Create new_review_code.py

new_review_code.py

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+import os
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments,BitsAndBytesConfig
+from datasets import load_dataset
+from trl import SFTTrainer
+from peft import AutoPeftModelForCausalLM, LoraConfig, get_peft_model, prepare_model_for_kbit_training
+from utils import find_all_linear_names, print_trainable_parameters
+
+output_dir="./results"
+model_name ="codellama/CodeLlama-7b-hf"
+
+dataset = load_dataset('timdettmers/openassistant-guanaco', split="train")
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+
+base_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, quantization_config=bnb_config)
+base_model.config.use_cache = False
+base_model = prepare_model_for_kbit_training(base_model)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"  # Fix weird overflow issue with fp16 training
+
+# Change the LORA hyperparameters accordingly to fit your use case
+peft_config = LoraConfig(
+    r=32,
+    lora_alpha=16,
+    target_modules=find_all_linear_names(base_model),
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+base_model = get_peft_model(base_model, peft_config)
+print_trainable_parameters(base_model)
+
+# Parameters for training arguments details => https://github.com/huggingface/transformers/blob/main/src/transformers/training_args.py#L158
+training_args = TrainingArguments(
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=1,
+    gradient_checkpointing =True,
+    max_grad_norm= 0.3,
+    num_train_epochs=3, 
+    learning_rate=1e-4,
+    bf16=True,
+    save_total_limit=3,
+    logging_steps=300,
+    output_dir=output_dir,
+    optim="paged_adamw_32bit",
+    lr_scheduler_type="constant",
+    warmup_ratio=0.05,
+)
+
+trainer = SFTTrainer(
+    base_model,
+    train_dataset=dataset,
+    dataset_text_field="text",
+    tokenizer=tokenizer,
+    max_seq_length=512,
+    args=training_args
+)
+
+trainer.train() 
+trainer.save_model(output_dir)
+
+output_dir = os.path.join(output_dir, "final_checkpoint")
+trainer.model.save_pretrained(output_dir)
+tokenizer.save_pretrained(output_dir)