update README

README.md

@@ -9,24 +9,11 @@ license_link: LICENSE
   Yi
 </h1>
 
-<a href="https://github.com/01-ai/Yi/actions/workflows/ci.yml">
-  <img src="https://github.com/01-ai/Yi/actions/workflows/ci.yml/badge.svg">
-</a>
-<a href="https://huggingface.co/01-ai">
-  <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-01--ai-blue">
-</a>
-<a href="https://www.modelscope.cn/organization/01ai/">
-  <img src="https://img.shields.io/badge/ModelScope-01--ai-blue">
-</a>
-<a href="https://github.com/01-ai/Yi/blob/main/LICENSE">
-  <img src="https://img.shields.io/github/license/01-ai/yi">
-</a>
-
 </div>
 
 ## Introduction
 
-The **Yi** series models are large language models trained from scratch by developers at [01.AI](https://01.ai/). The first public release contains two base models with the parameter size of 6B and 34B. Besides, a specialized version with **200K** context window size is also provided.
+The **Yi** series models are large language models trained from scratch by developers at [01.AI](https://01.ai/). The first public release contains two base models with the parameter size of 6B and 34B.
 
 ## News
 
@@ -34,20 +21,30 @@ The **Yi** series models are large language models trained from scratch by devel
 
 ## Model Performance
 
-(FIXME)
+| Model         |   MMLU   |  CMMLU   |  C-Eval  |  GAOKAO  |   BBH    | Commonsense Reasoning | Reading Comprehension | Math & Code |
+| :------------ | :------: | :------: | :------: | :------: | :------: | :-------------------: | :-------------------: | :---------: |
+|               |  5-shot  |  5-shot  |  5-shot  |  0-shot  | 3-shot@1 |           -           |           -           |      -      |
+| LLaMA2-34B    |   62.6   |    -     |    -     |    -     |   44.1   |         69.9          |         68.0          |    26.0     |
+| LLaMA2-70B    |   68.9   |   53.3   |    -     |   49.8   |   51.2   |         71.9          |         69.4          |    36.8     |
+| Baichuan2-13B |   59.2   |   62.0   |   58.1   |   54.3   |   48.8   |         64.3          |         62.4          |    23.0     |
+| Qwen-14B      |   66.3   |   71.0   |   72.1   |   62.5   |   53.4   |         73.3          |         72.5          |    39.8     |
+| Skywork-13B   |   62.1   |   61.8   |   60.6   |   68.1   |   41.7   |         72.4          |         61.4          |    24.9     |
+| InternLM-20B  |   62.1   |   59.0   |   58.8   |   45.5   |   52.5   |         78.3          |           -           |    26.0     |
+| Aquila-34B    |   67.8   |   71.4   |   63.1   |    -     |    -     |           -           |           -           |      -      |
+| Falcon-180B   |   70.4   |   58.0   |   57.8   |   59.0   |   54.0   |         77.3          |         68.8          |    34.0     |
+| Yi-6B         |   63.2   |   75.5   |   72.0   |   72.2   |   42.8   |         72.3          |         68.7          |    19.8     |
+| **Yi-34B**    | **76.3** | **83.7** | **81.4** | **82.8** | **54.3** |       **80.1**        |       **76.4**        |  **37.1**   |
 
-## Usage
 
-### 1. Download the model (optional)
+While benchmarking open-source models, we have observed a disparity between the results generated by our pipeline and those reported in public sources (e.g. OpenCampus). Upon conducting a more in-depth investigation of this difference, we have discovered that various models may employ different prompts, post-processing strategies, and sampling techniques, potentially resulting in significant variations in the outcomes. Our prompt and post-processing strategy remains consistent with the original benchmark, and greedy decoding is employed during evaluation without any post-processing for the generated content. For scores that did not report by original author (including score reported with different setting), we try to get results with our pipeline.
 
-By default the model weights and tokenizer will be downloaded from [HuggingFace](https://huggingface.co/01-ai) automatically in the next step. You can also download them manually from the following places:
+To extensively evaluate model's capability, we adopted the methodology outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande, ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ were incorporated to evaluate reading comprehension. CSQA was exclusively tested using a 7-shot setup, while all other tests were conducted in a 0-shot configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1), HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code". Due to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score is derived by averaging the scores on the remaining tasks. Since the scores for these two tasks are generally lower than the average, we believe that Falcon-180B's performance was not underestimated.
+
+## Usage
 
-- ModelScope (FIXME)
-- Mirror site (remember to extra the content with `tar`)
-  - [Yi-6B.tar](https://01-ai.tos-cn-beijing.volces.com/yi/models/Yi-6B.tar)
-  - [Yi-34B.tar](https://01-ai.tos-cn-beijing.volces.com/yi/models/Yi-34B.tar)
+Feel free to [create an issue](https://github.com/01-ai/Yi/issues/new) if you encounter any problem when using the Yi series models.
 
-### 2. Run with docker
+### 1. Run with docker
 
 The recommended approach to try out our models is through docker. We provide the following docker images.
 
@@ -56,7 +53,7 @@ The recommended approach to try out our models is through docker. We provide the
 
 Note that the `latest` tag always point to the latest code in the `main` branch. To test a stable version, please replace it with a specific [tag](https://github.com/01-ai/Yi/tags).
 
-#### 2.1 Try out the base model:
+#### 1.1 Try out the base model:
 
 ```bash
 docker run -it ghcr.io/01-ai/yi:latest python demo/text_generation.py
@@ -74,7 +71,7 @@ docker run -it                   \
 
 For more advanced usage, please refer the [doc](./demo/README.md).
 
-#### 2.2 Finetuning from the base model:
+#### 1.2 Finetuning from the base model:
 
 ```bash
 docker run -it                                      \
@@ -96,7 +93,7 @@ docker run -it                                       \
 
 For more advanced usage like fine-tuning based on your custom data, please refer the [doc](./finetune/README.md).
 
-#### 2.3 Quantization
+#### 1.3 Quantization
 
 ```bash
 docker run -it                                         \
@@ -128,4 +125,6 @@ Although we use data compliance checking algorithms during the training process
 
 ## License
 
-The source code in this repo is licensed under the [Apache 2.0 license](./LICENSE). The Yi series model must be adhere to the [Model License Agreement](./MODEL_LICENSE_AGREEMENT.txt).
+The source code in this repo is licensed under the [Apache 2.0 license](https://github.com/01-ai/Yi/blob/main/LICENSE).
+The Yi series model must be adhere to the [Model License Agreement](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
+For any questions related to licensing and copyright, please contact us ([[email protected]](mailto:[email protected])).