|
--- |
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license: other |
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widget: |
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- example_title: SUS-Chat |
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text: hi |
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output: |
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text: ' Hello! How can I assist you today?' |
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pipeline_tag: text-generation |
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tags: |
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- yi |
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- long context |
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- commercial use |
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- gptq |
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- function-calling |
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- function calling |
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extra_gated_prompt: "Purchase access to this repo [HERE](https://buy.stripe.com/6oE9Bmg8t1Dt1ck9BL)!" |
|
--- |
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# Function Calling Fine-tuned Yi Chat 200k Context |
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|
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Purchase access to this model [here](https://buy.stripe.com/6oE9Bmg8t1Dt1ck9BL). |
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|
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This model is fine-tuned for function calling. |
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- The function metadata format is the same as used for OpenAI. |
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- The model is suitable for commercial use. |
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- See the 'gptq' branch for the GPTQ model. |
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- AWQ and GGUF are available on request after purchase. |
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|
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Check out other fine-tuned function calling models [here](https://trelis.com/function-calling/). |
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|
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## Quick Server Setup |
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Runpod one click template, TGI API with EETQ (8bit) [here](https://runpod.io/gsc?template=p5zxy64o61&ref=jmfkcdio). You must add a HuggingFace Hub access token (HUGGING_FACE_HUB_TOKEN) to the environment variables as this is a gated model. |
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Runpod one click template, vLLM API with AWQ (4bit) [here](https://runpod.io/gsc?template=no46bznoof&ref=jmfkcdio). You must add a HuggingFace Hub access token (HUGGING_FACE_HUB_TOKEN) to the environment variables as this is a gated model. |
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|
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Runpod Affiliate [Link](https://runpod.io?ref=jmfkcdio) (helps support the Trelis channel). |
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|
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## Inference Scripts |
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See below for sample prompt format. |
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|
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Complete inference scripts are available for purchase [here](https://trelis.com/enterprise-server-api-and-inference-guide/): |
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- Easily format prompts using tokenizer.apply_chat_format (starting from openai formatted functions and a list of messages) |
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- Automate catching, handling and chaining of function calls. |
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|
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## Prompt Format |
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``` |
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B_FUNC, E_FUNC = "You have access to the following functions. Use them if required:\n\n", "\n\n" |
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B_INST, E_INST = "### Human: ", "\n\n### Assistant: " #SUSChat |
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prompt = f"{B_INST}{B_FUNC}{functionList.strip()}{E_FUNC}{user_prompt.strip()}{E_INST}\n\n" |
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``` |
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|
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### Using tokenizer.apply_chat_template |
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For an easier application of the prompt, you can set up as follows: |
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|
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Set up `messages`: |
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``` |
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[ |
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{ |
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"role": "function_metadata", |
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"content": "FUNCTION_METADATA" |
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}, |
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{ |
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"role": "user", |
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"content": "What is the current weather in London?" |
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}, |
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{ |
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"role": "function_call", |
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"content": "{\n \"name\": \"get_current_weather\",\n \"arguments\": {\n \"city\": \"London\"\n }\n}" |
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}, |
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{ |
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"role": "function_response", |
