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
@@ -54,10 +54,10 @@ These files were quantised using hardware kindly provided by [Massed Compute](ht
 <!-- repositories-available end -->
 
 <!-- prompt-template start -->
-## Prompt template: None
+## Prompt template: Yi
 
 ```
-{prompt}
+Human: {prompt} Assistant:
 
 ```
 
@@ -228,7 +228,7 @@ from huggingface_hub import InferenceClient
 endpoint_url = "https://your-endpoint-url-here"
 
 prompt = "Tell me about AI"
-prompt_template=f'''{prompt}
+prompt_template=f'''Human: {prompt} Assistant:
 '''
 
 client = InferenceClient(endpoint_url)
@@ -281,7 +281,7 @@ model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
 
 prompt = "Tell me about AI"
-prompt_template=f'''{prompt}
+prompt_template=f'''Human: {prompt} Assistant:
 '''
 
 print("\n\n*** Generate:")
@@ -365,13 +365,19 @@ 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
@@ -388,8 +394,9 @@ 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.