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@@ -7,7 +7,7 @@ license_name: yi-license
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  model_creator: 01-ai
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  model_name: Yi 34B
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  model_type: yi
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- prompt_template: '{prompt}
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  '
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  quantized_by: TheBloke
@@ -70,13 +70,13 @@ Here is an incomplete list of clients and libraries that are known to support GG
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  <!-- repositories-available end -->
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  <!-- prompt-template start -->
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- ## Prompt template: None
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  ```
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  Human: {prompt} Assistant:
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  ```
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- Prompt template mentioned in the Yi github repo.
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  <!-- prompt-template end -->
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@@ -192,7 +192,7 @@ Windows Command Line users: You can set the environment variable by running `set
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  Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
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  ```shell
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- ./main -ngl 32 -m yi-34b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
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  ```
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  Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
@@ -295,13 +295,19 @@ And thank you again to a16z for their generous grant.
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  The **Yi** series models are large language models trained from scratch by
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  developers at [01.AI](https://01.ai/). The first public release contains two
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- bilingual(English/Chinese) base models with the parameter sizes of 6B and 34B.
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- Both of them are trained with 4K sequence length and can be extended to 32K
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- during inference time.
 
 
 
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  ## News
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- - 🎯 **2023/11/02**: The base model of `Yi-6B` and `Yi-34B`.
 
 
 
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  ## Model Performance
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  | Aquila-34B | 67.8 | 71.4 | 63.1 | - | - | - | - | - |
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  | Falcon-180B | 70.4 | 58.0 | 57.8 | 59.0 | 54.0 | 77.3 | 68.8 | 34.0 |
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  | Yi-6B | 63.2 | 75.5 | 72.0 | 72.2 | 42.8 | 72.3 | 68.7 | 19.8 |
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- | **Yi-34B** | **76.3** | **83.7** | **81.4** | **82.8** | **54.3** | **80.1** | **76.4** | 37.1 |
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-
 
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  While benchmarking open-source models, we have observed a disparity between the
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  results generated by our pipeline and those reported in public sources (e.g.
 
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  model_creator: 01-ai
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  model_name: Yi 34B
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  model_type: yi
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+ prompt_template: 'Human: {prompt} Assistant:
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  '
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  quantized_by: TheBloke
 
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  <!-- repositories-available end -->
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  <!-- prompt-template start -->
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+ ## Prompt template: Yi
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  ```
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  Human: {prompt} Assistant:
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  ```
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+
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  <!-- prompt-template end -->
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  Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
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  ```shell
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+ ./main -ngl 32 -m yi-34b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Human: {prompt} Assistant:"
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  ```
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  Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
 
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  The **Yi** series models are large language models trained from scratch by
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  developers at [01.AI](https://01.ai/). The first public release contains two
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+ bilingual(English/Chinese) base models with the parameter sizes of 6B([`Yi-6B`](https://huggingface.co/01-ai/Yi-6B))
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+ and 34B([`Yi-34B`](https://huggingface.co/01-ai/Yi-34B)). Both of them are trained
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+ with 4K sequence length and can be extended to 32K during inference time.
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+ The [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K)
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+ and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) are base model with
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+ 200K context length.
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  ## News
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+ - 🎯 **2023/11/06**: The base model of [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K)
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+ and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) with 200K context length.
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+ - 🎯 **2023/11/02**: The base model of [`Yi-6B`](https://huggingface.co/01-ai/Yi-6B) and
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+ [`Yi-34B`](https://huggingface.co/01-ai/Yi-34B).
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  ## Model Performance
 
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  | Aquila-34B | 67.8 | 71.4 | 63.1 | - | - | - | - | - |
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  | Falcon-180B | 70.4 | 58.0 | 57.8 | 59.0 | 54.0 | 77.3 | 68.8 | 34.0 |
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  | Yi-6B | 63.2 | 75.5 | 72.0 | 72.2 | 42.8 | 72.3 | 68.7 | 19.8 |
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+ | Yi-6B-200K | 64.0 | 75.3 | 73.5 | 73.9 | 42.0 | 72.0 | 69.1 | 19.0 |
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+ | **Yi-34B** | **76.3** | **83.7** | 81.4 | 82.8 | **54.3** | **80.1** | 76.4 | 37.1 |
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+ | Yi-34B-200K | 76.1 | 83.6 | **81.9** | **83.4** | 52.7 | 79.7 | **76.6** | 36.3 |
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  While benchmarking open-source models, we have observed a disparity between the
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  results generated by our pipeline and those reported in public sources (e.g.