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[doc][feat] modified readme (#441)
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## News

<details open>
<summary>🎯 <b>2024/03/06</b>: The Yi-9B is open-sourced and available to the public.</summary>
<summary>🎯 <b>2024-03-06</b>: The <code>Yi-9B</code> is open-sourced and available to the public.</summary>
<br>
Yi-9B stands out as the top performer among a range of similar-sized open-source models (including Mistral-7B, SOLAR-10.7B, Gemma-7B, DeepSeek-Coder-7B-Base-v1.5 and more), particularly excelling in code, math, common-sense reasoning, and reading comprehension.
<code>Yi-9B</code> stands out as the top performer among a range of similar-sized open-source models (including Mistral-7B, SOLAR-10.7B, Gemma-7B, DeepSeek-Coder-7B-Base-v1.5 and more), particularly excelling in code, math, common-sense reasoning, and reading comprehension.
</details>

<details open>
<summary>🎯 <b>2024/01/23</b>: The Yi-VL models, <code><a href="https://huggingface.co/01-ai/Yi-VL-34B">Yi-VL-34B</a></code> and <code><a href="https://huggingface.co/01-ai/Yi-VL-6B">Yi-VL-6B</a></code>, are open-sourced and available to the public.</summary>
<summary>🎯 <b>2024-01-23</b>: The Yi-VL models, <code><a href="https://huggingface.co/01-ai/Yi-VL-34B">Yi-VL-34B</a></code> and <code><a href="https://huggingface.co/01-ai/Yi-VL-6B">Yi-VL-6B</a></code>, are open-sourced and available to the public.</summary>
<br>
<code><a href="https://huggingface.co/01-ai/Yi-VL-34B">Yi-VL-34B</a></code> has ranked <strong>first</strong> among all existing open-source models in the latest benchmarks, including <a href="https://arxiv.org/abs/2311.16502">MMMU</a> and <a href="https://arxiv.org/abs/2401.11944">CMMMU</a> (based on data available up to January 2024).</li>
</details>


<details>
<summary>🎯 <b>2023/11/23</b>: <a href="#chat-models">Chat models</a> are open-sourced and available to the public.</summary>
<summary>🎯 <b>2023-11-23</b>: <a href="#chat-models">Chat models</a> are open-sourced and available to the public.</summary>
<br>This release contains two chat models based on previously released base models, two 8-bit models quantized by GPTQ, and two 4-bit models quantized by AWQ.

- `Yi-34B-Chat`
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</details>

<details>
<summary>🔔 <b>2023/11/23</b>: The Yi Series Models Community License Agreement is updated to <a href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt">v2.1</a>.</summary>
<summary>🔔 <b>2023-11-23</b>: The Yi Series Models Community License Agreement is updated to <a href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt">v2.1</a>.</summary>
</details>

<details>
<summary>🔥 <b>2023/11/08</b>: Invited test of Yi-34B chat model.</summary>
<summary>🔥 <b>2023-11-08</b>: Invited test of Yi-34B chat model.</summary>
<br>Application form:

- [English](https://cn.mikecrm.com/l91ODJf)
- [Chinese](https://cn.mikecrm.com/gnEZjiQ)
</details>

<details>
<summary>🎯 <b>2023/11/05</b>: <a href="#base-models">The base models, </a><code>Yi-6B-200K</code> and <code>Yi-34B-200K</code>, are open-sourced and available to the public.</summary>
<summary>🎯 <b>2023-11-05</b>: <a href="#base-models">The base models, </a><code>Yi-6B-200K</code> and <code>Yi-34B-200K</code>, are open-sourced and available to the public.</summary>
<br>This release contains two base models with the same parameter sizes as the previous
release, except that the context window is extended to 200K.
</details>