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"content": "{\n \"temperature\": \"15 C\",\n \"condition\": \"Cloudy\"\n}" |
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}, |
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{ |
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"role": "assistant", |
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"content": "The current weather in London is Cloudy with a temperature of 15 Celsius" |
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} |
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] |
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``` |
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|
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with `FUNCTION_METADATA` as: |
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``` |
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[ |
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{ |
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"type": "function", |
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"function": { |
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"name": "get_current_weather", |
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"description": "This function gets the current weather in a given city", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"city": { |
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"type": "string", |
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"description": "The city, e.g., San Francisco" |
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}, |
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"format": { |
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"type": "string", |
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"enum": ["celsius", "fahrenheit"], |
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"description": "The temperature unit to use." |
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} |
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}, |
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"required": ["city"] |
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} |
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} |
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}, |
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{ |
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"type": "function", |
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"function": { |
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"name": "get_clothes", |
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"description": "This function provides a suggestion of clothes to wear based on the current weather", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"temperature": { |
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"type": "string", |
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"description": "The temperature, e.g., 15 C or 59 F" |
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}, |
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"condition": { |
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"type": "string", |
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"description": "The weather condition, e.g., 'Cloudy', 'Sunny', 'Rainy'" |
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} |
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}, |
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"required": ["temperature", "condition"] |
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} |
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} |
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} |
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] |
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``` |
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and then apply the chat template to get a formatted prompt: |
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``` |
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tokenizer = AutoTokenizer.from_pretrained('Trelis/SUS-Chat-34B-function-calling-v3', trust_remote_code=True) |
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|
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prompt = tokenizer.apply_chat_template(prompt, tokenize=False) |
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``` |
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If you are using a gated model, you need to first run: |
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``` |
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pip install huggingface_hub |
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huggingface-cli login |
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``` |
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|
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### Manual Prompt: |
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``` |
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Human: You have access to the following functions. Use them if required: |
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|
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[ |
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{ |
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"type": "function", |
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"function": { |
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"name": "get_stock_price", |
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"description": "Get the stock price of an array of stocks", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"names": { |
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"type": "array", |
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"items": { |
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"type": "string" |
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}, |
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"description": "An array of stocks" |
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} |
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}, |
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"required": [ |
