Update README.md

README.md

@@ -74,13 +74,13 @@ So if you're able and willing to contribute, it'd be most gratefully received an
 * Patreon: coming soon! (just awaiting approval)
 * Ko-Fi: https://ko-fi.com/TheBlokeAI
    
-# Original model card
+# Original model card: WizardLM 13B 1.0
 
 ## WizardLM: An Instruction-following LLM Using Evol-Instruct
 Empowering Large Pre-Trained Language Models to Follow Complex Instructions
 
 <p align="center" width="100%">
-<a ><img src="imgs/WizardLM.png" alt="WizardLM" style="width: 20%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/WizardLM.png" alt="WizardLM" style="width: 20%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE)
@@ -103,9 +103,9 @@ At present, our core contributors are preparing the **33B** version and we expec
 
 ### GPT-4 automatic evaluation
 
-We adopt the automatic evaluation framework based on GPT-4 proposed by FastChat to assess the performance of chatbot models. As shown in the following figure, WizardLM-13B achieved better results than Vicuna-13b. 
+We adopt the automatic evaluation framework based on GPT-4 proposed by FastChat to assess the performance of chatbot models. As shown in the following figure, WizardLM-13B achieved better results than Vicuna-13b.
 <p align="center" width="100%">
-<a ><img src="imgs/WizarLM13b-GPT4.png" alt="WizardLM" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/WizarLM13b-GPT4.png" alt="WizardLM" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 ### WizardLM-13B performance on different skills.
@@ -113,7 +113,7 @@ We adopt the automatic evaluation framework based on GPT-4 proposed by FastChat
 The following figure compares WizardLM-13B and ChatGPT’s skill on Evol-Instruct testset. The result indicates that WizardLM-13B achieves 89.1% of ChatGPT’s performance on average, with almost 100% (or more than) capacity on 10 skills, and more than 90% capacity on 22 skills.
 
 <p align="center" width="100%">
-<a ><img src="imgs/evol-testset_skills-13b.png" alt="WizardLM" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/evol-testset_skills-13b.png" alt="WizardLM" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 ## Call for Feedbacks
@@ -132,11 +132,11 @@ We just sample some cases to demonstrate the performance of WizardLM and ChatGPT
 [Evol-Instruct](https://github.com/nlpxucan/evol-instruct) is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.
 
 <p align="center" width="100%">
-<a ><img src="imgs/git_overall.png" alt="WizardLM" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/git_overall.png" alt="WizardLM" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 <p align="center" width="100%">
-<a ><img src="imgs/git_running.png" alt="WizardLM" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/git_running.png" alt="WizardLM" style="width: 86%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 ## Contents
@@ -184,7 +184,7 @@ This JSON file is a list of dictionaries, each dictionary contains the following
 We release [WizardLM] weights as delta weights to comply with the LLaMA model license.
 You can add our delta to the original LLaMA weights to obtain the WizardLM weights. Instructions:
 1. Get the original LLaMA weights in the huggingface format by following the instructions [here](https://huggingface.co/docs/transformers/main/model_doc/llama).
-2. Please download our delta model at the following [link](https://huggingface.co/victor123/WizardLM) 
+2. Please download our delta model at the following [link](https://huggingface.co/victor123/WizardLM)
 3. Use the following scripts to get WizardLM weights by applying our delta:
 ```
 python src/weight_diff_wizard.py recover --path_raw <path_to_step_1_dir> --path_diff <path_to_step_2_dir> --path_tuned <path_to_store_recovered_weights>
@@ -247,16 +247,16 @@ python src\inference_wizardlm.py
 
 ### Evaluation
 
-To evaluate Wizard, we conduct human evaluation on the inputs from our human instruct evaluation set [`WizardLM_testset.jsonl`](./data/WizardLM_testset.jsonl) . This evaluation set was collected by the authors and covers a diverse list of user-oriented instructions including difficult Coding Generation & Debugging, Math, Reasoning, Complex Formats, Academic Writing, Extensive Disciplines, and so on. We performed a blind pairwise comparison between Wizard and baselines. Specifically, we recruit 10 well-educated annotators to rank the models from 1 to 5 on relevance, knowledgeable, reasoning, calculation and accuracy. 
+To evaluate Wizard, we conduct human evaluation on the inputs from our human instruct evaluation set [`WizardLM_testset.jsonl`](./data/WizardLM_testset.jsonl) . This evaluation set was collected by the authors and covers a diverse list of user-oriented instructions including difficult Coding Generation & Debugging, Math, Reasoning, Complex Formats, Academic Writing, Extensive Disciplines, and so on. We performed a blind pairwise comparison between Wizard and baselines. Specifically, we recruit 10 well-educated annotators to rank the models from 1 to 5 on relevance, knowledgeable, reasoning, calculation and accuracy.
 
-WizardLM achieved significantly better results than Alpaca and Vicuna-7b. 
+WizardLM achieved significantly better results than Alpaca and Vicuna-7b.
 <p align="center" width="60%">
-<a ><img src="imgs/win.png" alt="WizardLM" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/win.png" alt="WizardLM" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 In the high-difficulty section of our test set (difficulty level >= 8), WizardLM even outperforms ChatGPT, with a win rate 7.9% larger than Chatgpt (42.9% vs. 35.0%). This indicates that our method can significantly improve the ability of large language models to handle complex instructions.
 <p align="center" width="60%">
-<a ><img src="imgs/windiff.png" alt="WizardLM" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
+<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/imgs/windiff.png" alt="WizardLM" style="width: 60%; min-width: 300px; display: block; margin: auto;"></a>
 </p>
 
 ### Citation
@@ -265,7 +265,7 @@ Please cite the repo if you use the data or code in this repo.
 
 ```
 @misc{xu2023wizardlm,
-      title={WizardLM: Empowering Large Language Models to Follow Complex Instructions}, 
+      title={WizardLM: Empowering Large Language Models to Follow Complex Instructions},
       author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
       year={2023},
       eprint={2304.12244},
@@ -275,4 +275,4 @@ 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 WizardLM 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 WizardLM 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.