARCH_STARCODER2,
          {
+@@ -1352,6 +1372,22 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
+             { LLM_TENSOR_POS_NET_ATTN_OUT,  "posnet.%d.attn_output" },
+         },
+     },
++    {
++        LLM_ARCH_MISTRAL3,
++        {
++            { LLM_TENSOR_TOKEN_EMBD,  "token_embd" },
++            { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
++            { LLM_TENSOR_ATTN_NORM,   "blk.%d.attn_norm" },
++            { LLM_TENSOR_ATTN_Q,      "blk.%d.attn_q" },
++            { LLM_TENSOR_ATTN_K,      "blk.%d.attn_k" },
++            { LLM_TENSOR_ATTN_V,      "blk.%d.attn_v" },
++            { LLM_TENSOR_ATTN_OUT,    "blk.%d.attn_output" },
++            { LLM_TENSOR_FFN_NORM,    "blk.%d.ffn_norm" },
++            { LLM_TENSOR_FFN_GATE,    "blk.%d.ffn_gate" },
++            { LLM_TENSOR_FFN_UP,      "blk.%d.ffn_up" },
++            { LLM_TENSOR_FFN_DOWN,    "blk.%d.ffn_down" },
++        }
++    },
+     {
+         LLM_ARCH_UNKNOWN,
+         {
 diff --git a/src/llama-arch.h b/src/llama-arch.h
-index ec742224..aad92a5d 100644
+index ec742224..8476ae0a 100644
 --- a/src/llama-arch.h
 +++ b/src/llama-arch.h
 @@ -41,6 +41,7 @@ enum llm_arch {
@@ -59,8 +92,16 @@ index ec742224..aad92a5d 100644
      LLM_ARCH_STARCODER2,
      LLM_ARCH_MAMBA,
      LLM_ARCH_XVERSE,
+@@ -68,6 +69,7 @@ enum llm_arch {
+     LLM_ARCH_CHAMELEON,
+     LLM_ARCH_SOLAR,
+     LLM_ARCH_WAVTOKENIZER_DEC,
++    LLM_ARCH_MISTRAL3,
+     LLM_ARCH_UNKNOWN,
+ };
+ 
 diff --git a/src/llama-model.cpp b/src/llama-model.cpp
-index ab1a07d1..70183041 100644
+index ab1a07d1..db4f2685 100644
 --- a/src/llama-model.cpp
 +++ b/src/llama-model.cpp
 @@ -878,6 +878,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
@@ -73,7 +114,15 @@ index ab1a07d1..70183041 100644
          case LLM_ARCH_STARCODER2:
              {
                  ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
-@@ -2537,6 +2540,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
+@@ -1274,6 +1277,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
+                 ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
+                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
+             } break;
++        case LLM_ARCH_MISTRAL3: break;
+         default: throw std::runtime_error("unsupported model architecture");
+     }
+ 
+@@ -2537,6 +2541,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                          layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
                      }
                  } break;
@@ -83,7 +132,23 @@ index ab1a07d1..70183041 100644
              case LLM_ARCH_STARCODER2:
                  {
                      tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
-@@ -4029,6 +4035,7 @@ enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
+@@ -3531,6 +3538,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
+                     output   = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
+                     output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"),   {n_embd}, 0);
+                 } break;
++            case LLM_ARCH_MISTRAL3: break;
+             default:
+                 throw std::runtime_error("unknown architecture");
+         }
+@@ -4009,6 +4017,7 @@ enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
+         case LLM_ARCH_GRANITE_MOE:
+         case LLM_ARCH_CHAMELEON:
+         case LLM_ARCH_SOLAR:
++        case LLM_ARCH_MISTRAL3:
+             return LLAMA_ROPE_TYPE_NORM;
+ 
+         // the pairs of head values are offset by n_rot/2
+@@ -4029,6 +4038,7 @@ enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
          case LLM_ARCH_PHIMOE:
          case LLM_ARCH_GEMMA:
          case LLM_ARCH_GEMMA2:
@@ -92,21 +157,16 @@ index ab1a07d1..70183041 100644
          case LLM_ARCH_OPENELM:
          case LLM_ARCH_GPTNEOX:
 diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
-index 6eb1da08..d2f3a510 100644
+index 6eb1da08..ebcbafa1 100644
 --- a/src/llama-quant.cpp
 +++ b/src/llama-quant.cpp
-@@ -737,6 +737,15 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
+@@ -737,6 +737,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
          // This used to be a regex, but <regex> has an extreme cost to compile times.
          bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
  
