Instructions to use srini98/mistral-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

mistral-function-calling / README.md

srini98

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f3cbe79 verified over 2 years ago

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metadata

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

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.