calc9

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Model Details

@dataclass class SFTConfig: sft_model_name: str = 'facebook/opt-350m' sft_dataset_path: str = 'train.csv' sft_model_cache_dir: str = 'cache' sft_output_dir: str = '.' hf_key: str = ''

peft_config = LoraConfig(
    r=6, # TODO: play with this number 
    lora_alpha= 11, # TODO: play with this number 
    target_modules=['q_proj', 'v_proj', 'k_proj'],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM" # TODO: you need to figure this out. HINT https://github.com/huggingface/peft/blob/3d2bf9a8b261ed2960f26e61246cf0aa624a6115/src/peft/utils/peft_types.py#L67
)
    
training_args = TrainingArguments(
    per_device_train_batch_size=2,
    gradient_accumulation_steps=2,
    gradient_checkpointing =False,
    max_grad_norm= 0.3,
    num_train_epochs=1, # TODO: play with this number 
    save_steps= 100,
    learning_rate=0.0004, # TODO: play with this number 
    bf16=True,
    save_total_limit=3,
    logging_steps=10,
    output_dir='./sft_models',
    optim="adamw_torch",
    lr_scheduler_type="cosine",
    warmup_ratio=0.05,
    remove_unused_columns=False,
    report_to="none",
)

generate_max_length: int = 64

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