01-ai
/

Yi-6B

@@ -5,63 +5,83 @@ license_link: LICENSE
 ---
 <div align="center">
-<h1>
-  Yi
-</h1>
 </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 sizes of 6B and 34B.
 ## News
-- 🎯 **2023/11/02**: The base model of `Yi-6B` and `Yi-34B`
-## Dependency Installation
-```shell
-pip install transformers==4.34.0 sentencepiece==0.1.99 accelerate==0.24.1
-```
-## Generation Demonstration
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-6B", device_map="auto", torch_dtype="auto", trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-6B", trust_remote_code=True)
-inputs = tokenizer('Please count number for me: 1, 2, 3', return_tensors="pt")
-outputs = model.generate(inputs.input_ids.cuda(), max_new_tokens=256)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
 ## Model Performance
-| 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**   |
-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. OpenCompass). 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 were not reported by the original authors (including scores reported with different settings), we try to get results with our pipeline.
-To evaluate the model's capability extensively, 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 with 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
-Although we use data compliance checking algorithms during the training process to ensure the compliance of the trained model to the best of our ability, due to the complexity of the data and the diversity of language model usage scenarios, we cannot guarantee that the model will generate correct and reasonable output in all scenarios. Please be aware that there is still a risk of the model producing problematic outputs. We will not be responsible for any risks and issues resulting from misuse, misguidance, illegal usage, and related misinformation, as well as any associated data security concerns.
 ## License
-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])).

 ---
 <div align="center">
+<img src="./Yi.svg" width="200px">
 </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
+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.
 ## News
+- 🎯 **2023/11/02**: The base model of `Yi-6B` and `Yi-34B`.
 ## Model Performance
+| Model         |   MMLU   |  CMMLU   |  C-Eval  |  GAOKAO  |   BBH    | Common-sense 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          |           -           |    30.4     |
+| 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     |
+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.
+OpenCompass). 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 were not reported by the original authors (including scores reported
+with different settings), we try to get results with our pipeline.
+To evaluate the model's capability extensively, 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 with 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
+Please visit our [github repository](https://github.com/01-ai/) for general
+guidance on how to use this model.
 ## Disclaimer
+Although we use data compliance checking algorithms during the training process
+to ensure the compliance of the trained model to the best of our ability, due to
+the complexity of the data and the diversity of language model usage scenarios,
+we cannot guarantee that the model will generate correct and reasonable output
+in all scenarios. Please be aware that there is still a risk of the model
+producing problematic outputs. We will not be responsible for any risks and
+issues resulting from misuse, misguidance, illegal usage, and related
+misinformation, as well as any associated data security concerns.
 ## License
+The **Yi** series models are fully open for academic research and free
+commercial usage. All usage must be adhered to the [Model License
+Agreement](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE). To apply for
+the official commercial license, please contact us
+([[email protected]](mailto:[email protected])).

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