README.md

---
license: gemma
library_name: transformers
pipeline_tag: text-generation
base_model: google/gemma-2-9b-it
language:
- en
- zh
tags:
- llama-factory
- orpo
---



# Model Summary

Gemma-2-9B-Chinese-Chat is the first instruction-tuned language model built upon [google/gemma-2-9b-it](https://huggingface.co./google/gemma-2-9b-it) for Chinese & English users with various abilities such as roleplaying & tool-using.

Developed by: [Shenzhi Wang](https://shenzhi-wang.netlify.app) (王慎执) and [Yaowei Zheng](https://github.com/hiyouga) (郑耀威)

- License: [Gemma License](https://ai.google.dev/gemma/terms)
- Base Model: [google/gemma-2-9b-it](https://huggingface.co./google/gemma-2-9b-it)
- Model Size: 9.24B
- Context length: 8K

# 1. Introduction

This is the first model specifically fine-tuned for Chinese & English users based on the [google/gemma-2-9b-it](https://huggingface.co./google/gemma-2-9b-it) with a preference dataset with more than 100K preference pairs. The fine-tuning algorithm we employ is ORPO [1].

**Compared to the original [google/gemma-2-9b-it](https://huggingface.co./google/gemma-2-9b-it), our Gemma-2-9B-Chinese-Chat model significantly reduces the issues of "Chinese questions with English answers" and the mixing of Chinese and English in responses, with enhanced performance in roleplay, tool using, and math.**

[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](https://github.com/hiyouga/LLaMA-Factory).

Training details:

- epochs: 3
- learning rate: 3e-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

Please upgrade the `transformers` package to ensure it supports Gemma-2 models. The current version we are using is `4.42.2`.

```python
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "shenzhi-wang/Gemma-2-9B-Chinese-Chat"
dtype = torch.bfloat16

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

chat = [
    {"role": "user", "content": "写一首关于机器学习的诗。"},
]
input_ids = tokenizer.apply_chat_template(
    chat, tokenize=True, 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))
```