Update README.md

README.md

@@ -1,3 +1,70 @@
----
-license: llama3.1
----
+---
+license: llama3.1
+---
+
+# Model Summary
+
+llama3.1-8B-Chinese-Chat is an instruction-tuned language model for Chinese & English users with various abilities such as roleplaying & tool-using built upon the Meta-Llama-3.1-8B-Instruct model.
+
+Developers: [Shenzhi Wang](https://shenzhi-wang.netlify.app)\*, [Yaowei Zheng](https://github.com/hiyouga)\*, Guoyin Wang (in.ai), Shiji Song, Gao Huang. (\*: Equal Contribution)
+
+- License: [Llama-3.1 License](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE)
+- Base Model: Meta-Llama-3.1-8B-Instruct
+- Model Size: 8.03B
+- Context length: 8K
+
+# 1. Introduction
+
+This is the first model specifically fine-tuned for Chinese & English user through ORPO [1] based on the [Meta-Llama-3.1-8B-Instruct model](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct).
+
+**Compared to the original [Meta-Llama-3.1-8B-Instruct model](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct), our llama3.1-8B-Chinese-Chat model significantly reduces the issues of "Chinese questions with English answers" and the mixing of Chinese and English in responses.**
+
+
+[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
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_id = "shenzhi-wang/Llama3.1-8B-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))
+```