 +        // don't quantize vision stuff
-+        quantize &= name.find("v.blk.") == std::string::npos;
-+
-+        quantize &= name.find("mm.mm_input_projection.weight") == std::string::npos;
-+        quantize &= name.find("mm.mm_soft_emb_norm.weight") == std::string::npos;
-+        quantize &= name.find("v.patch_embedding.weight") == std::string::npos;
-+        quantize &= name.find("v.position_embedding.weight") == std::string::npos;
-+        quantize &= name.find("v.post_layernorm.weight") == std::string::npos;
++        quantize &= name.find("v.") == std::string::npos;
++        quantize &= name.find("mm.") == std::string::npos;
 +
          // quantize only 2D and 3D tensors (experts)
          quantize &= (ggml_n_dims(tensor) >= 2);

llama/patches/0022-metal-add-op_neg.patch

+From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
+From: Michael Yang <git@mxy.ng>
+Date: Wed, 2 Apr 2025 15:26:15 -0700
+Subject: [PATCH] metal: add op_neg
+
+---
+ ggml/src/ggml-metal/ggml-metal.m     | 15 +++++++++++++++
+ ggml/src/ggml-metal/ggml-metal.metal |  7 +++++++
+ 2 files changed, 22 insertions(+)
+
+diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
+index e4c093f9..d8422f1b 100644
+--- a/ggml/src/ggml-metal/ggml-metal.m
++++ b/ggml/src/ggml-metal/ggml-metal.m
+@@ -423,6 +423,7 @@ enum ggml_metal_kernel_type {
+     GGML_METAL_KERNEL_TYPE_SQRT,
+     GGML_METAL_KERNEL_TYPE_SIN,
+     GGML_METAL_KERNEL_TYPE_COS,
++    GGML_METAL_KERNEL_TYPE_NEG,
+     GGML_METAL_KERNEL_TYPE_SUM_ROWS,
+     GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,
+     GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,
+@@ -1039,6 +1040,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
+         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQRT,                          sqrt,                           true);
+         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN,                           sin,                            true);
+         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS,                           cos,                            true);
++        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NEG,                           neg,                            true);
+         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                      sum_rows,                       true);
+         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGMAX,                        argmax,                         true);
+         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,               pool_2d_avg_f32,                true);
+@@ -1202,6 +1204,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
+                 case GGML_UNARY_OP_GELU_QUICK:
+                 case GGML_UNARY_OP_SILU:
+                 case GGML_UNARY_OP_ELU:
++                case GGML_UNARY_OP_NEG:
+                     return ggml_is_contiguous(op->src[0]);
+                 default:
+                     return false;
+@@ -1873,6 +1876,18 @@ static void ggml_metal_encode_node(
+ 
+                     [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                 } break;
++                case GGML_UNARY_OP_NEG:
++                {
++                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NEG].pipeline;
++
++                    [encoder setComputePipelineState:pipeline];
++                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
++                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
++
++                    const int64_t n = ggml_nelements(dst);
++
++                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
++                } break;
+                 default:
+                 {
+                     GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));
+diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
+index f38909d0..bb0ff668 100644
+--- a/ggml/src/ggml-metal/ggml-metal.metal
++++ b/ggml/src/ggml-metal/ggml-metal.metal
+@@ -945,6 +945,13 @@ kernel void kernel_cos(
+     dst[tpig] = cos(src0[tpig]);
+ }
+ 
++kernel void kernel_neg(
++        device const float * src0,
++        device       float * dst,
++        uint tpig[[thread_position_in_grid]]) {
++    dst[tpig] = -src0[tpig];
++}
++
+ kernel void kernel_sum_rows(
+         device const float * src0,
+         device       float * dst,

ml/backend.go

@@ -118,6 +118,7 @@ type Tensor interface {
 	Bytes() []byte
 	Floats() []float32
 
