Model Summary

Llama3-70B-Chinese-Chat is one of the first instruction-tuned LLMs for Chinese & English users with various abilities such as roleplaying, tool-using, and math, built upon the meta-llama/Meta-Llama-3-70B-Instruct model.

Developed by: Shenzhi Wang (王慎执) and Yaowei Zheng (郑耀威)

• License: Llama-3 License
• Base Model: Meta-Llama-3-70B-Instruct
• Model Size: 70.6B
• Context length: 8K

1. Introduction

This is one of the first LLM fine-tuned specifically for Chinese and English users, based on the Meta-Llama-3-70B-Instruct model. The fine-tuning algorithm used is ORPO [1].

Our Llama3-70B-Chinese-Chat model was trained on a dataset containing over 100K preference pairs, with a roughly equal ratio of Chinese and English data. This dataset will be available soon.

Compared to the original Meta-Llama-3-70B-Instruct model, the Llama3-70B-Chinese-Chat model greatly reduces the issues of "Chinese questions with English answers" and the mixing of Chinese and English in responses. Additionally, Llama3-70B-Chinese-Chat excels at roleplaying, function calling, and mathematics.

With much more parameters than our Llama3-8B-Chinese-Chat model, our Llama3-70B-Chinese-Chat offers significant performance enhancements. If you enjoyed our Llama3-8B-Chinese-Chat, the Llama3-70B-Chinese-Chat is a must-try!

[1] Hong, Jiwoo, Noah Lee, and James Thorne. "Reference-free Monolithic Preference Optimization with Odds Ratio." arXiv preprint arXiv:2403.07691 (2024).

Training framework: LLaMA-Factory.

Training details:

• epochs: 3 (We also provide a 2-epoch model version at the epoch_2 branch)
• learning rate: 1.5e-6
• learning rate scheduler type: cosine
• Warmup ratio: 0.1
• cutoff len (i.e. context length): 8192
• orpo beta (i.e. \lambda in the ORPO paper): 0.05
• global batch size: 128
• fine-tuning type: full parameters
• optimizer: paged_adamw_32bit

2. Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "shenzhi-wang/Llama3-70B-Chinese-Chat"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)

messages = [
    {"role": "user", "content": "写一首诗吧"},
]

input_ids = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=8192,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))