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"names" |
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] |
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} |
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} |
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}, |
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{ |
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"type": "function", |
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"function": { |
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"name": "get_big_stocks", |
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"description": "Get the names of the largest N stocks by market cap", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"number": { |
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"type": "integer", |
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"description": "The number of largest stocks to get the names of, e.g. 25" |
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}, |
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"region": { |
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"type": "string", |
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"description": "The region to consider, can be \"US\" or \"World\"." |
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} |
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}, |
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"required": [ |
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"number" |
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] |
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} |
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} |
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} |
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] |
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|
|
Get the names of the five largest stocks by market cap Assistant: |
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|
|
{ |
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"name": "get_big_stocks", |
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"arguments": { |
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"number": 5 |
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} |
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}<|endoftext|> |
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``` |
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|
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# Dataset |
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See [Trelis/function_calling_v3](https://huggingface.co/datasets/Trelis/function_calling_v3). |
|
|
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# License |
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This model may be used commercially for inference according to the terms of the Yi license, or for further fine-tuning and inference. Users may not re-publish or re-sell this model in the same or derivative form (including fine-tunes). |
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|
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** |
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The SFT chat fine-tuned model's repo card follows below. |
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** |
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# 🐷SUS-Chat: Instruction tuning done right |
|
|
|
<p align="left"> |
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<a href="README_CN.md">中文</a>  ｜  English  |
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</p> |
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|
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<br><br> |
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|
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<div align="center"> |
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|
|
<p align="center"> |
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<img src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/sustech.svg?sanitize=true" width="200px"> |
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<img src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/ccnl.png?sanitize=true" width="200px"> |
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</p> |
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|
|
<div style="display: inline-block;"> |
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|
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<a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/issues"> |
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<img src="https://img.shields.io/github/issues/SUSTech-IDEA/SUS-Chat?logo=github" style="margin: 0 0;"> |
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</a> |
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|
|
</div> |
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|
|
<div style="display: inline-block;"> |
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|
|
<a href="https://huggingface.co/SUSTech"> |
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<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-SUSTech-blue" style="margin: 0 0;"> |
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</a> |
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|
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</div> |
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|
|
<div style="display: inline-block;"> |
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|
|
<a rel="noopener nofollow" href="https://www.modelscope.cn/organization/sustc/"> |
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<img src="https://img.shields.io/badge/🤖ModelScope-sustc-blue" style="margin: 0 0;"> |
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</a> |
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|
|
</div> |
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|
|
<a href="https://wisemodel.cn/organization/SUSTech"> |
|
<img src="https://img.shields.io/badge/WiseModel-SUSTech-blue"> </a> |
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|
|
<div style="display: inline-block;"> |