+	Neg(ctx Context) Tensor
 	Add(ctx Context, t2 Tensor) Tensor
 	Mul(ctx Context, t2 Tensor) Tensor
 	Mulmat(ctx Context, t2 Tensor) Tensor
@@ -132,7 +133,10 @@ type Tensor interface {
 	Conv2D(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
 
 	RoPE(ctx Context, positionIDs, ropeFactors Tensor, dim, ropeType uint32, base, scale float32) Tensor
+	IM2Col(ctx Context, weight Tensor, s0, s1, p0, p1, d0, d1 int) Tensor
 
+	Sin(ctx Context) Tensor
+	Cos(ctx Context) Tensor
 	Tanh(ctx Context) Tensor
 	GELU(ctx Context) Tensor
 	SILU(ctx Context) Tensor
@@ -147,9 +151,13 @@ type Tensor interface {
 	Unpad(ctx Context, shape ...int) Tensor
 
 	Stack(ctx Context, dim int, s ...Tensor) Tensor
+
+	// Repeat repeats the tensor n times along dimension dim
+	Repeat(ctx Context, dim, n int) Tensor
 	Concat(ctx Context, t2 Tensor, dim int) Tensor
 	Rows(ctx Context, t2 Tensor) Tensor
 	Copy(ctx Context, t2 Tensor) Tensor
+	Duplicate(ctx Context) Tensor
 }
 
 // ScaledDotProductAttention implements a fused attention
@@ -214,7 +222,7 @@ func Dump(ctx Context, t Tensor, opts ...DumpOptions) string {
 			return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)
 		})
 	case DTypeF16, DTypeQ80, DTypeQ40:
-		f32 := ctx.Empty(DTypeF32, t.Shape()...)
+		f32 := ctx.Input().Empty(DTypeF32, t.Shape()...)
 		f32 = t.Copy(ctx, f32)
 		return dump[[]float32](ctx, f32, opts[0].Items, func(f float32) string {
 			return strconv.FormatFloat(float64(f), 'f', opts[0].Precision, 32)

ml/backend/ggml/ggml.go

@@ -711,6 +711,13 @@ func (t *Tensor) DType() ml.DType {
 	}
 }
 
+func (t *Tensor) Neg(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_neg(ctx.(*Context).ctx, t.t),
+	}
+}
+
 func (t *Tensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	return &Tensor{
 		b: t.b,
@@ -718,6 +725,27 @@ func (t *Tensor) Add(ctx ml.Context, t2 ml.Tensor) ml.Tensor {
 	}
 }
 
+func (t *Tensor) Repeat(ctx ml.Context, dim, n int) ml.Tensor {
+	if dim < 0 || dim >= C.GGML_MAX_DIMS {
+		panic("invalid dimension")
+	}
+
+	shape := make([]C.int64_t, C.GGML_MAX_DIMS)
+	for i := range C.GGML_MAX_DIMS {
+		if i == dim {
+			shape[i] = C.int64_t(t.Dim(i) * n)
+		} else {
+			shape[i] = C.int64_t(t.Dim(i))
+		}
+	}
+
+	tmpl := C.ggml_new_tensor(ctx.(*Context).ctx, t.t._type, C.int(len(shape)), unsafe.SliceData(shape))
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_repeat(ctx.(*Context).ctx, t.t, tmpl),
+	}
+}
+
 func (t *Tensor) Stack(ctx ml.Context, dim int, s ...ml.Tensor) ml.Tensor {
 	if len(s) > 0 {
 		return t.Concat(ctx, s[0].Stack(ctx, dim, s[1:]...), dim)
@@ -854,6 +882,20 @@ func (t *Tensor) Softmax(ctx ml.Context) ml.Tensor {
 	}
 }
 
