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README.md

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+\---
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 license: apache-2.0
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+\---
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+### Storm-7B
+
+> **Developed by**: [Jie Liu](https://jieliu.site/)$^{*1,2}$, [Zhanhui Zhou](https://scholar.google.com/citations?user=SbACfYQAAAAJ&hl=zh-CN)$^{*2}$, [Chao Yang](https://scholar.google.com/citations?user=5KRbHPMAAAAJ&hl=zh-CN)$^{2}$, [Han-Sen Zhong](https://scholar.google.com.hk/citations?user=X_ZfX8sAAAAJ&hl=zh-CN)$^{2}$, and [Wanli Ouyang](https://wlouyang.github.io/)$^{1,2}$.
+>
+> $^{1}$MMLab, The Chinese University of Hong Kong  $^{2}$Shanghai AI Laboratory
+
+#### Introduction
+
+We released Storm-7B, the first open-source language model comparable to the GPT-4 series on the [AlpacaEval 2.0](https://tatsu-lab.github.io/alpaca_eval/) leaderboard, ranking 3rd in length-controlled win rate.
+
+The recipe for this model is simple: 1) fine-tuning from [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106), 2) applying iterative DPO training, a variant of DPO where a language model iteratively learns from the preferences of the trained reward model. We will release our technical report and code as soon as possible.
+
+A snapshot of the AlpacaEval 2.0 leaderboard (2024/4/28) is listed below: 
+
+|                          | **LC Win Rate** | **Win Rate** |
+| :----------------------: | :-------------: | :----------: |
+|   GPT-4 Turbo (04/09)    |      55.0%      |    46.1%     |
+|  GPT-4 Preview (11/06)   |      50.0%      |    50.0%     |
+|       **Storm-7B**       |      48.9%      |    52.5%     |
+| Nanbeige Plus Chat v0.1  |      44.5%      |    56.7%     |
+|    Qwen1.5 110B Chat     |      43.9%      |    33.8%     |
+| Aligner 2B+Claude 3 Opus |      41.8%      |    34.5%     |
+|  Claude 3 Opus (02/29)   |      40.5%      |    29.1%     |
+|          GPT-4           |      38.1%      |    23.6%     |
+|    openchat-3.5-0106     |      15.4%      |    10.1%     |
+
+Please refer to the [leaderboard webpage](https://tatsu-lab.github.io/alpaca_eval/) for up-to-date results. 
+
+We also conducted preliminary evaluations on other benchmarks and observed no significant degradation.
+
+|                   | ARC   | HellaSwag | MMLU  | TruthfulQA | Winogrande | Avg.  |
+| ----------------- | ----- | --------- | ----- | ---------- | ---------- | ----- |
+| **Storm-7B**      | 67.58 | 80.97     | 62.21 | 57.24      | 80.51      | 69.70 |
+| openchat-3.5-0106 | 66.38 | 83.00     | 63.47 | 52.55      | 81.06      | 69.29 |
+| internlm2-7b      | 58.02 | 81.24     | 65.24 | 48.73      | 83.82      | 67.41 |
+| gemma-7B          | 61.09 | 82.20     | 64.56 | 44.79      | 79.01      | 66.33 |
+| Yi-9B             | 61.18 | 78.82     | 70.06 | 42.45      | 77.51      | 66.00 |
+| Meta-Llama-3-8B   | 59.47 | 82.09     | 66.69 | 43.90      | 77.35      | 65.90 |
+| Mistral-7B-v0.1   | 59.98 | 83.31     | 64.16 | 42.15      | 78.37      | 65.59 |
+| Qwen-7b           | 51.37 | 78.47     | 59.84 | 47.79      | 72.69      | 62.03 |
+
+#### Uses
+
+Our model uses the same chat template as [Openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106). A sample code snippet for inference using our model is provided below.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda"
+
+model = AutoModelForCausalLM.from_pretrained("jieliu/Storm-7B").to(device)
+tokenizer = AutoTokenizer.from_pretrained("jieliu/Storm-7B")
+model.eval().requires_grad_(False)
+
+def generate_response(prompt):
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+    outputs = model.generate(
+        input_ids,
+        max_length=2048,
+        do_sample=True,
+        temperature=1.0,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+    response_ids = outputs[0]
+    response_text = tokenizer.decode(response_ids, skip_special_tokens=True)
+    return response_text
+
+prompt = "I'm trying to teach myself to have nicer handwriting. Can you help?"
+input_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:"
+response_text = generate_response(input_prompt)
+print("Response:", response_text)
+```
+
+#### Limitations
+
+Storm-7B is a quick demonstration that a language model, fine-tuned with AI feedback, can easily surpass or match state-of-the-art models, as assessed by the same AI feedback. However, this improvement on the automatic leaderboard may not necessarily indicate better alignment with human intentions. Our model therefore represents a critical, preliminary reevaluation of the RLAIF paradigm, questioning how much learning from and being evaluated by AI feedback aligns with actual human preferences.
+
+#### Citation
+
+```
+@misc{liu2024storm,
+    title = {Storm-7B},
+    url = {},
+    author = {Jie Liu and Zhanhui Zhou and Chao Yang and Han-Sen Zhong and Wanli Ouyang},
+    month = {April},
+    year = {2024}
+}
+```
+