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|
|
<a rel="noopener nofollow" href="https://github.com/SUSTech-IDEA/SUS-Chat/blob/main/LICENSE"> |
|
<img src="https://img.shields.io/badge/Code_License-Apache_2.0-lightblue" style="margin: 0 0;"> |
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</a> |
|
|
|
</div> |
|
|
|
<div style="display: inline-block;"> |
|
|
|
<a rel="noopener nofollow" href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt"> |
|
<img src="https://img.shields.io/badge/Model_License-Model_Agreement-lightblue" style="margin: 0 0;"> |
|
</a> |
|
|
|
</div> |
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|
|
<div style="display: inline-block;"> |
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|
|
<a rel="noopener nofollow" href="mailto:[email protected]"> |
|
<img src="https://img.shields.io/badge/✉️[email protected]" style="margin: 0 0;"> |
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</a> |
|
|
|
</div> |
|
|
|
</div> |
|
|
|
# News |
|
|
|
- 2023-12-09: 🔥 `Tigerbot` variant has been |
|
[deleted](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/438), |
|
`SUS-Chat-34B` is now the the top-ranked LLaMA model and the |
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top-ranked chat model. |
|
|
|
- 2023-12-07: SUS-Chat-34B is now available on |
|
[WiseModel🧠](https://wisemodel.cn/model/SUSTech/SUS-Chat-34B). |
|
|
|
- 2023-12-06: Try [SUS-Chat-34B |
|
chat-ui](https://huggingface.co/spaces/SUSTech/SUS-Chat-34B). |
|
|
|
- 2023-12-05: SUS-Chat-34B is now available on |
|
[ModelScope🤖](https://www.modelscope.cn/models/SUSTC/SUS-Chat-34B/summary) |
|
|
|
- 2023-12-05: SUS-Chat-34B is ranked 2nd in [Open LLM |
|
leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
|
and surpassed all models under 70B. |
|
|
|
- 2023-12-01: SUS-Chat-34B is now available on |
|
[HuggingFace🤗](https://huggingface.co/SUSTech/SUS-Chat-34B). |
|
|
|
# Introduction |
|
|
|
<img src="https://hackmd.io/_uploads/HJlDtzhBa.png" id="fig-sus" |
|
alt="Figure 1: DALL·E 2023-12-01 11.03.28 - An imposing, majestic wild boar combined with elements of a futuristic transformer robot. The boar itself should be intricately blended with these tra" /> |
|
|
|
**SUS-Chat-34B** is a 34B bilingual Chinese-English dialogue model, |
|
jointly released by the **[Southern University of Science and |
|
Technology](https://huggingface.co/SUSTech)** and |
|
**[IDEA-CCNL](https://huggingface.co/IDEA-CCNL)**. This model is based |
|
on [`01-ai/Yi-34B`](https://huggingface.co/01-ai/Yi-34B) and has been |
|
fine-tuned on millions of high-quality, multilingual instruction data. |
|
While maintaining the strong language capabilities of the base model, |
|
the SUS-Chat-34B model has improved the model’s response to human |
|
instructions through high-quality instruction fine-tuning and excels at |
|
imitating human thought processes through chains of thought. It |
|
introduces inter-instruction attention sharing in long texts, expanding |
|
the window size from 4K to 8K, significantly enhancing the usability of |
|
multi-turn dialogues. |
|
|
|
It has surpassed all models of the same size in almost all benchmark |
|
tests and is better suited to meet the practical needs of complex |
|
multilingual tasks. Compared to larger models, SUS-Chat-34B remains |
|
highly competitive and has achieved state-of-the-art performance in our |
|
comprehensive evaluations. |
|
|
|
SUS-Chat-34B model has the following highlights: |
|
|
|
1. Large-scale complex instruction following data: Trained with 1.4 |
|
billion tokens of high-quality complex instruction data, covering |
|
Chinese and English, multi-turn dialogues, mathematics, reasoning, |
|
and various other types of instruction data; |
|
2. Strong performance in general tasks: The SUS-Chat-34B model excels |
|
in numerous mainstream Chinese and English tasks, surpassing other |
|
open-source instruction fine-tuned models of the same parameter |
|
scale. It also competes well against models with larger parameter |
|
scales; |
|
3. Longer context window and excellent multi-turn dialogue |
|
capabilities: Currently, SUS-Chat-34B supports an 8K context window, |
|
and is trained with a large amount of multi-turn instruction and |
|
single-multi-turn mixed data, demonstrating remarkable capabilities |
|
in long-text dialogue information focus and instruction follow-up. |
|
|
|
SUS-Chat powerfully demonstrates that through the right instruction |
|
fine-tuning, academic institutions can achieve better performance |
|
without increasing model parameters, using open-source datasets and |
|
models. This bridges the gap between academia and industry in large |
|
language models and opens new possibilities for collaboration between |
|
academic and industrial sectors. |
|
|
|
# Performance |
|
|
|
To better evaluate the performance of the SUS-Chat-34B model, we |
|
conducted assessments across multiple benchmark tests and have |
|
open-sourced the evaluation framework |
|
[TLEM](https://huggingface.co/spaces/SUSTech/tlem) to facilitate |
|
replication and comparison by other researchers. |
|
|
|
In TLEM, we utilized various benchmark tests including MMLU, CMMLU, |
|
C-Eval, BBH, GSM-8K, and MATH, to measure the model’s knowledge and |
|
thinking capabilities. In these metrics, the SUS-Chat-34B model achieved |
|
state-of-the-art performance. Additionally, we incorporated |
|
[lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) to test |
|
SUS-Chat and similar models on winogrande, hellaswag, arc, and |
|
truthful-qa, assessing the model’s common-sense reasoning ability and |
|
susceptibility to illusions. |
|
|
|
Overall, the SUS-Chat-34B model significantly outperformed models of |
|
similar scale and achieved the most advanced comprehensive performance. |
|
|
|
<img |
|
src="https://github.com/SUSTech-IDEA/SUS-Chat/raw/main/assets/radar.png" |
|
id="fig-bench" alt="Figure 2: Benchmark" /> |
|
|
|
<div> |
|
|
|
<table> |
|
<colgroup> |
|
<col style="width: 50%" /> |
|
<col style="width: 50%" /> |
|
</colgroup> |
|
<tbody> |
|
<tr class="odd"> |
|
<td style="text-align: center;"><div width="50.0%" |
|
data-layout-align="center"> |
