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.

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);