Merge branch 'hiyouga:main' into pixtral-patch

Former-commit-id: 93a441a6b7

src/llamafactory/model/loader.py

@@ -82,6 +82,8 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
             padding_side="right",
             **init_kwargs,
         )
+    except Exception as e:
+        raise OSError("Failed to load tokenizer.") from e
 
     if model_args.new_special_tokens is not None:
         num_added_tokens = tokenizer.add_special_tokens(
@@ -97,12 +99,13 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
     try:
         processor = AutoProcessor.from_pretrained(model_args.model_name_or_path, **init_kwargs)
         patch_processor(processor, config, tokenizer, model_args)
-    except Exception:
+    except Exception as e:
+        logger.warning("Processor was not found: {}.".format(e))
         processor = None
 
     # Avoid load tokenizer, see:
     # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/auto/processing_auto.py#L324
-    if "Processor" not in processor.__class__.__name__:
+    if processor is not None and "Processor" not in processor.__class__.__name__:
         processor = None
 
     return {"tokenizer": tokenizer, "processor": processor}

src/llamafactory/model/model_utils/longlora.py

@@ -353,7 +353,7 @@ def llama_sdpa_attention_forward(
 
 
 def _apply_llama_patch() -> None:
-    require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
+    require_version("transformers>=4.41.2,<=4.45.2", "To fix: pip install transformers>=4.41.2,<=4.45.2")
     LlamaAttention.forward = llama_attention_forward
     LlamaFlashAttention2.forward = llama_flash_attention_2_forward
     LlamaSdpaAttention.forward = llama_sdpa_attention_forward

src/llamafactory/model/model_utils/packing.py

@@ -114,7 +114,7 @@ def get_unpad_data(attention_mask: "torch.Tensor") -> Tuple["torch.Tensor", "tor
 
 
 def _patch_for_block_diag_attn(model_type: str) -> None:
-    require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
+    require_version("transformers>=4.41.2,<=4.45.2", "To fix: pip install transformers>=4.41.2,<=4.45.2")
     if is_transformers_version_greater_than_4_43():
         import transformers.modeling_flash_attention_utils
 

src/llamafactory/model/patcher.py

@@ -110,6 +110,9 @@ def patch_config(
     if getattr(config, "model_type", None) == "qwen2" and is_trainable and model_args.flash_attn == "fa2":
         setattr(config, "use_cache", False)  # qwen2 does not support use_cache when using flash attn
 
+    if "LlavaLlamaForCausalLM" in getattr(config, "architectures", []):
+        raise ValueError("Please download llava models with hf-compatible format: https://huggingface.co/llava-hf")
+
     # deepspeed zero3 is not compatible with low_cpu_mem_usage
     init_kwargs["low_cpu_mem_usage"] = model_args.low_cpu_mem_usage and (not is_deepspeed_zero3_enabled())