|
<h2 id="english-understanding">English Understanding</h2> |
|
<table> |
|
<thead> |
|
<tr class="header"> |
|
<th style="text-align: right;">Model</th> |
|
<th style="text-align: center;">mmlu (0-shot)</th> |
|
</tr> |
|
</thead> |
|
<tbody> |
|
<tr class="odd"> |
|
<td style="text-align: right;">GPT-4</td> |
|
<td style="text-align: center;">83</td> |
|
</tr> |
|
<tr class="even"> |
|
<td style="text-align: right;">SUS-Chat-34B</td> |
|
<td style="text-align: center;"><u>74.35</u></td> |
|
</tr> |
|
<tr class="odd"> |
|
<td style="text-align: right;">Qwen-72b-Chat</td> |
|
<td style="text-align: center;"><strong>74.52</strong></td> |
|
</tr> |
|
<tr class="even"> |
|
<td style="text-align: right;">Deepseek-68b-Chat</td> |
|
<td style="text-align: center;">69.43</td> |
|
</tr> |
|
<tr class="odd"> |
|
<td style="text-align: right;">OrionStar-Yi-34B-Chat</td> |
|
<td style="text-align: center;">68.51</td> |
|
</tr> |
|
<tr class="even"> |
|
<td style="text-align: right;">Yi-34B-Chat</td> |
|
<td style="text-align: center;">66.96</td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
</div></td> |
|
<td style="text-align: center;"><div width="50.0%" |
|
data-layout-align="center"> |
|
<h2 id="chinese-capabilities">Chinese Capabilities</h2> |
|
<table> |
|
<colgroup> |
|
<col style="width: 34%" /> |
|
<col style="width: 32%" /> |
|
<col style="width: 32%" /> |
|
</colgroup> |
|
<thead> |
|
<tr class="header"> |
|
<th style="text-align: right;">Model</th> |
|
<th style="text-align: center;">cmmlu (0-shot)</th> |
|
<th style="text-align: center;">C-Eval (0-shot)<a href="#fn1" |
|
class="footnote-ref" id="fnref1" |
|
role="doc-noteref"><sup>1</sup></a></th> |
|
</tr> |
|
</thead> |
|
<tbody> |
|
<tr class="odd"> |
|
<td style="text-align: right;">GPT-4</td> |
|
<td style="text-align: center;">71</td> |
|
<td style="text-align: center;">69.9</td> |
|
</tr> |
|
<tr class="even"> |
|
<td style="text-align: right;">SUS-Chat-34B</td> |
|
<td style="text-align: center;"><strong>78.68</strong></td> |
|
<td style="text-align: center;"><strong>82.42</strong></td> |
|
</tr> |
|
<tr class="odd"> |
|
<td style="text-align: right;">Qwen-72b-Chat</td> |
|
<td style="text-align: center;"><u>77.02</u></td> |
|
<td style="text-align: center;"><u>77.22</u></td> |
|
</tr> |
|
<tr class="even"> |
|
<td style="text-align: right;">Deepseek-68b-Chat</td> |
|
<td style="text-align: center;">48.51</td> |
|
<td style="text-align: center;">59.7</td> |
|
</tr> |
|
<tr class="odd"> |
|
<td style="text-align: right;">OrionStar-Yi-34B-Chat</td> |
|
<td style="text-align: center;">66.88</td> |
|
<td style="text-align: center;">65.13</td> |
|
</tr> |
|
<tr class="even"> |
|
<td style="text-align: right;">Yi-34B-Chat</td> |
|
<td style="text-align: center;">55.16</td> |
|
<td style="text-align: center;">77.16</td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
</div></td> |
|
</tr> |
|
</tbody> |
|
</table> |
|
<section id="footnotes" class="footnotes footnotes-end-of-document" |
|
role="doc-endnotes"> |
|
<hr /> |
|
<ol> |
|
<li id="fn1"><p>C-Eval results are evaluated on the validation |
|
datasets<a href="#fnref1" class="footnote-back" |
|
role="doc-backlink">↩︎</a></p></li> |
|
</ol> |
|
</section> |
|
|
|
</div> |
|
|
|
## Math & Reasoning |
|
|
|
| Model | gsm8k (0-shot) | MATH (0-shot) | BBH (0-shot) | |
|
|----------------------:|:--------------:|:-------------:|:------------:| |
|
| GPT-4 | 91.4 | 45.8 | 86.7 | |
|
| SUS-Chat-34B | **80.06** | 28.7 | 67.62 | |
|
| Qwen-72b-Chat | <u>76.57</u> | **35.9** | **72.63** | |
|
| Deepseek-68b-Chat | 74.45 | <u>29.56</u> | <u>69.73</u> | |
|
| OrionStar-Yi-34B-Chat | 54.36 | 12.8 | 62.88 | |
|
| Yi-34B-Chat | 63.76 | 10.02 | 61.54 | |
|
|
|
## More Tasks |
|
|
|
| Model | winogrande (5-shot) | arc (25-shot) | hellaswag (10-shot) | TruthfulQA mc1 (0-shot) | TruthfulQA mc2 (0-shot) | |
|
|----------------------:|:-------------------:|:-------------:|:-------------------:|:-----------------------:|:-----------------------:| |
|
| GPT-4 | — | 94.5 | 91.4 | 59.00 | — | |
|
| SUS-Chat-34B | **81.22** | <u>81.54</u> | 83.79 | **40.64** | **57.47** | |
|
| Qwen-72b-Chat | 76.09 | **82.10** | <u>86.06</u> | 39.17 | <u>56.37</u> | |
|
| Deepseek-68b-Chat | <u>80.58</u> | 81.29 | **87.02** | <u>40.02</u> | 50.64 | |
|
| OrionStar-Yi-34B-Chat | 77.27 | 80.19 | 84.54 | 36.47 | 53.24 | |
|
| Yi-34B-Chat | 76.64 | 70.66 | 82.29 | 38.19 | 54.57 | |
|
|
|
## Overall |
|
|
|
| Model | Average | |
|
|----------------------:|:---------:| |
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| SUS-Chat-34B | **69.05** | |
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| Qwen-72b-Chat | 68.41 | |
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| Deepseek-68b-Chat | 62.91 | |
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| OrionStar-Yi-34B-Chat | 60.21 | |
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| Yi-34B-Chat | 59.72 | |
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To reproduce the results, please start a corresponding vllm server and |
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refer to |
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[here](https://sustech-tlem.static.hf.space/index.html#start-evaluating-your-model-in-3-line). |
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# Usage |
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|
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SUS-Chat-34B is a standard LLaMA model and should be seamlessly |
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compatible with the LLaMA ecosystem. We provide the following example to |
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demonstrate how it can be used for multi-turn dialogues. |
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Feel free to [open an |
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issue](https://github.com/SUSTech-IDEA/SUS-Chat/issues) if you have any |
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questions. |
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|
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``` python |
