Upload models/src/training/params.py with huggingface_hub

models/src/training/params.py

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+from dataclasses import dataclass, field
+from typing import Optional
+
+from transformers import TrainingArguments
+
+
+@dataclass
+class ModelArguments:
+    model_id: Optional[str] = field(default="Qwen/Qwen2-VL-7B-Instruct")
+
+
+@dataclass
+class TrainingArguments(TrainingArguments):
+    cache_dir: Optional[str] = field(default=None)
+    optim: str = field(default="adamw_torch")
+    adam_beta1: float = field(default=0.9)
+    adam_beta2: float = field(default=0.999)
+    adam_epsilon: float = field(default=1e-8)
+
+    freeze_vision_tower: bool = field(default=False)
+    freeze_llm: bool = field(default=False)
+    tune_merger: bool = field(default=False)
+    disable_flash_attn2: bool = field(default=False)
+
+    max_seq_length: int = field(
+        default=32768, # This is the default value of the qwen2-vl model
+        metadata={
+            "help":
+                "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
+        },
+    )
+
+    double_quant: bool = field(
+        default=True,
+        metadata={"help": "Compress the quantization statistics through double quantization."}
+    )
+    quant_type: str = field(
+        default="nf4",
+        metadata={"help": "Quantization data type to use. Should be one of `fp4` or `nf4`."}
+    )
+    bits: int = field(
+        default=16,
+        metadata={"help": "How many bits to use."}
+    )
+    lora_enable: bool = False
+    vision_lora: bool = False
+    use_dora: bool = False
+    lora_rank: int = 64
+    lora_alpha: int = 16
+    lora_dropout: float = 0.05
+    lora_weight_path: str = ""
+    lora_bias: str = "none"
+    vision_lr: Optional[float] = None
+    merger_lr: Optional[float] = None
+    lora_namespan_exclude: str = field(default=None, metadata={"help": "List of namespan to exclude for LoRA"})
+    num_lora_modules: int = -1
+    use_liger: bool = True
+
+
+@dataclass
+class DataArguments:
+    data_path: str = field(
+        default=None, metadata={"help": "Path to the training data."}
+    )
+    lazy_preprocess: bool = False
+    image_folder: Optional[str] = field(default=None)
+    image_min_pixels: Optional[int] = field(default=3136)
+    image_max_pixels: Optional[int] = field(default=12845056)
+    video_min_pixels: Optional[int] = field(default=100352)
+    video_max_pixels: Optional[int] = field(default=602112)
+    fps: float = 1.0