Instructions to use hiieu/Vistral-7B-Chat-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hiieu/Vistral-7B-Chat-function-calling with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hiieu/Vistral-7B-Chat-function-calling")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hiieu/Vistral-7B-Chat-function-calling")
model = AutoModelForCausalLM.from_pretrained("hiieu/Vistral-7B-Chat-function-calling", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hiieu/Vistral-7B-Chat-function-calling with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hiieu/Vistral-7B-Chat-function-calling"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hiieu/Vistral-7B-Chat-function-calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hiieu/Vistral-7B-Chat-function-calling

SGLang

How to use hiieu/Vistral-7B-Chat-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 "hiieu/Vistral-7B-Chat-function-calling" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hiieu/Vistral-7B-Chat-function-calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "hiieu/Vistral-7B-Chat-function-calling" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hiieu/Vistral-7B-Chat-function-calling",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use hiieu/Vistral-7B-Chat-function-calling with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for hiieu/Vistral-7B-Chat-function-calling to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for hiieu/Vistral-7B-Chat-function-calling to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hiieu/Vistral-7B-Chat-function-calling to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="hiieu/Vistral-7B-Chat-function-calling",
    max_seq_length=2048,
)

Docker Model Runner
How to use hiieu/Vistral-7B-Chat-function-calling with Docker Model Runner:
```
docker model run hf.co/hiieu/Vistral-7B-Chat-function-calling
```

Vistral-7B-Chat-function-calling / README.md

hiieu

Update README.md

ae136f6 verified over 2 years ago

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	---
	library_name: transformers
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- mistral
	- trl
	license: apache-2.0
	language:
	- en
	- vi
	base_model: Viet-Mistral/Vistral-7B-Chat
	---
	## Model Description

	This model was fine-tuned on Vistral-7B-chat for function calling.

	## