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README.md

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 license: apache-2.0
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+# Reasons to Reject? Aligning Language Models with Judgments.
+This repository contains the CUT model from our work,
+
+[Reasons to Reject? Aligning Language Models with Judgments](https://arxiv.org/abs/2312.14591).
+
+Weiwen Xu, Deng Cai, Zhisong Zhang, Wai Lam, Shuming Shi
+
+The source codes can be found in https://github.com/wwxu21/CUT 
+**** 
+
+## 1. Model description
+
+The model is tuned after 4 iterations of online alignment. In each iteration, we apply the following three steps:
+
+- Step 1: Collect instructions, and obtain the responses from the target model.
+
+- Step 2: Annotate judgments for the responses.
+
+- Step 3: Apply CUT to fine-tune the target model with the above instruction-response-judgment triplets.
+
+We use [LLaMA2-chat-13b](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) as the base LLM. In each iteration, we sample 1000 instructions from [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca).
+To avoid over-fitting, we ensure that the sampled data are different in each iteration.
+We then ask GPT4 for the judgment annotation.
+
+
+## 2. Intended uses & limitations
+The CUT model is a chat model and it uses the following [Alpaca template](https://github.com/tatsu-lab/stanford_alpaca):
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:
+```
+
+### 3. How to use
+
+#### 1. Huggingface
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+torch.set_default_device("cuda")
+
+model = AutoModelForCausalLM.from_pretrained("xww033/cut-13b", torch_dtype=torch.float16)
+tokenizer = AutoTokenizer.from_pretrained("xww033/cut-13b")
+
+inputs = tokenizer('''Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+How did US states get their names?
+
+### Response:''', return_tensors="pt", return_attention_mask=False)
+
+outputs = model.generate(**inputs, max_length=200)
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+```
+
+#### 2. FastChat
+
+[Fastchat](https://github.com/lm-sys/FastChat) provides a simple setup for those interested in trying our aligned model. After downloading the [CUT model](https://huggingface.co/xww033/cut-13b) through HuggingFace, clone the Fastchat repository:
+
+```bash
+git clone https://github.com/lm-sys/FastChat.git
+cd FastChat
+```
+
+Download the required packages:
+
+```bash
+pip install --upgrade pip  # enable PEP 660 support
+pip install -e .
+```
+
+Finally, run the following:
+
+```bash
+python -m fastchat.serve.cli --model-path xww033/cut-13b --conv-template alpaca
+```
+
+
+### 4. BibTeX entry and citation info
+```bibtxt
+@article{xu2023reasons,
+  title={Reasons to Reject? Aligning Language Models with Judgments},
+  author={Xu, Weiwen and Cai, Deng and Zhang, Zhisong and Lam, Wai and Shi, Shuming},
+  journal={arXiv preprint arXiv:2312.14591},
+  year={2023}
+}
+```