convert : avoid AutoConfig for Mamba and Mamba2 hparams

Author: compilade

convert_hf_to_gguf.py

@@ -4127,6 +4127,14 @@ def set_gguf_parameters(self):
 class MambaModel(TextModel):
     model_arch = gguf.MODEL_ARCH.MAMBA
 
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        # Avoid using AutoConfig for hparams
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+                hparams = json.load(f)
+        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
+
     def set_vocab(self):
         vocab_size = self.hparams["vocab_size"]
         # Round vocab size to next multiple of 8
@@ -4205,6 +4213,15 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 class Mamba2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.MAMBA2
 
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        # Avoid using AutoConfig for hparams
+        # It wrongly assumes all Mamba2 models are Mamba-Codestral-7B-v0.1
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+                hparams = json.load(f)
+        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
+
     def set_vocab(self):
         vocab_size = self.hparams["vocab_size"]
         # Round vocab size to next multiple of 16
@@ -5968,12 +5985,20 @@ def get_model_architecture(dir_model: Path, model_type: ModelType, hparams: Any
     hparams = ModelBase.load_hparams(dir_model) if hparams is None else hparams
     text_config = hparams.get("text_config", {})
     vision_config = hparams.get("vision_config", {})
-    arch = hparams["architectures"][0]
+    arch = None
+    if (arches := hparams.get("architectures")) is not None and len(arches) > 0:
+        arch = arches[0]
+    elif "ssm_cfg" in hparams:
+        # For non-hf Mamba and Mamba2 models
+        arch = hparams["ssm_cfg"].get("layer", "Mamba") + "ForCausalLM"
+
     # if "architectures" is found in the sub-config, use that instead
     if model_type == ModelType.TEXT and text_config.get("architectures") is not None:
         arch = text_config["architectures"][0]
     elif model_type == ModelType.VISION and vision_config.get("architectures") is not None:
         arch = vision_config["architectures"][0]
+    if arch is None:
+        raise ValueError("Failed to detect model architecture")
     return arch