+func (t *Tensor) Sin(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_sin(ctx.(*Context).ctx, t.t),
+	}
+}
+
+func (t *Tensor) Cos(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_cos(ctx.(*Context).ctx, t.t),
+	}
+}
+
 func (t *Tensor) Tanh(ctx ml.Context) ml.Tensor {
 	return &Tensor{
 		b: t.b,
@@ -942,6 +984,13 @@ func (t *Tensor) RoPE(ctx ml.Context, positionIDs, ropeFactors ml.Tensor, ropeDi
 	}
 }
 
+func (t *Tensor) IM2Col(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_im2col(ctx.(*Context).ctx, t.t, t2.(*Tensor).t, C.int(s0), C.int(s1), C.int(p0), C.int(p1), C.int(d0), C.int(d1), true, C.GGML_TYPE_F32),
+	}
+}
+
 func (t *Tensor) GELU(ctx ml.Context) ml.Tensor {
 	return &Tensor{
 		b: t.b,
@@ -1010,3 +1059,10 @@ func (t *Tensor) ScaledDotProductAttention(ctx ml.Context, key, value, mask ml.T
 		return kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	}
 }
+
+func (t *Tensor) Duplicate(ctx ml.Context) ml.Tensor {
+	return &Tensor{
+		b: t.b,
+		t: C.ggml_dup(ctx.(*Context).ctx, t.t),
+	}
+}

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal-embed.metal

@@ -3083,6 +3083,13 @@ kernel void kernel_cos(
     dst[tpig] = cos(src0[tpig]);
 }
 
+kernel void kernel_neg(
+        device const float * src0,
+        device       float * dst,
+        uint tpig[[thread_position_in_grid]]) {
+    dst[tpig] = -src0[tpig];
+}
+
 kernel void kernel_sum_rows(
         device const float * src0,
         device       float * dst,

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal.m

@@ -423,6 +423,7 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_SQRT,
     GGML_METAL_KERNEL_TYPE_SIN,
     GGML_METAL_KERNEL_TYPE_COS,
+    GGML_METAL_KERNEL_TYPE_NEG,
     GGML_METAL_KERNEL_TYPE_SUM_ROWS,
     GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,
     GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,
@@ -1039,6 +1040,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQRT,                          sqrt,                           true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIN,                           sin,                            true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_COS,                           cos,                            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NEG,                           neg,                            true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                      sum_rows,                       true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGMAX,                        argmax,                         true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_AVG_F32,               pool_2d_avg_f32,                true);
@@ -1202,6 +1204,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_SILU:
                 case GGML_UNARY_OP_ELU:
+                case GGML_UNARY_OP_NEG:
                     return ggml_is_contiguous(op->src[0]);
                 default:
                     return false;
@@ -1873,6 +1876,18 @@ static void ggml_metal_encode_node(
 
                     [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                 } break;
+                case GGML_UNARY_OP_NEG:
+                {
+                    id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_NEG].pipeline;
+
+                    [encoder setComputePipelineState:pipeline];
+                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+                    [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+
+                    const int64_t n = ggml_nelements(dst);
+
+                    [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
+                } break;
                 default:
                 {
                     GGML_LOG_WARN("%s: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(dst->op));

ml/backend/ggml/ggml/src/ggml-metal/ggml-metal.metal

@@ -945,6 +945,13 @@ kernel void kernel_cos(
     dst[tpig] = cos(src0[tpig]);
 }
 
+kernel void kernel_neg(
+        device const float * src0,
+        device       float * dst,
+        uint tpig[[thread_position_in_grid]]) {
+    dst[tpig] = -src0[tpig];
+}
+
 kernel void kernel_sum_rows(
         device const float * src0,
         device       float * dst,

model/models/gemma3/model_text.go

@@ -11,7 +11,7 @@ import (
 	"github.com/ollama/ollama/model/input"
 )
 
-type TextOptions struct {
+type TextConfig struct {
 	hiddenSize, numHeads, numKVHeads int
 	attnKeyLen, attnValLen           int
 	eps, ropeScale                   float32
@@ -28,7 +28,7 @@ type TextModel struct {
 	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
 	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
 
