Delete utilities/modeling.py

utilities/modeling.py

@@ -1,90 +0,0 @@
-from unsloth import FastLanguageModel
-import torch
-from trl import SFTTrainer
-from transformers import TrainingArguments
-from unsloth import is_bfloat16_supported
-
-
-def load_model(model_name, max_seq_length):
-    dtype = None 
-    load_in_4bit = True
-    
-    model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name = model_name,
-        max_seq_length = max_seq_length,
-        dtype = dtype,
-        load_in_4bit = load_in_4bit,
-        # token = ""
-    )
-    return model, tokenizer
-
-
-def get_peft(model, peft, max_seq_length, random_seed):
-    
-    model = FastLanguageModel.get_peft_model(
-    model,
-    r = peft['r',]
-    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
-                      "gate_proj", "up_proj", "down_proj",],
-    lora_alpha = peft['alpha'],
-    lora_dropout = peft['dropout'],
-    bias = peft['bias'], 
-    use_gradient_checkpointing = "unsloth",
-    random_state = random_seed,
-    use_rslora = peft['rslora'],  # We support rank stabilized LoRA
-    loftq_config = peft['loftq_config'], # And LoftQ
-    )
-    return model
-
-
-def get_trainer(model, tokenizer, dataset, sft, 
-                data_field, max_seq_length, random_seed,
-                num_epochs, max_steps):
-    
-    trainer = SFTTrainer(
-        model = model,
-        tokenizer = tokenizer,
-        train_dataset = dataset,
-        dataset_text_field = data_field,
-        max_seq_length = max_seq_length,
-        dataset_num_proc = 2,
-        packing = False,
-        args = TrainingArguments(
-            per_device_train_batch_size = sft['per_device_train_batch_size'],
-            gradient_accumulation_steps = sft['gradient_accumulation_steps'],
-            warmup_steps = sft['warmup_steps'],
-            num_train_epochs = num_epochs,
-            max_steps = max_steps,
-            learning_rate = sft['learning_rate'],
-            fp16 = not is_bfloat16_supported(),
-            bf16 = is_bfloat16_supported(),
-            logging_steps = sft['logging_steps'],
-            optim = sft['optim'],
-            weight_decay = sft['weight_decay'],
-            lr_scheduler_type = sft['lr_scheduler_type'],
-            seed = random_seed,
-            output_dir = "outputs",
-        ),
-    )
-    return trainer
-
-
-def prepare_trainer(model_name, max_seq_length, random_seed,
-                    num_epochs, max_steps,
-                    peft, sft, dataset, data_field):
-
-    print("Loading Model")
-    model, tokenizer = load_model(model_name, max_seq_length)
-
-    print("Preparing for PEFT")
-    model = get_peft(model, peft, max_seq_length, random_seed)
-
-    print("Getting Trainer Model")
-    trainer = get_trainer(model, tokenizer, dataset, data_field, max_seq_length, random_seed,
-                          num_epochs, max_steps)
-
-    return trainer
-
-if __name__ == "__main__":
-    trainer = prepare_trainer()
-