README.md

基于中英文混合语料增量训练，词表扩充汉字。

训练细节和benchmark指标： https://github.com/CVI-SZU/Linly


```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Linly-AI/Chinese-LLaMA-2-7B-hf", device_map="cuda:0", torch_dtype=torch.float16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Linly-AI/Chinese-LLaMA-2-7B-hf", use_fast=False, trust_remote_code=True)
prompt = "北京有什么好玩的地方？"

prompt = f"### Instruction:{prompt.strip()}  ### Response:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
generate_ids = model.generate(inputs.input_ids, do_sample=True, max_new_tokens=2048, top_k=10, top_p=0.85, temperature=1, repetition_penalty=1.15, eos_token_id=2, bos_token_id=1, pad_token_id=0)
response = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
response = response.lstrip(prompt)
```


# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Linly-AI__Chinese-LLaMA-2-7B-hf)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 42.44   |
| ARC (25-shot)         | 48.04          |
| HellaSwag (10-shot)   | 73.25    |
| MMLU (5-shot)         | 35.04         |
| TruthfulQA (0-shot)   | 39.92   |
| Winogrande (5-shot)   | 70.17   |
| GSM8K (5-shot)        | 6.22        |
| DROP (3-shot)         | 24.46         |