-	*TextOptions
+	*TextConfig
 }
 
 const (
@@ -55,7 +55,7 @@ func newTextModel(c fs.Config) *TextModel {
 			},
 		),
 		Layers: make([]TextLayer, numBlocks),
-		TextOptions: &TextOptions{
+		TextConfig: &TextConfig{
 			hiddenSize:     int(c.Uint("embedding_length")),
 			numHeads:       int(c.Uint("attention.head_count")),
 			numKVHeads:     int(c.Uint("attention.head_count_kv")),
@@ -84,7 +84,7 @@ type TextSelfAttention struct {
 	Output    *nn.Linear  `gguf:"attn_output"`
 }
 
-func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
+func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextConfig) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
 	ropeType := uint32(2)
 
@@ -120,12 +120,12 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, pos
 }
 
 func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	ropeBase := m.TextOptions.ropeLocalBase
+	ropeBase := m.TextConfig.ropeLocalBase
 	if (layer+1)%gemmaGlobalCacheCount == 0 {
-		ropeBase = m.TextOptions.ropeGlobalBase
+		ropeBase = m.TextConfig.ropeGlobalBase
 	}
 
-	return key.RoPE(ctx, shift, nil, uint32(m.TextOptions.attnKeyLen), uint32(2), ropeBase, m.TextOptions.ropeScale), nil
+	return key.RoPE(ctx, shift, nil, uint32(m.TextConfig.attnKeyLen), uint32(2), ropeBase, m.TextConfig.ropeScale), nil
 }
 
 type TextMLP struct {
@@ -134,7 +134,7 @@ type TextMLP struct {
 	Gate *nn.Linear `gguf:"ffn_gate"`
 }
 
-func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
+func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextConfig) ml.Tensor {
 	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
 	return mlp.Down.Forward(ctx, hiddenState)
 }
@@ -148,7 +148,7 @@ type TextLayer struct {
 	PostMLPNorm       *nn.RMSNorm `gguf:"post_ffw_norm"`
 }
 
-func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
+func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *TextConfig) ml.Tensor {
 	residual := hiddenState
 
 	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
@@ -173,7 +173,7 @@ func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs,
 
 func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, batch input.Batch, cache kvcache.Cache) ml.Tensor {
 	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
-	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextOptions.hiddenSize)))
+	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextConfig.hiddenSize)))
 
 	// set image embeddings
 	var except []int
@@ -206,7 +206,7 @@ func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor
 			lastLayerOutputs = outputs
 		}
 
-		hiddenState = layer.Forward(ctx, i, hiddenState, positions, lastLayerOutputs, cache, m.TextOptions)
+		hiddenState = layer.Forward(ctx, i, hiddenState, positions, lastLayerOutputs, cache, m.TextConfig)
 	}
 
 	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)

model/models/mistral3/imageproc.go

+package mistral3
+
+import (
+	"image"
+	_ "image/jpeg"
+	_ "image/png"
+	"math"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/model/imageproc"
+)
+
+type ImageProcessor struct {
+	imageSize   int
+	patchSize   int
+	numChannels int
+	longestEdge int
+}
+
+func newImageProcessor(c fs.Config) ImageProcessor {
+	return ImageProcessor{
+		imageSize:   int(c.Uint("vision.image_size", 1540)),
+		patchSize:   int(c.Uint("vision.patch_size", 14)),
+		numChannels: int(c.Uint("vision.num_channels", 3)),
+		longestEdge: int(c.Uint("vision.longest_edge", 1540)),
+	}
+}
+
+// ProcessImage prepares an image for the vision model by:
+// 1. Compositing transparent images
+// 2. Resizing to fit model constraints while preserving aspect ratio
+// 3. Normalizing pixel values
+// Returns normalized image data and the final size in pixels
+func (p *ImageProcessor) ProcessImage(img image.Image) ([]float32, image.Point, error) {
+	img = imageproc.Composite(img)
+
+	size := img.Bounds().Size()
+	ratio := max(float64(size.Y)/float64(p.longestEdge), float64(size.X)/float64(p.longestEdge))
+	if ratio > 1.0 {
+		size = image.Point{
+			int(math.Floor(float64(size.X) / ratio)),
+			int(math.Floor(float64(size.Y) / ratio)),
+		}
+	}
+
+	patchesX := (size.X-1)/p.patchSize + 1
+	patchesY := (size.Y-1)/p.patchSize + 1
+	size = image.Point{
+		patchesX * p.patchSize,
+		patchesY * p.patchSize,
+	}
+
+	img = imageproc.Resize(img, size, imageproc.ResizeBilinear)
+	data := imageproc.Normalize(img, imageproc.ClipDefaultMean, imageproc.ClipDefaultSTD, true, true)
+	return data, size, nil
+}