<details>
<summary>🎯 <b>2023/11/02</b>: <a href="#base-models">The base models, </a><code>Yi-6B</code> and <code>Yi-34B</code>, are open-sourced and available to the public.</summary>
<summary>🎯 <b>2023-11-02</b>: <a href="#base-models">The base models, </a><code>Yi-6B</code> and <code>Yi-34B</code>, are open-sourced and available to the public.</summary>
<br>The first public release contains two bilingual (English/Chinese) base models
with the parameter sizes of 6B and 34B. Both of them are trained with 4K
sequence length and can be extended to 32K during inference time.
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| Model | Minimum VRAM | Recommended GPU Example |
|----------------------|--------------|:-------------------------------------:|
| Yi-6B-Chat | 15 GB | RTX 3090 <br> RTX 4090 <br> A10 <br> A30 |
| Yi-6B-Chat-4bits | 4 GB | RTX 3060 <br> RTX 4060 |
| Yi-6B-Chat-8bits | 8 GB | RTX 3070 <br> RTX 4060 |
| Yi-6B-Chat | 15 GB | 1 x RTX 3090 <br> 1 x RTX 4090 <br> A10 <br> A30 |
| Yi-6B-Chat-4bits | 4 GB | 1 x RTX 3060 <br> 1 x RTX 4060 |
| Yi-6B-Chat-8bits | 8 GB | 1 x RTX 3070 <br> 1 x RTX 4060 |
| Yi-34B-Chat | 72 GB | 4 x RTX 4090 <br> A800 (80GB) |
| Yi-34B-Chat-4bits | 20 GB | RTX 3090 <br> RTX 4090 <br> A10 <br> A30 <br> A100 (40GB) |
| Yi-34B-Chat-4bits | 20 GB | 1 x RTX 3090 <br> 1 x RTX 4090 <br> A10 <br> A30 <br> A100 (40GB) |
| Yi-34B-Chat-8bits | 38 GB | 2 x RTX 3090 <br> 2 x RTX 4090 <br> A800 (40GB) |

Below are detailed minimum VRAM requirements under different batch use cases.
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| Model | Minimum VRAM | Recommended GPU Example |
|----------------------|--------------|:-------------------------------------:|
| Yi-6B | 15 GB | RTX3090 <br> RTX4090 <br> A10 <br> A30 |
| Yi-6B | 15 GB | 1 x RTX 3090 <br> 1 x RTX 4090 <br> A10 <br> A30 |
| Yi-6B-200K | 50 GB | A800 (80 GB) |
| Yi-9B | 20 GB | 1 x RTX 4090 (24 GB) |
| Yi-34B | 72 GB | 4 x RTX 4090 <br> A800 (80 GB) |
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- [Benchmarks](#benchmarks)
- [Chat model performance](#chat-model-performance)
- [Base model performance](#base-model-performance)
- [Yi-34B and Yi-34B-200K](#yi-34b-and-yi-34b-200k)
- [Yi-9B](#yi-9b)

## Ecosystem

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- [Upstream](#upstream)
- [Downstream](#downstream)
- [Serving](#serving)
- [Quantitation](#️quantitation)
- [Fine-tuning](#️fine-tuning)
- [Quantization](#quantization-1)
- [Fine-tuning](#fine-tuning-1)
- [API](#api)

### Upstream
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![Yi-9B benchmark - details](https://github.com/01-ai/Yi/blob/main/assets/img/Yi-9B_benchmark_details.png?raw=true)

- In terms of **overall** ability (`Mean-All), Yi-9B performs the best among similarly sized open-source models, surpassing DeepSeek-Coder, DeepSeek-Math, Mistral-7B, SOLAR-10.7B, and Gemma-7B.
- In terms of **overall** ability (Mean-All), Yi-9B performs the best among similarly sized open-source models, surpassing DeepSeek-Coder, DeepSeek-Math, Mistral-7B, SOLAR-10.7B, and Gemma-7B.

![Yi-9B benchmark - overall](https://github.com/01-ai/Yi/blob/main/assets/img/Yi-9B_benchmark_overall.png?raw=true)

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