Update README.md

README.md

@@ -10,19 +10,23 @@ Xwin-LM: Powerful, Stable, and Reproducible LLM Alignment
   <a href="https://huggingface.co/Xwin-LM">
     <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue">
   </a>
+  <a href="https://github.com/Xwin-LM/Xwin-LM">
+    <img src="https://img.shields.io/badge/GitHub-yellow.svg?style=social&logo=github">
+  </a>
 </p>
 
 
 
 **Step up your LLM alignment with Xwin-LM!**
 
-Xwin-LM aims to develop and open-source alignment technologies for large language models, including supervised fine-tuning (SFT), reward models, reject sampling, reinforcement learning, etc. Our first release, built-upon on the Llama2 base models, ranked **TOP-1** on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/). Notably, it's **the first to surpass GPT-4** on this benchmark. The project will be continuously updated.
+Xwin-LM aims to develop and open-source alignment technologies for large language models, including supervised fine-tuning (SFT), reward models (RM), reject sampling, reinforcement learning from human feedback (RLHF), etc. Our first release, built-upon on the Llama2 base models, ranked **TOP-1** on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/). Notably, it's **the first to surpass GPT-4** on this benchmark. The project will be continuously updated.
 
 ## News
 
-- :boom: [Sep, 2023] We released [Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1), which has achieved a win-rate against Davinci-003 of **95.57%** on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmark, ranking as **TOP-1** on AlpacaEval. **It was the FIRST model surpassing GPT-4** on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/). Also note its winrate v.s. GPT-4 is **60.61**.
-- :boom: [Sep, 2023] We released [Xwin-LM-13B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-13B-V0.1), which has achieved **91.76%** win-rate on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/), ranking as **top-1** among all 13B models.
-- :boom: [Sep, 2023] We released [Xwin-LM-7B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-7B-V0.1), which has achieved **87.82%** win-rate on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/), ranking as **top-1** among all 7B models.
+- 💥 [Sep, 2023] We released [Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1), which has achieved a win-rate against Davinci-003 of **95.57%** on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmark, ranking as **TOP-1** on AlpacaEval. **It was the FIRST model surpassing GPT-4** on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/). Also note its winrate v.s. GPT-4 is **60.61**.
+- 🔍 [Sep, 2023] RLHF plays crucial role in the strong performance of Xwin-LM-V0.1 release!
+- 💥 [Sep, 2023] We released [Xwin-LM-13B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-13B-V0.1), which has achieved **91.76%** win-rate on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/), ranking as **top-1** among all 13B models.
+- 💥 [Sep, 2023] We released [Xwin-LM-7B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-7B-V0.1), which has achieved **87.82%** win-rate on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/), ranking as **top-1** among all 7B models.
 
 
 ## Model Card
@@ -60,19 +64,22 @@ The table below displays the performance of Xwin-LM on [AlpacaEval](https://tats
 
 ### Xwin-LM performance on NLP foundation tasks.
 
-The following table provides a comparison of Xwin-LMs with other LLMs on NLP foundation tasks. 
+The following table provides a comparison of Xwin-LMs with other LLMs on NLP foundation tasks in [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). 
 
 | Model            | MMLU 5-shot | ARC 25-shot | TruthfulQA 0-shot | HellaSwag 10-shot | Average    |
 |------------------|-------------|-------------|-------------------|-------------------|------------|
-| Text-davinci-003 | <u>56.9<u/> | **85.2**    | **59.3**          | <u>82.2<u/>       | **70.9**   |
+| Text-davinci-003 | 56.9 | **85.2**    | 59.3         | 82.2       | 70.9  |
 |Vicuna-13b 1.1   | 51.3        | 53.0        | 51.8              | 80.1              | 59.1       |
-|Guanaco 30B   | 57.6        | 63.7        | 50.7              | **85.1**              | 64.3       |   
+|Guanaco 30B   | 57.6        | 63.7        | 50.7              | 85.1              | 64.3       |   
 | WizardLM-7B 1.0      | 42.7        | 51.6        | 44.7              | 77.7              | 54.2       |
 | WizardLM-13B 1.0     | 52.3        | 57.2        | 50.5              | 81.0              | 60.2       |
-| WizardLM-30B 1.0    | **58.8**    | <u>62.5<u/> | <u>52.4<u/>       | 83.3          | <u>64.2<u/>|
-| **Xwin-LM-7B-V0.1**      | 49.7        | 56.2        | 48.1              | 79.5              | 58.4       |
-| **Xwin-LM-13B-V0.1**      | -        | -        | -              | -              | -       |
-| **Xwin-LM-70B-V0.1**      | -        | -        | -              | -              | -       |
+| WizardLM-30B 1.0    | 58.8    | 62.5 | 52.4       | 83.3          | 64.2|
+| Llama-2-7B-Chat      | 48.3        | 52.9        | 45.6     | 78.6    | 56.4       |
+| Llama-2-13B-Chat      | 54.6        | 59.0        | 44.1     | 81.9    | 59.9       |
+| Llama-2-70B-Chat      | 63.9        | 64.6        | 52.8       | 85.9   | 66.8       |
+| **Xwin-LM-7B-V0.1**      | 49.7        | 56.2        | 48.1     | 79.5    | 58.4       |
+| **Xwin-LM-13B-V0.1**      | 56.6        | 62.4        | 45.5     | 83.0    | 61.9       |
+| **Xwin-LM-70B-V0.1**      | **69.6**        | 70.5        | **60.1**       | **87.1**   | **71.8**       |
 
 
 ## Inference
@@ -85,7 +92,7 @@ A chat between a curious user and an artificial intelligence assistant. The assi
 
 ### HuggingFace Example
 
-```
+```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 model = AutoModelForCausalLM.from_pretrained("Xwin-LM/Xwin-LM-7B-V0.1")
@@ -106,7 +113,7 @@ print(output)
 
 ### vllm Example
 Because Xwin-LM is based on Llama2, it also offers support for rapid inference using [vllm](https://github.com/vllm-project/vllm). Please refer to [vllm](https://github.com/vllm-project/vllm) for detailed installation instructions.
-```
+```python
 from vllm import LLM, SamplingParams
 (
     prompt := "A chat between a curious user and an artificial intelligence assistant. "
@@ -124,6 +131,10 @@ for output in outputs:
     print(generated_text)
 ```
 
+## TODO
+
+- [ ] Release the source code
+- [ ] Release more capabilities, such as math, reasoning, and etc.
 
 ## Citation
 Please consider citing our work if you use the data or code in this repo.