model/models/mistral3/model.go

+package mistral3
+
+import (
+	"bytes"
+	"image"
+	"slices"
+	"sync"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Model struct {
+	model.Base
+	*TextModel
+	*VisionModel         `gguf:"v,vision"`
+	*MultiModalProjector `gguf:"mm"`
+
+	ImageProcessor
+}
+
+// Implement MultimodalProcessor interface
+var _ model.MultimodalProcessor = (*Model)(nil)
+
+func New(c fs.Config) (model.Model, error) {
+	textModel, err := NewTextModel(c)
+	if err != nil {
+		return nil, err
+	}
+
+	m := &Model{
+		TextModel:           textModel,
+		VisionModel:         newVisionModel(c),
+		ImageProcessor:      newImageProcessor(c),
+		MultiModalProjector: newMultiModalProjector(c),
+	}
+
+	m.Cache = kvcache.NewCausalCache(m.TextModel.Shift)
+
+	return m, nil
+}
+
+type PatchMerger struct {
+	MergingLayer *nn.Linear `gguf:"merging_layer"`
+}
+
+func (pm *PatchMerger) Forward(ctx ml.Context, visionOutputs ml.Tensor, size image.Point, spatialMergeSize int) ml.Tensor {
+	d := visionOutputs.Dim(0)
+	imageGrid := visionOutputs.Permute(ctx, 1, 0, 2, 3).Contiguous(ctx).Reshape(ctx, size.X, size.Y, d)
+	kernel := ctx.Input().Empty(ml.DTypeF32, spatialMergeSize, spatialMergeSize, d)
+	patches := kernel.IM2Col(ctx, imageGrid, spatialMergeSize, spatialMergeSize, 0, 0, 1, 1)
+	reshaped := patches.Reshape(ctx, d*spatialMergeSize*spatialMergeSize, patches.Dim(1)*patches.Dim(2))
+	return pm.MergingLayer.Forward(ctx, reshaped)
+}
+
+type MultiModalProjector struct {
+	Norm        *nn.RMSNorm  `gguf:"norm"`
+	Linear1     *nn.Linear   `gguf:"linear_1"`
+	Linear2     *nn.Linear   `gguf:"linear_2"`
+	PatchMerger *PatchMerger `gguf:"patch_merger"`
+
+	spatialMergeSize int
+	eps              float32
+	patchSize        int
+}
+
+func (p *MultiModalProjector) Forward(ctx ml.Context, visionOutputs ml.Tensor, size image.Point) (ml.Tensor, image.Point) {
+	visionOutputs = p.Norm.Forward(ctx, visionOutputs, p.eps)
+	patchSizes := image.Point{size.X / p.patchSize, size.Y / p.patchSize}
+	visionOutputs = p.PatchMerger.Forward(ctx, visionOutputs, patchSizes, p.spatialMergeSize)
+	visionOutputs = p.Linear1.Forward(ctx, visionOutputs)
+	visionOutputs = visionOutputs.GELU(ctx)
+	return p.Linear2.Forward(ctx, visionOutputs), image.Point{patchSizes.X / p.spatialMergeSize, patchSizes.Y / p.spatialMergeSize}
+}
+
+func newMultiModalProjector(c fs.Config) *MultiModalProjector {
+	return &MultiModalProjector{
+		spatialMergeSize: int(c.Uint("spatial_merge_size", 2)),
+		eps:              c.Float("text_config.rms_norm_eps", 1e-5),
+		patchSize:        int(c.Uint("vision.patch_size", 14)),
+	}
+}
+
+func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, error) {
+	if len(m.VisionModel.Layers) == 0 {
