update LICENSE and README

LICENSE

@@ -190,7 +190,7 @@ latest version of the Agreement will be posted by the Licensor through
 https://01.ai.
  
 For any questions related to licensing and copyright, please contact the
-Licensor at oss@01.ai.
+Licensor at yi@01.ai.
 
 
                       Yi系列模型许可协议
@@ -324,4 +324,4 @@ Yi系列模型按“原样”提供。许可方不对Yi系列模型提供任何
 7. 协议更新及联系方式
 
 许可方有权对协议进行不时更新。许可方将通过https://01.ai公布协议最新版本。有关许
-可和版权的任何问题，请通过oss@01.ai 与许可方联系。
+可和版权的任何问题，请通过yi@01.ai 与许可方联系。

README.md

@@ -9,118 +9,36 @@ 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
 
-- 🎯 **2023/11/05**: The base model of `Yi-6B` and `Yi-34B` (with **200K** context window)
+- 🎯 **2023/11/02**: The base model of `Yi-6B` and `Yi-34B` 
 
 ## Model Performance
 
-(FIXME)
-
-## Usage
-
-### 1. Download the model (optional)
-
-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:
-
-- 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)
-
-### 2. Run with docker
-
-The recommended approach to try out our models is through docker. We provide the following docker images.
-
-- `ghcr.io/01-ai/yi:latest`
-- `ml-a100-cn-beijing.cr.volces.com/ci/01-ai/yi:latest`
-
-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:
-
-```bash
-docker run -it ghcr.io/01-ai/yi:latest python demo/text_generation.py
-```
-
-To reuse the downloaded models in the previous step, you can mount them into the container:
-
-```bash
-docker run -it                   \
-  -v /path/to/model:/model       \
-  ghcr.io/01-ai/yi:latest        \
-  python demo/text_generation.py \
-  --model /model
-```
-
-For more advanced usage, please refer the [doc](./demo/README.md).
-
-#### 2.2 Finetuning from the base model:
-
-```bash
-docker run -it                                      \
-  -v /path/to/base/model:/base_model                \
-  -v /path/to/save/finetuned/model:/finetuned_model \
-  ghcr.io/01-ai/yi:latest                           \
-  bash finetune/scripts/run_sft_Yi_6b.sh
-```
-
-Once finished, you can compare the finetuned model and the base model with the following command:
-
-```bash
-docker run -it                                       \
-  -v /path/to/save/finetuned/model/:/finetuned_model \
-  -v /path/to/base/model/:/base_model                \
-  ghcr.io/01-ai/yi:latest                            \
-  bash finetune/scripts/run_eval.sh
-```
-
-For more advanced usage like fine-tuning based on your custom data, please refer the [doc](./finetune/README.md).
-
-#### 2.3 Quantization
-
-```bash
-docker run -it                                         \
-  -v /path/to/base/model:/base_model                   \
-  -v /path/to/save/quantization/model:/quantized_model \
-  ghcr.io/01-ai/yi:latest                              \
-  python quantization/gptq/quant_autogptq.py           \
-  --model /base_model                                  \
-  --output_dir /quantized_model                        \
-  --trust_remote_code        
-```
+| 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**   |
 
-Once finished, you can then evaluate the resulted model as follows:
 
-```bash
-docker run -it                                         \
-  -v /path/to/save/quantization/model:/quantized_model \
-  ghcr.io/01-ai/yi:latest                              \
-  python quantization/gptq/eval_quantized_model.py     \
-  --model /quantized_model                             \
-  --trust_remote_code
-```
+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.
 
-For more detailed explanation, please read the [doc](./quantization/gptq/README.md)
+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.
 
 ## Disclaimer
 
@@ -128,4 +46,5 @@ 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 Yi series model must be adhere to the [Model License Agreement](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE).
+For any questions related to licensing and copyright, please contact us ([[email protected]](mailto:[email protected])).