add Yi-6B-200K and Yi-34B-200K

README.md

@@ -32,10 +32,12 @@ during inference time.
 
 ## News
 
+- 🎯 **2023/11/05**: The base model of `Yi-6B-200K` and `Yi-34B-200K` with 200K context length.
 - 🎯 **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 |           -            |           -           |      -      |
@@ -48,8 +50,9 @@ during inference time.
 | 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     |
-
+| Yi-6B-200K    |   64.0   |   75.3   |   73.5   |   73.9   |   42.0   |          72.0          |         69.1          |    19.0     |
+| **Yi-34B**    | **76.3** | **83.7** |   81.4   |   82.8   | **54.3** |        **80.1**        |         76.4          |    37.1     |
+| Yi-34B-200K   |   76.1   |   83.6   | **81.9** | **83.4** |   52.7   |          79.7          |       **76.6**        |    36.3     |
 
 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.
@@ -192,7 +195,14 @@ For more detailed explanation, please read the [doc](https://github.com/01-ai/Yi
 
 ## Disclaimer
 
-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 complex 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.
+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
+complex 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