| """ | |
| Usage: | |
| srun -p INTERN2 --job-name='convert_2_fp16' --gres=gpu:0 --cpus-per-task=8 --quotatype="auto" python -u husky/convert_reward_fp16.py --in-checkpoint work_dirs/llm/Ziya-LLaMA-7B-Reward --out-checkpoint work_dirs/llm/reward_model | |
| """ | |
| import argparse | |
| import os.path | |
| from transformers import LlamaTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| def convert_fp16(in_checkpoint, out_checkpoint): | |
| tokenizer = LlamaTokenizer.from_pretrained(in_checkpoint, use_fast=False) | |
| model = AutoModelForSequenceClassification.from_pretrained( | |
| in_checkpoint, torch_dtype=torch.float16, low_cpu_mem_usage=False | |
| ) | |
| if not os.path.exists(out_checkpoint): | |
| os.mkdir(out_checkpoint) | |
| model.save_pretrained(out_checkpoint) | |
| tokenizer.save_pretrained(out_checkpoint) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--in-checkpoint", type=str, help="Path to the model") | |
| parser.add_argument("--out-checkpoint", type=str, help="Path to the output model") | |
| args = parser.parse_args() | |
| convert_fp16(args.in_checkpoint, args.out_checkpoint) | |