How to view the readme file normally in vscode since I see something like this :
[**🇨🇳Chinese**](./README.md) | [**🌐English**](./README_EN.md) | [**📖Documentation**](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/wiki) | [**❓Issues**](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/issues) | [**💬Discussions**](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/discussions) | [**⚔️Arena**](http://llm-arena.ymcui.com/)

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    <img src="./pics/banner.png" width="800"/>
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    <img alt="GitHub" src="https://img.shields.io/github/license/ymcui/Chinese-LLaMA-Alpaca-3.svg?color=blue&style=flat-square">
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    <a href="https://huggingface.co/hfl">🤗 Hugging Face</a> •
    <a href="https://modelscope.cn/profile/ChineseAlpacaGroup">🤖 ModelScope</a> •
    <a href="https://sota.jiqizhixin.com/project/chinese-llama-alpaca-3">🐿️ 机器之心SOTA!模型</a> •
    <a href="https://wisemodel.cn/models/ChineseAlpacaGroup/llama-3-chinese-8b-instruct-v3">🟣 wisemodel</a> •
    <a href="https://huggingface.co/spaces/hfl-rc/llama-3-chinese-8b-instruct-demo">🤗 Online Demo</a>
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This project is developed based on Meta's newly released next-generation open-source large language model [Llama-3](https://github.com/facebookresearch/llama3) and is the third generation of the Chinese-LLaMA-Alpaca open-source LLM series ([1st gen](https://github.com/ymcui/Chinese-LLaMA-Alpaca), [2nd gen](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)). This project has open-sourced **the Llama-3-Chinese base model and the Chinese Llama-3-Chinese-Instruct instruction-tuned large model**. These models use large-scale Chinese data for continual pre-training on the original Llama-3, and are fine-tuned with selected instruction data to further enhance Chinese basic semantic and instruction understanding capabilities, significantly improving performance compared to the second-generation models.

#### Main Content

- 🚀 Open-source Llama-3-Chinese base model and Llama-3-Chinese-Instruct instruction model (v1, v2, v3)
- 🚀 Released pre-training scripts and instruction fine-tuning scripts, allowing users to further train or fine-tune the model as needed
- 🚀 Released alpaca_zh_51k, stem_zh_instruction, ruozhiba_gpt4 (4o/4T) instruction data
- 🚀 Provides a tutorial for quickly quantizing and deploying large models locally using a personal computer's CPU/GPU
- 🚀 Supports [🤗transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp), [text-generation-webui](https://github.com/oobabooga/text-generation-webui), [vLLM](https://github.com/vllm-project/vllm), [Ollama](https://ollama.com) and other Llama-3 ecosystems

----

[Chinese Mixtral](https://github.com/ymcui/Chinese-Mixtral) | [Chinese LLaMA-2 & Alpaca-2 Large Models](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) | [Chinese LLaMA & Alpaca Large Models](https://github.com/ymcui/Chinese-LLaMA-Alpaca) | [Multimodal Chinese LLaMA & Alpaca Large Models](https://github.com/airaria/Visual-Chinese-LLaMA-Alpaca) | [Multimodal VLE](https://github.com/iflytek/VLE) | [Chinese MiniRBT](https://github.com/iflytek/MiniRBT) | [Chinese LERT](https://github.com/ymcui/LERT) | [Chinese-English PERT](https://github.com/ymcui/PERT) | [Chinese MacBERT](https://github.com/ymcui/MacBERT) | [Chinese ELECTRA](https://github.com/ymcui/Chinese-ELECTRA) | [Chinese XLNet](https://github.com/ymcui/Chinese-XLNet) | [Chinese BERT](https://github.com/ymcui/Chinese-BERT-wwm) | [Knowledge Distillation Tool TextBrewer](https://github.com/airaria/TextBrewer) | [Model Pruning Tool TextPruner](https://github.com/airaria/TextPruner) | [Distillation and Pruning Integrated GRAIN](https://github.com/airaria/GRAIN)


## News

**[2024/05/30] Release Llama-3-Chinese-8B-Instruct-v3, which has better performance on downstream tasks than v1/v2. For details, see: [📚Version 3.0 Release Log](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/releases/tag/v3.0)**

[2024/05/08] Release Llama-3-Chinese-8B-Instruct-v2, which is directly tuned on  [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) with 5M instructions. For details, see: [📚Version 2.0 Release Log](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/releases/tag/v2.0)

[2024/05/07] Add pre-training and SFT scripts. For details, see: [📚Version 1.1 Release Log](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/releases/tag/v1.1)

[2024/04/30] Released the Llama-3-Chinese-8B base model and Llama-3-Chinese-8B-Instruct instruction model. For details, see: [📚Version 1.0 Release Log](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/releases/tag/v1.0)

[2024/04/19] 🚀 Officially launched the Chinese-LLaMA-Alpaca-3 project


## Content Guide
| Section                              | Description                                                   |
| ------------------------------------ | ------------------------------------------------------------- |
| [💁🏻‍♂️Model Introduction](#model-introduction) | Briefly introduces the technical features of the models related to this project |
| [⏬Model Download](#model-download)  | Download addresses for the Chinese Llama-3 large models      |
| [💻Inference and Deployment](#inference-and-deployment) | Describes how to quantize the model and deploy it using a personal computer to experience the large model |
| [💯Model Performance](#model-performance) | Introduces the effects of the model on some tasks             |
| [📝Training and Fine-Tuning](#training-and-fine-tuning) | Introduces how to train and fine-tune the Chinese Llama-3 large models |
| [❓Frequently Asked Questions](#frequently-asked-questions) | Replies to some common questions