Merge pull request #3835 from BUAADreamer/main

fix some features in llava-style training

data/dataset_info.json

@@ -38,6 +38,20 @@
       "assistant_tag": "assistant"
     }
   },
+  "mllm_pt_demo": {
+    "hf_hub_url": "BUAADreamer/mllm_pt_demo",
+    "formatting": "sharegpt",
+    "columns": {
+      "messages": "messages",
+      "images": "images"
+    },
+    "tags": {
+      "role_tag": "role",
+      "content_tag": "content",
+      "user_tag": "user",
+      "assistant_tag": "assistant"
+    }
+  },
   "alpaca_en": {
     "hf_hub_url": "llamafactory/alpaca_en",
     "ms_hub_url": "llamafactory/alpaca_en"

src/llamafactory/hparams/model_args.py

@@ -85,6 +85,10 @@ class ModelArguments:
         default=False,
         metadata={"help": "Whethor or not to use multimodal LLM that accepts visual inputs."},
     )
+    tune_mm_proj: bool = field(
+        default=False,
+        metadata={"help": "Whethor or not only finetune mm_projector for MLLM."},
+    )
     moe_aux_loss_coef: Optional[float] = field(
         default=None,
         metadata={"help": "Coefficient of the auxiliary router loss in mixture-of-experts model."},

src/llamafactory/model/adapter.py

@@ -10,6 +10,7 @@
 from .utils.misc import find_all_linear_modules, find_expanded_modules
 from .utils.quantization import QuantizationMethod
 from .utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model
+from .utils.visual import filter_vision_tower_linear
 
 
 if TYPE_CHECKING:
@@ -58,6 +59,9 @@ def init_adapter(
         if model_args.visual_inputs and hasattr(model, "vision_tower"):  # freeze vision model
             model.vision_tower.requires_grad_(False)
 
+        if model_args.visual_inputs and hasattr(model, "language_model") and model_args.tune_mm_proj:  # freeze language model if only tune mm_proj
+            model.language_model.requires_grad_(False)
+
     if finetuning_args.finetuning_type == "freeze" and is_trainable:
         logger.info("Fine-tuning method: Freeze")
         num_layers = (
@@ -180,6 +184,9 @@ def init_adapter(
             if finetuning_args.use_llama_pro:
                 target_modules = find_expanded_modules(model, target_modules, finetuning_args.num_layer_trainable)
 
+            if model_args.visual_inputs:
+                target_modules = filter_vision_tower_linear(target_modules)
+
             if (
                 finetuning_args.use_dora
                 and getattr(model, "quantization_method", None) is not None

src/llamafactory/model/utils/visual.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Tuple
+from typing import TYPE_CHECKING, Tuple, List
 
 import torch
 import transformers.models
@@ -82,3 +82,8 @@ def configure_visual_model(config: "PretrainedConfig") -> None:
     if getattr(config, "is_yi_vl_derived_model", None):
         logger.info("Detected Yi-VL model, applying projector patch.")
         transformers.models.llava.modeling_llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVL
+
+
+def filter_vision_tower_linear(target_modules: List[str]) -> str:
+    target_modules = f"^(?!.*vision_tower).*(?:{'|'.join(target_modules)}).*"
+    return target_modules