try fix model loading quen3.6-35b

packages/node/meshnet_node/model_backend.py

@@ -134,8 +134,9 @@ class TorchModelShard:
                     self.model.to(self.device)
         except Exception as exc:
             if _looks_like_oom(exc):
+                memory_kind = "VRAM" if self.device.type == "cuda" else "RAM"
                 raise InsufficientVRAMError(
-                    f"insufficient VRAM to load {model_id} layers {shard_start}:{shard_end} "
+                    f"insufficient {memory_kind} to load {model_id} layers {shard_start}:{shard_end} "
                     f"with {quantization} quantization; choose a smaller shard or lower quantization"
                 ) from exc
             raise
@@ -411,7 +412,7 @@ def _should_partial_materialize_shard(
         return False
     if total_layers_hint is None:
         return False
-    return not (shard_start == 0 and shard_end >= total_layers_hint - 1)
+    return True
 
 
 def _load_partial_model_from_snapshot(
@@ -476,7 +477,7 @@ def _load_partial_model_from_snapshot(
         )
 
     with init_empty_weights_fn():
-        model = auto_model_for_causal_lm.from_config(cfg, torch_dtype=dtype)
+        model = auto_model_for_causal_lm.from_config(_causal_lm_config(cfg), torch_dtype=dtype)
     tie_weights = getattr(model, "tie_weights", None)
     if callable(tie_weights):
         tie_weights()
@@ -498,7 +499,7 @@ def _load_partial_model_from_snapshot(
             for tensor_name in names:
                 set_tensor_fn(
                     model,
-                    tensor_name,
+                    _checkpoint_tensor_name_for_model(model, tensor_name),
                     device,
                     value=handle.get_tensor(tensor_name),
                     dtype=dtype,
@@ -569,38 +570,74 @@ def _native_torch_dtype(cfg: Any, torch: Any) -> Any:
     return torch.bfloat16
 
 
+def _causal_lm_config(cfg: Any) -> Any:
+    """Use the text decoder config for composite VLM/MoE presets."""
+    get_text_config = getattr(cfg, "get_text_config", None)
+    if callable(get_text_config):
+        try:
+            return get_text_config()
+        except Exception:
+            pass
+    text_config = getattr(cfg, "text_config", None)
+    if text_config is not None:
+        return text_config
+    return cfg
+
+
+def _checkpoint_tensor_name_for_model(model: Any, tensor_name: str) -> str:
+    """Map multimodal checkpoint keys onto text-only CausalLM modules when needed."""
+    inner = getattr(model, "model", None)
+    if inner is not None and hasattr(inner, "language_model"):
+        return tensor_name
+    if ".language_model." in tensor_name:
+        return tensor_name.replace(".language_model.", ".")
+    return tensor_name
+
+
+def _transformer_backbone(model: Any) -> Any:
+    if hasattr(model, "model"):
+        inner = model.model
+        language_model = getattr(inner, "language_model", None)
+        if language_model is not None:
+            return language_model
+        return inner
+    if hasattr(model, "transformer"):
+        return model.transformer
+    raise ModelBackendError(
+        "unsupported HuggingFace model architecture: no transformer backbone found"
+    )
+
+
 def _model_layers(model: Any) -> Any:
-    if hasattr(model, "model") and hasattr(model.model, "layers"):
-        return model.model.layers
-    if hasattr(model, "transformer") and hasattr(model.transformer, "h"):
-        return model.transformer.h
+    backbone = _transformer_backbone(model)
+    for attr in ("layers", "h", "blocks"):
+        layers = getattr(backbone, attr, None)
+        if layers is not None:
+            return layers
     raise ModelBackendError(
         "unsupported HuggingFace model architecture: no transformer layers found"
     )
 
 
 def _embed_tokens(model: Any) -> Any:
-    if hasattr(model, "model") and hasattr(model.model, "embed_tokens"):
-        return model.model.embed_tokens
-    if hasattr(model, "transformer") and hasattr(model.transformer, "wte"):
-        return model.transformer.wte
+    backbone = _transformer_backbone(model)
+    for attr in ("embed_tokens", "wte"):
+        embed = getattr(backbone, attr, None)
+        if embed is not None:
+            return embed
     raise ModelBackendError(
         "unsupported HuggingFace model architecture: no token embeddings found"
     )
 
 
 def _position_embeddings(model: Any) -> Any | None:
-    if hasattr(model, "transformer") and hasattr(model.transformer, "wpe"):
-        return model.transformer.wpe
-    return None
+    backbone = _transformer_backbone(model)
+    return getattr(backbone, "wpe", None)
 
 
 def _rotary_embedding_module(model: Any) -> Any | None:
-    if hasattr(model, "model") and hasattr(model.model, "rotary_emb"):
-        return model.model.rotary_emb
-    if hasattr(model, "transformer") and hasattr(model.transformer, "rotary_emb"):
-        return model.transformer.rotary_emb
-    return None
+    backbone = _transformer_backbone(model)
+    return getattr(backbone, "rotary_emb", None)
 
 
 def _active_modules_for_shard(model: Any, shard_start: int, shard_end: int) -> list[Any]:
@@ -627,10 +664,11 @@ def _active_modules_for_shard(model: Any, shard_start: int, shard_end: int) -> l
 
 
 def _final_norm(model: Any) -> Any | None:
-    if hasattr(model, "model") and hasattr(model.model, "norm"):
-        return model.model.norm
-    if hasattr(model, "transformer") and hasattr(model.transformer, "ln_f"):
-        return model.transformer.ln_f
+    backbone = _transformer_backbone(model)
+    for attr in ("norm", "ln_f", "final_layer_norm"):
+        norm = getattr(backbone, attr, None)
+        if norm is not None:
+            return norm
     return None
 
 
@@ -743,7 +781,12 @@ def _looks_like_oom(exc: BaseException) -> bool:
     current: BaseException | None = exc
     while current is not None:
         text = str(current).lower()
-        if "out of memory" in text or "cuda error: out of memory" in text:
+        if (
+            "out of memory" in text
+            or "cuda error: out of memory" in text
+            or "paging file is too small" in text
+            or "os error 1455" in text
+        ):
             return True
         current = current.__cause__ or current.__context__
     return False