Update README.md

README.md

@@ -138,24 +138,37 @@ Thank you to all my generous patrons and donaters!
 
 # Original model card: WizardLM's WizardCoder 15B 1.0
 
+This is the Full-Weight of WizardCoder.
+
+**Repository**: https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder
+
+**Twitter**: https://twitter.com/WizardLM_AI/status/1669109414559911937
+
+**Paper**: Is coming, with brand-new Evol+ methods for code LLMs.
+
+**Demos (Only support code-related English instructions now.)**: 
+
+[Demo](https://8194635813f45a1e.gradio.app/), 
+[Backup Demo1](https://375cead61e4db124.gradio.app/), 
+[Backup Demo2](https://1594ad375fc80cc7.gradio.app/),
+[Backup Demo3](https://4989441110ee350f.gradio.app/)
+
+
+
 # WizardCoder: Empowering Code Large Language Models with Evol-Instruct
 
-[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE)
-[![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE)
-[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/)
 
 To develop our WizardCoder model, we begin by adapting the Evol-Instruct method specifically for coding tasks. This involves tailoring the prompt to the domain of code-related instructions. Subsequently, we fine-tune the Code LLM, StarCoder, utilizing the newly created instruction-following training set.
 
 ## News
 
 - 🔥 Our **WizardCoder-15B-v1.0** model achieves the **57.3 pass@1** on the [HumanEval Benchmarks](https://github.com/openai/human-eval), which is **22.3** points higher than the SOTA open-source Code LLMs.
-- 🔥 We released **WizardCoder-15B-v1.0** trained with **78k** evolved code instructions. Please checkout the [Model Weights](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0), [Demo](https://1c48cbf5c83110ed.gradio.app/), and [Paper]().
+- 🔥 We released **WizardCoder-15B-v1.0** trained with **78k** evolved code instructions. Please checkout the [Model Weights](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0), and [Paper]().
 - 📣 Please refer to our Twitter account https://twitter.com/WizardLM_AI and HuggingFace Repo https://huggingface.co/WizardLM . We will use them to announce any new release at the 1st time. 
 
 
 ## Comparing WizardCoder with the Closed-Source Models.
 
-The SOTA LLMs for code generation, such as GPT4, Claude, and Bard, are predominantly closed-source. Acquiring access to the APIs of these models proves challenging. In this study, we adopt an alternative approach by retrieving the scores for HumanEval and HumanEval+ from the [LLM-Humaneval-Benchmarks](https://github.com/my-other-github-account/llm-humaneval-benchmarks). Notably, all the mentioned models generate code solutions for each problem utilizing a single attempt, and the resulting pass rate percentage is reported. Our **WizardCoder** generates answers using greedy decoding.
 
 🔥 The following figure shows that our **WizardCoder attains the third position in this benchmark**, surpassing Claude-Plus (59.8 vs. 53.0) and Bard (59.8 vs. 44.5). Notably, our model exhibits a substantially smaller size compared to these models.
 
@@ -163,9 +176,11 @@ The SOTA LLMs for code generation, such as GPT4, Claude, and Bard, are predomina
 <a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/WizardCoder/imgs/pass1.png" alt="WizardCoder" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
+❗**Note: In this study, we copy the scores for HumanEval and HumanEval+ from the [LLM-Humaneval-Benchmarks](https://github.com/my-other-github-account/llm-humaneval-benchmarks). Notably, all the mentioned models generate code solutions for each problem utilizing a **single attempt**, and the resulting pass rate percentage is reported. Our **WizardCoder** generates answers using greedy decoding and tests with the same [code](https://github.com/evalplus/evalplus).**
+
 ## Comparing WizardCoder with the Open-Source Models.
 
-The following table conducts a comprehensive comparison of our **WizardCoder** with other models on the HumanEval and MBPP benchmarks. We adhere to the approach outlined in previous studies by generating n samples for each problem to estimate the pass@1 score. The findings clearly demonstrate that our **WizardCoder** exhibits a substantial performance advantage over all the open-source models.
+The following table clearly demonstrates that our **WizardCoder** exhibits a substantial performance advantage over all the open-source models. ❗**If you are confused with the different scores of our model (57.3 and 59.8), please check the Notes.**
 
 
 | Model            | HumanEval Pass@1 | MBPP Pass@1 |
@@ -184,7 +199,10 @@ The following table conducts a comprehensive comparison of our **WizardCoder** w
 | WizardLM-30B  1.0| 37.8             |--           |
 | WizardCoder-15B  1.0 | **57.3**     |**51.8**     |
 
-*： The reproduced result of StarCoder on MBPP.
+
+❗**Note: The reproduced result of StarCoder on MBPP.**
+
+❗**Note: The above table conducts a comprehensive comparison of our **WizardCoder** with other models on the HumanEval and MBPP benchmarks. We adhere to the approach outlined in previous studies by generating **20 samples** for each problem to estimate the pass@1 score and evaluate with the same [code](https://github.com/openai/human-eval/tree/master). The scores of GPT4 and GPT3.5 reported by [OpenAI](https://openai.com/research/gpt-4) are 67.0 and 48.1 (maybe these are the early version GPT4&3.5).**
 
 ## Call for Feedbacks
 We welcome everyone to use your professional and difficult instructions to evaluate WizardCoder, and show us examples of poor performance and your suggestions in the [issue discussion](https://github.com/nlpxucan/WizardLM/issues) area. We are focusing on improving the Evol-Instruct now and hope to relieve existing weaknesses and issues in the the next version of WizardCoder. After that, we will open the code and pipeline of up-to-date Evol-Instruct algorithm and work with you together to improve it.
@@ -208,7 +226,7 @@ We welcome everyone to use your professional and difficult instructions to evalu
 
 We will provide our latest models for you to try for as long as possible. If you find a link is not working, please try another one. At the same time, please try as many **real-world** and **challenging** code-related problems that you encounter in your work and life as possible. We will continue to evolve our models with your feedbacks.
 
-[Demo Link](https://1c48cbf5c83110ed.gradio.app/) (We adopt the greedy decoding now.)
+
 
 ## Fine-tuning
 
@@ -350,4 +368,4 @@ Please cite the repo if you use the data or code in this repo.
 ```
 ## Disclaimer
 
-The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of WizardCoder is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.
+The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of WizardCoder is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.