cgraph ok, just missing 2D RoPE

Author: ngxson

convert_hf_to_gguf.py

@@ -1898,6 +1898,55 @@ def prepare_tensors(self):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("LlavaForConditionalGeneration")
+class LlavaVisionModel(VisionModel):
+    img_break_tok_id = -1
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if self.hparams["model_type"] == "pixtral":
+            # fix missing config.json values
+            self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 16)
+            self.hparams["num_hidden_layers"] = self.hparams.get("num_hidden_layers", 24)
+            self.hparams["intermediate_size"] = self.hparams.get("intermediate_size", 4096)
+            self.hparams["hidden_size"] = self.hparams.get("hidden_size", 1024)
+            self.hparams["layer_norm_eps"] = self.hparams.get("layer_norm_eps", 1e-5)
+            self.img_break_tok_id = 12 # see tokenizer_config.json
+        else:
+            raise ValueError(f"Unsupported model type: {self.hparams['model_type']}")
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if hparams["model_type"] == "pixtral":
+            self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.PIXTRAL)
+            # default values below are taken from HF tranformers code
+            self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
+            self.gguf_writer.add_vision_use_silu(True)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = n_head
+
+        if name.startswith("multi_modal_projector.") or name.startswith("vision_tower."):
+            # process vision tensors
+            if name.endswith(("q_proj.weight", "q_proj.bias")):
+                data_torch = LlamaModel.permute(data_torch, n_head, n_head)
+            if name.endswith(("k_proj.weight", "k_proj.bias")):
+                data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
+            return [(self.map_tensor_name(name), data_torch)]
+
+        if self.img_break_tok_id > 0 and "embed_tokens.weight" in name:
+            logger.info(f"Extracting [IMG_BREAK] token embedding from {name}")
+            # for pixtral model, we need to extract the [IMG_BREAK] token embedding
+            img_break_embd = data_torch[self.img_break_tok_id]
+            name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK]
+            return [(self.map_tensor_name(name), img_break_embd)]
+
+        return [] # skip other tensors
+
+
 @ModelBase.register("Idefics3ForConditionalGeneration", "SmolVLMForConditionalGeneration")
 class SmolVLMModel(VisionModel):
     def __init__(self, *args, **kwargs):

examples/llava/clip-impl.h

@@ -60,6 +60,7 @@
 #define TN_ATTN_V          "%s.blk.%d.attn_v.%s"
 #define TN_ATTN_OUTPUT     "%s.blk.%d.attn_out.%s"
 #define TN_FFN_DOWN        "%s.blk.%d.ffn_down.%s"
+#define TN_FFN_GATE        "%s.blk.%d.ffn_gate.%s"
 #define TN_FFN_UP          "%s.blk.%d.ffn_up.%s"
 #define TN_LN_1            "%s.blk.%d.ln1.%s"
 #define TN_LN_2            "%s.blk.%d.ln2.%s"
@@ -73,6 +74,7 @@
 #define TN_MM_INP_PROJ     "mm.input_projection.weight" // gemma3
 #define TN_MM_SOFT_EMB_N   "mm.soft_emb_norm.weight"    // gemma3
 #define TN_MM_PROJECTOR    "mm.model.fc.weight"         // idefics3
+#define TN_TOK_IMG_BREAK   "v.token_embd.img_break"     // pixtral
 
 // mimicpmv
 #define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k"
@@ -101,6 +103,7 @@ enum projector_type {
     PROJECTOR_TYPE_MERGER,
     PROJECTOR_TYPE_GEMMA3,
     PROJECTOR_TYPE_IDEFICS3,
+    PROJECTOR_TYPE_PIXTRAL,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -112,7 +115,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_GLM_EDGE,  "adapter"},
     { PROJECTOR_TYPE_MERGER,    "qwen2vl_merger"},
     { PROJECTOR_TYPE_GEMMA3,    "gemma3"},
-    { PROJECTOR_TYPE_IDEFICS3,  "idefics3"},
+    { PROJECTOR_TYPE_PIXTRAL,   "pixtral"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {