Instructions to use srini98/mistral-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use srini98/mistral-function-calling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="srini98/mistral-function-calling")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("srini98/mistral-function-calling") model = AutoModelForCausalLM.from_pretrained("srini98/mistral-function-calling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use srini98/mistral-function-calling with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "srini98/mistral-function-calling" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "srini98/mistral-function-calling", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/srini98/mistral-function-calling
- SGLang
How to use srini98/mistral-function-calling with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "srini98/mistral-function-calling" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "srini98/mistral-function-calling", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "srini98/mistral-function-calling" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "srini98/mistral-function-calling", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use srini98/mistral-function-calling with Docker Model Runner:
docker model run hf.co/srini98/mistral-function-calling
| language: | |
| - en | |
| datasets: | |
| - glaiveai/glaive-function-calling-v2 | |
| The model was finetuned using the glaive dataset using qlora and full finetuning using FSDP. | |
| Dataset link : [Link here](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | |
| For training , inference and evaluation kindly check this repository: | |
| https://github.com/Srini-98/Function-Calling-Using-Mistral | |
| Use the following prompt format | |
| ``` | |
| SYSTEM: You are a helpful assistant with access to the following functions. Use them if required - | |
| { | |
| "name": "function_name", | |
| "description": "description", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "param_name1": { | |
| "type": "string", | |
| "description": "description of param" | |
| }, | |
| "param_name2": { | |
| "type": "string", | |
| "description": "description of param" | |
| }, | |
| "param_name3":{ | |
| "type: "string", | |
| "description" : "description of param" | |
| } | |
| }, | |
| "required": [ | |
| "param_name1", | |
| ] | |
| } | |
| } | |
| USER: {question here} | |
| ASSISTANT: {model answer} <|endoftext|> | |
| ``` | |
| Example: | |
| ``` | |
| SYSTEM: You are a helpful assistant with access to the following functions. Use them if required - | |
| { | |
| "name": "calculate_tax", | |
| "description": "Calculate the tax amount", | |
| "parameters": { | |
| "type": "object", | |
| "properties": { | |
| "income": { | |
| "type": "number", | |
| "description": "The income amount" | |
| } | |
| }, | |
| "required": [ | |
| "income" | |
| ] | |
| } | |
| } | |
| USER: Hi, I need to calculate my tax for this year. My income is $70,000. | |
| ASSISTANT: <functioncall> {"name": "calculate_tax", "arguments": '{"income": 70000}'} <|endoftext|> | |
| FUNCTION RESPONSE: {"tax_amount": 17500} | |
| ASSISTANT: Based on your income, your tax for this year is $17,500. <|endoftext|> | |
| ``` | |
| The answer generation can be stopped with the <|endoftext|> token. You can add multiple functions as well and set param names. "Required" field forces model to always call that param. |