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from transformers import AutoModelForCausalLM, AutoTokenizer # 🤗 Transformers, or |
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# from modelscope import AutoModelForCausalLM, AutoTokenizer # 🤖 ModelScope |
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def chat_template(messages): |
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history = "" |
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for message in messages: |
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match message: |
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case {"role": "user", "content": message}: |
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history += f"### Human: {message}\n\n### Assistant: " |
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case {"role": "assistant", "content": message}: |
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history += message |
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return history |
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model_path = "SUSTech/SUS-Chat-34B" |
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# model_path = "SUSTC/SUS-Chat-34B" # ModelScope |
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, device_map="auto", torch_dtype="auto" |
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).eval() |
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messages = [{"role": "user", "content": "hi"}] |
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input_ids = tokenizer.encode( |
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chat_template(messages), return_tensors="pt", add_special_tokens=False |
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).to("cuda") |
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output_ids = model.generate(input_ids.to("cuda"), max_length=256) |
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response = tokenizer.decode( |
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output_ids[0][input_ids.shape[1] :], skip_special_tokens=False |
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) |
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messages.append({"role": "assistant", "content": response}) |
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|
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# Second round |
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messages.append({"role": "user", "content": "What is the capital of China?"}) |
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input_ids = tokenizer.encode( |
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chat_template(messages), return_tensors="pt", add_special_tokens=False |
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).to("cuda") |
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output_ids = model.generate(input_ids.to("cuda"), max_length=256) |
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response = tokenizer.decode( |
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output_ids[0][input_ids.shape[1] :], skip_special_tokens=False |
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) |
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|
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messages.append({"role": "assistant", "content": response}) |
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``` |
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# Limitations |
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|
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SUS-Chat has only undergone supervised fine-tuning and has not yet been |
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trained on human preference learning. As a result, it may produce |
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unreasonable responses in some situations and exacerbate existing issues |
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in language models, including hallucinations, non-determinism, and |
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cumulative errors. To achieve better performance for downstream tasks, |
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we recommend adjusting the generation configuration parameters |
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accordingly. |
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|
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# Disclaimer |
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|
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During the training process, we used data compliance check algorithms to |
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ensure the compliance of the training model as much as possible. Due to |
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the complexity of the data and the diverse use cases of language models, |
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we cannot guarantee that the model will produce correct and reasonable |
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outputs in all scenarios. Please be aware that there is still a risk of |
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the model generating problematic outputs. We will not be responsible for |
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any risks or issues arising from misuse, misguidance, illegal use, and |
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related misinformation, as well as data security issues related to the |
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model. |
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|
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# License |
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|
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This model is developed entirely for academic research and free |
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commercial use, but it must adhere to the |
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[license](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt) |
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from [01-ai](https://huggingface.co/01-ai). |