+		return nil, model.ErrNoVisionModel
+	}
+
+	image, _, err := image.Decode(bytes.NewReader(multimodalData))
+	if err != nil {
+		return nil, err
+	}
+
+	f32s, size, err := m.ImageProcessor.ProcessImage(image)
+	if err != nil {
+		return nil, err
+	}
+
+	pixelValues, err := ctx.Input().FromFloatSlice(f32s, size.X, size.Y, m.ImageProcessor.numChannels)
+	if err != nil {
+		return nil, err
+	}
+
+	visionOutputs := m.VisionModel.Forward(ctx, pixelValues)
+	features, size := m.MultiModalProjector.Forward(ctx, visionOutputs, size)
+
+	// split into patches to be sent to the text transformer
+	parent := imageFeatures{tensor: features}
+	rows := make([]*imageRow, size.Y)
+	for i := range rows {
+		rows[i] = &imageRow{parent: &parent, s: i, shape: []int{features.Dim(0), size.X}}
+	}
+
+	return rows, nil
+}
+
+type imageFeatures struct {
+	tensor ml.Tensor
+
+	dataOnce sync.Once
+	data     []float32
+}
+
+type imageRow struct {
+	parent *imageFeatures
+	s      int
+	shape  []int
+}
+
+func (r *imageRow) data() []float32 {
+	n := 1
+	for _, s := range r.shape {
+		n *= s
+	}
+
+	return r.parent.data[r.s*n : (r.s+1)*n]
+}
+
+// PostTokenize arranges Mistral 3's inputs for the forward pass
+// In Mistral 3 and Pixtral, the input patches are arranged as follows:
+// [IMG]...[IMG][IMG_BREAK][IMG]...[IMG][IMG_BREAK][IMG]...[IMG][IMG_END]
+// Each sequence of [IMG]...[IMG] is a set of patches of vision embeddings
+// that can be processed together.
+func (m *Model) PostTokenize(inputs []input.Input) ([]input.Input, error) {
+	var result []input.Input
+	for _, inp := range inputs {
+		if inp.Multimodal == nil {
+			result = append(result, inp)
+		} else {
+			inputMultimodal := inp.Multimodal.([]*imageRow)
+			for i, row := range inputMultimodal {
+				// [IMG]
+				result = append(result, input.Input{Token: 10, Multimodal: row, MultimodalHash: inp.MultimodalHash, SameBatch: row.shape[1]})
+				result = append(result, slices.Repeat([]input.Input{{Token: 10}}, row.shape[1]-1)...)
+				if i == len(inputMultimodal)-1 {
+					// [IMG_END]
+					result = append(result, input.Input{Token: 13})
+				} else {
+					// [IMG_BREAK]
+					result = append(result, input.Input{Token: 12})
+				}
+			}
+		}
+	}
+
+	return result, nil
+}
+
+func (m *Model) Forward(ctx ml.Context, batch input.Batch) (ml.Tensor, error) {
+	positions, err := ctx.Input().FromIntSlice(batch.Positions, len(batch.Positions))
+	if err != nil {
+		return nil, err
+	}
+
+	outputs, err := ctx.Input().FromIntSlice(batch.Outputs, len(batch.Outputs))
+	if err != nil {
+		return nil, err
+	}
+
+	return m.TextModel.Forward(ctx, batch.Inputs, positions, outputs, batch, m.Cache), nil
+}
+
+func init() {
+	model.Register("mistral3", New)
+}

