--- datasets: - Trelis/function_calling_v3 license: other extra_gated_prompt: "Purchase access to this repo [HERE](https://buy.stripe.com/3cs3cY5tPdmbaMU6ps)!" tags: - function-calling - function calling --- # Function Calling Fine-tuned DeepSeek Chat 67B Purchase access to this model [here](https://buy.stripe.com/3cs3cY5tPdmbaMU6ps). This model is fine-tuned for function calling. - The function metadata format is the same as used for OpenAI. - The model is suitable for commercial use. - There is no GGUF yet as I'm awaiting a tokenizer.model file from the base repo. Check out other fine-tuned function calling models [here](https://trelis.com/function-calling/). ## Quick Server Setup Runpod one click templates: (You must add a HuggingFace Hub access token (HUGGING_FACE_HUB_TOKEN) to the environment variables as this is a gated model.) - [TGI 8bit EETQ](https://runpod.io/gsc?template=j29uypqrc1&ref=jmfkcdio). - [TGI API AWQ](https://runpod.io/gsc?template=cfzbdwjcpx&ref=jmfkcdio) Runpod Affiliate [Link](https://runpod.io?ref=jmfkcdio) (helps support the Trelis channel). ## Inference Scripts See below for sample prompt format. Complete inference scripts are available for purchase [here](https://trelis.com/enterprise-server-api-and-inference-guide/): - Easily format prompts using tokenizer.apply_chat_format (starting from openai formatted functions and a list of messages) - Automate catching, handling and chaining of function calls. ## Prompt Format ``` B_FUNC, E_FUNC = "You have access to the following functions. Use them if required:\n\n", "\n\n" B_INST, E_INST = "User: ", "\n\nAssistant:" #Deepseek prompt = f"{B_INST}{B_FUNC}{functionList.strip()}{E_FUNC}{user_prompt.strip()}{E_INST}\n\n" ``` ### Using tokenizer.apply_chat_template For an easier application of the prompt, you can set up as follows: Set up `messages`: ``` [ { "role": "function_metadata", "content": "FUNCTION_METADATA" }, { "role": "user", "content": "What is the current weather in London?" }, { "role": "function_call", "content": "{\n \"name\": \"get_current_weather\",\n \"arguments\": {\n \"city\": \"London\"\n }\n}" }, { "role": "function_response", "content": "{\n \"temperature\": \"15 C\",\n \"condition\": \"Cloudy\"\n}" }, { "role": "assistant", "content": "The current weather in London is Cloudy with a temperature of 15 Celsius" } ] ``` with `FUNCTION_METADATA` as: ``` [ { "type": "function", "function": { "name": "get_current_weather", "description": "This function gets the current weather in a given city", "parameters": { "type": "object", "properties": { "city": { "type": "string", "description": "The city, e.g., San Francisco" }, "format": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "The temperature unit to use." } }, "required": ["city"] } } }, { "type": "function", "function": { "name": "get_clothes", "description": "This function provides a suggestion of clothes to wear based on the current weather", "parameters": { "type": "object", "properties": { "temperature": { "type": "string", "description": "The temperature, e.g., 15 C or 59 F" }, "condition": { "type": "string", "description": "The weather condition, e.g., 'Cloudy', 'Sunny', 'Rainy'" } }, "required": ["temperature", "condition"] } } } ] ``` and then apply the chat template to get a formatted prompt: ``` tokenizer = AutoTokenizer.from_pretrained('Trelis/deepseek-llm-67b-chat-function-calling-v3', trust_remote_code=True) prompt = tokenizer.apply_chat_template(prompt, tokenize=False) ``` If you are using a gated model, you need to first run: ``` pip install huggingface_hub huggingface-cli login ``` ### Manual Prompt: ``` User: You have access to the following functions. Use them if required: [ { "type": "function", "function": { "name": "get_stock_price", "description": "Get the stock price of an array of stocks", "parameters": { "type": "object", "properties": { "names": { "type": "array", "items": { "type": "string" }, "description": "An array of stocks" } }, "required": [ "names" ] } } }, { "type": "function", "function": { "name": "get_big_stocks", "description": "Get the names of the largest N stocks by market cap", "parameters": { "type": "object", "properties": { "number": { "type": "integer", "description": "The number of largest stocks to get the names of, e.g. 25" }, "region": { "type": "string", "description": "The region to consider, can be \"US\" or \"World\"." } }, "required": [ "number" ] } } } ] Get the price of Apple's stock Assistant: { "name": "get_stock_price", "arguments": { "names": [ "AAPL" ] } }<｜end▁of▁sentence｜> ``` # Dataset See [Trelis/function_calling_v3](https://huggingface.co/datasets/Trelis/function_calling_v3). # License This model may be used commercially for inference according to the terms of the DeepSeek 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). ** The original model card follows below: **

DeepSeek Chat

[🏠Homepage] | [🤖 Chat with DeepSeek LLM] | [Discord] | [Wechat(微信)]

### 1. Introduction of Deepseek LLM Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community. ### 2. Model Summary `deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM) - **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/) ### 3. How to Use Here give some examples of how to use our model. #### Chat Completion ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-llm-67b-chat" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id messages = [ {"role": "user", "content": "Who are you?"} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result) ``` Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input. ``` User: {messages[0]['content']} Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']} Assistant: ``` **Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input. ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).