Update README.md

README.md

@@ -1,34 +1,31 @@
-<h1 align="center">AutoGPTQ</h1>
-<p align="center">An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.</p>
-<p align="center">
-    <a href="https://github.com/PanQiWei/AutoGPTQ/releases">
-        <img alt="GitHub release" src="https://img.shields.io/github/release/PanQiWei/AutoGPTQ.svg">
-    </a>
-    <a href="https://pypi.org/project/auto-gptq/">
-        <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dd/auto-gptq">
-    </a>
-</p>
-<h4 align="center">
-    <p>
-        <b>English</b> |
-        <a href="https://github.com/PanQiWei/AutoGPTQ/blob/main/README_zh.md">中文</a>
-    </p>
-</h4>
-
-*<center>📣 Long time no see! 👋 Architecture upgrade, performance optimization and more new features will come in July and August, stay tune! 🥂</center>*
-
-## News or Update
-
-- 2023-08-21 - (News) - Team of Qwen officially released 4bit quantized version of Qwen-7B based on `auto-gptq`, and provided [a detailed benchmark results](https://huggingface.co/Qwen/Qwen-7B-Chat-Int4#%E9%87%8F%E5%8C%96-quantization)
-- 2023-08-06 - (Update) - Support exllama's q4 CUDA kernel to have at least 1.3x speed up for int4 quantized models when doing inference.
-- 2023-08-04 - (Update) - Support RoCm so that AMD GPU users can use auto-gptq with CUDA extensions.
-- 2023-07-26 - (Update) - An elegant [PPL benchmark script](examples/benchmark/perplexity.py) to get results that can be fairly compared with other libraries such as `llama.cpp`.
-- 2023-06-05 - (Update) - Integrate with 🤗 peft to use gptq quantized model to train adapters, support LoRA, AdaLoRA, AdaptionPrompt, etc.
-- 2023-05-30 - (Update) - Support download/upload quantized model from/to 🤗 Hub.
-
-*For more histories please turn to [here](docs/NEWS_OR_UPDATE.md)*
-
-## Performance Comparison
+---
+license: apache-2.0
+datasets:
+- PetraAI/PetraAI
+language:
+- ar
+- en
+- ch
+- zh
+metrics:
+- accuracy
+- bertscore
+- bleu
+- chrf
+- code_eval
+- brier_score
+tags:
+- chemistry
+- biology
+- finance
+- legal
+- music
+- code
+- art
+- climate
+- medical
+- text-generation-inference
+---
 
 ### Inference Speed
 > The result is generated using [this script](examples/benchmark/generation_speed.py), batch size of input is 1, decode strategy is beam search and enforce the model to generate 512 tokens, speed metric is tokens/s (the larger, the better).
@@ -336,4 +333,4 @@ pytest tests/ -s
 - Specially thanks **qwopqwop200**, for code in this project that relevant to quantization are mainly referenced from [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/cuda).
 
 
-[![Star History Chart](https://api.star-history.com/svg?repos=PanQiwei/AutoGPTQ&type=Date)](https://star-history.com/#PanQiWei/AutoGPTQ&Date)
+[![Star History Chart](https://api.star-history.com/svg?repos=PanQiwei/AutoGPTQ&type=Date)](https://star-history.com/#PanQiWei/AutoGPTQ&Date)