Upload README.md

README.md

@@ -7,7 +7,7 @@ license_name: yi-license
 model_creator: 01-ai
 model_name: Yi 34B
 model_type: yi
-prompt_template: '{prompt}
+prompt_template: 'Human: {prompt} Assistant:
 
   '
 quantized_by: TheBloke
@@ -70,13 +70,13 @@ Here is an incomplete list of clients and libraries that are known to support GG
 <!-- repositories-available end -->
 
 <!-- prompt-template start -->
-## Prompt template: None
+## Prompt template: Yi
 
 ```
 Human: {prompt} Assistant:
 
 ```
-Prompt template mentioned in the Yi github repo.
+
 <!-- prompt-template end -->
 
 
@@ -192,7 +192,7 @@ Windows Command Line users: You can set the environment variable by running `set
 Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
 
 ```shell
-./main -ngl 32 -m yi-34b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
+./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:"
 ```
 
 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.
 
 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.
+bilingual(English/Chinese) base models with the parameter sizes of 6B([`Yi-6B`](https://huggingface.co/01-ai/Yi-6B))
+and 34B([`Yi-34B`](https://huggingface.co/01-ai/Yi-34B)). Both of them are trained
+with 4K sequence length and can be extended to 32K during inference time.
+The [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K)
+and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) are base model with
+200K context length.
 
 ## News
 
-- 🎯 **2023/11/02**: The base model of `Yi-6B` and `Yi-34B`.
+- 🎯 **2023/11/06**: The base model of [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K)
+and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) with 200K context length.
+- 🎯 **2023/11/02**: The base model of [`Yi-6B`](https://huggingface.co/01-ai/Yi-6B) and
+[`Yi-34B`](https://huggingface.co/01-ai/Yi-34B).
 
 
 ## Model Performance
@@ -318,8 +324,9 @@ 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.