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README.md
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Quantization made by Richard Erkhov.
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[Github](https://github.com/RichardErkhov)
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[Discord](https://discord.gg/pvy7H8DZMG)
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[Request more models](https://github.com/RichardErkhov/quant_request)
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cut-13b - GGUF
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- Model creator: https://huggingface.co/xww033/
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- Original model: https://huggingface.co/xww033/cut-13b/
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [cut-13b.Q2_K.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q2_K.gguf) | Q2_K | 4.52GB |
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| [cut-13b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q3_K_S.gguf) | Q3_K_S | 5.27GB |
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| [cut-13b.Q3_K.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q3_K.gguf) | Q3_K | 5.9GB |
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| [cut-13b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q3_K_M.gguf) | Q3_K_M | 5.9GB |
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| [cut-13b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q3_K_L.gguf) | Q3_K_L | 6.45GB |
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| [cut-13b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.IQ4_XS.gguf) | IQ4_XS | 6.54GB |
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| [cut-13b.Q4_0.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q4_0.gguf) | Q4_0 | 6.86GB |
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| [cut-13b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.IQ4_NL.gguf) | IQ4_NL | 6.9GB |
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| [cut-13b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q4_K_S.gguf) | Q4_K_S | 6.91GB |
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| [cut-13b.Q4_K.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q4_K.gguf) | Q4_K | 7.33GB |
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| [cut-13b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q4_K_M.gguf) | Q4_K_M | 7.33GB |
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| [cut-13b.Q4_1.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q4_1.gguf) | Q4_1 | 7.61GB |
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| [cut-13b.Q5_0.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q5_0.gguf) | Q5_0 | 8.36GB |
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| [cut-13b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q5_K_S.gguf) | Q5_K_S | 8.36GB |
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| [cut-13b.Q5_K.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q5_K.gguf) | Q5_K | 8.6GB |
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| [cut-13b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q5_K_M.gguf) | Q5_K_M | 8.6GB |
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| [cut-13b.Q5_1.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q5_1.gguf) | Q5_1 | 9.1GB |
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| [cut-13b.Q6_K.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q6_K.gguf) | Q6_K | 9.95GB |
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| [cut-13b.Q8_0.gguf](https://huggingface.co/RichardErkhov/xww033_-_cut-13b-gguf/blob/main/cut-13b.Q8_0.gguf) | Q8_0 | 12.88GB |
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Original model description:
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---
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license: apache-2.0
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---
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# Reasons to Reject? Aligning Language Models with Judgments.
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This repository contains the CUT model from our work,
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[Reasons to Reject? Aligning Language Models with Judgments](https://arxiv.org/abs/2312.14591).
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Weiwen Xu, Deng Cai, Zhisong Zhang, Wai Lam, Shuming Shi
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The source codes can be found in https://github.com/wwxu21/CUT
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****
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## 1. Model description
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This model achieves 91.36 on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval).
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It is tuned after 4 iterations of online alignment. In each iteration, we apply the following three steps:
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- Step 1: Collect instructions, and obtain the responses from the target model.
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- Step 2: Annotate judgments for the responses.
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- Step 3: Apply CUT to fine-tune the target model with the above instruction-response-judgment triplets.
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Specifically, 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).
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To avoid over-fitting, we ensure that the sampled data are different in each iteration.
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We then ask GPT4 for the judgment annotation.
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## 2. Template
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The CUT model is a chat model and it uses the following [Alpaca template](https://github.com/tatsu-lab/stanford_alpaca):
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{instruction}
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### Response:
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```
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### 3. How to use
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#### 3.1. Huggingface
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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torch.set_default_device("cuda")
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model = AutoModelForCausalLM.from_pretrained("xww033/cut-13b", torch_dtype=torch.float16)
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tokenizer = AutoTokenizer.from_pretrained("xww033/cut-13b")
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inputs = tokenizer('''Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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How did US states get their names?
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### Response:''', return_tensors="pt", return_attention_mask=False)
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outputs = model.generate(**inputs, max_length=2048)
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text = tokenizer.batch_decode(outputs)[0]
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print(text)
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```
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#### 3.2. FastChat
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[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:
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```bash
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git clone https://github.com/lm-sys/FastChat.git
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cd FastChat
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```
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Download the required packages:
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```bash
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pip install --upgrade pip # enable PEP 660 support
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pip install -e .
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```
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Finally, run the following:
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```bash
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python -m fastchat.serve.cli --model-path xww033/cut-13b --conv-template alpaca
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```
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### 4. BibTeX entry and citation info
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```bibtxt
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@article{xu2023reasons,
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title={Reasons to Reject? Aligning Language Models with Judgments},
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author={Xu, Weiwen and Cai, Deng and Zhang, Zhisong and Lam, Wai and Shi, Shuming},
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journal={arXiv preprint arXiv:2312.14591},
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year={2023}
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}
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```
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