model/models/mistral3/model_text.go

+package mistral3
+
+import (
+	"fmt"
+	"math"
+	"strings"
+
+	"github.com/ollama/ollama/fs"
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type TextOptions struct {
+	hiddenSize, numHeads, numKVHeads, headDim int
+	eps, ropeBase, ropeScale                  float32
+	ropeDim                                   uint32
+}
+
+type TextModel struct {
+	model.Base
+	model.BytePairEncoding
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
+
+	*TextOptions
+}
+
+type SelfAttention struct {
+	Query  *nn.Linear `gguf:"attn_q"`
+	Key    *nn.Linear `gguf:"attn_k"`
+	Value  *nn.Linear `gguf:"attn_v"`
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	ropeType := uint32(0)
+	headDim := opts.headDim
+	if headDim == 0 {
+		headDim = opts.hiddenSize / opts.numHeads
+	}
+
+	q := sa.Query.Forward(ctx, hiddenState)
+	q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
+	q = q.RoPE(ctx, positionIDs, nil, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
+
+	k := sa.Key.Forward(ctx, hiddenState)
+	k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+	k = k.RoPE(ctx, positionIDs, nil, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
+
+	v := sa.Value.Forward(ctx, hiddenState)
+	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
+
+	kqv := nn.Attention(ctx, q, k, v, 1.0/math.Sqrt(float64(headDim)), cache)
+	kqv = kqv.Reshape(ctx, headDim*opts.numHeads, batchSize)
+	return sa.Output.Forward(ctx, kqv)
+}
+
+func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return key.RoPE(ctx, shift, nil, uint32(0), m.ropeDim, m.ropeBase, m.ropeScale), nil
+}
+
+type MLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).SILU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type Layer struct {
+	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
+	SelfAttention *SelfAttention
+	MLPNorm       *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP           *MLP
+}
+
+func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positionIDs, cache, opts)
+
+	// In the final layer (outputs != nil), optimize by pruning to just the token positions
+	// we need logits for.
+	if outputs != nil {
+		hiddenState = hiddenState.Rows(ctx, outputs)
+		residual = residual.Rows(ctx, outputs)
+	}
+
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, batch input.Batch, cache kvcache.Cache) ml.Tensor {
+	hiddenState := m.TokenEmbedding.Forward(ctx, inputs).Duplicate(ctx)
+
+	// image embeddings
+	for _, image := range batch.Multimodal {
+		row := image.Multimodal.(*imageRow)
+		row.parent.dataOnce.Do(func() {
+			// use a new, throwaway context so the image tensor is not added to the graph
+			temp := m.Backend().NewContext()
+			temp.Forward(row.parent.tensor).Compute(row.parent.tensor)
+			row.parent.data = row.parent.tensor.Floats()
+			temp.Close()
+		})
+
+		imageFeature, err := ctx.Input().FromFloatSlice(row.data(), row.shape...)
+		if err != nil {
+			panic(err)
+		}
+
+		ctx.Forward(imageFeature.Copy(ctx, hiddenState.View(ctx, image.Index*hiddenState.Stride(1), imageFeature.Dim(0)*imageFeature.Dim(1))))
+	}
+
+	for i, layer := range m.Layers {
+		cache.SetLayer(i)
+
+		var lastLayerOutputs ml.Tensor
+		if i == len(m.Layers)-1 {
+			lastLayerOutputs = outputs
+		}
+
+		hiddenState = layer.Forward(ctx, hiddenState, positions, lastLayerOutputs, cache, m.TextOptions)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	return m.Output.Forward(ctx, hiddenState)
+}
+
+func NewTextModel(c fs.Config) (*TextModel, error) {
+	if !strings.EqualFold(c.String("tokenizer.ggml.model"), "gpt2") {
+		return nil, fmt.Errorf("tokenizer %s not yet supported", c.String("tokenizer.ggml.model"))
+	}
+
+	textModel := &TextModel{
+		BytePairEncoding: model.NewBytePairEncoding(
+			c.String("tokenizer.ggml.pretokenizer", `[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Types:  c.Uints("tokenizer.ggml.token_type"),
+				Merges: c.Strings("tokenizer.ggml.merges"),
+				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id", 1)),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id", 2)),
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+			},
+		),
+		Layers: make([]Layer, c.Uint("block_count")),
+		TextOptions: &TextOptions{
+			hiddenSize: int(c.Uint("embedding_length")),
+			numHeads:   int(c.Uint("attention.head_count")),
+			numKVHeads: int(c.Uint("attention.head_count_kv")),
+			headDim:    int(c.Uint("attention.key_length")),
+			eps:        c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:   c.Float("rope.freq_base"),
+			ropeScale:  c.Float("rope.freq_scale", 1),
+			ropeDim:    c.Uint("rope.dimension_count"),
+		},
+	}
+
+	return textModel, nil
+}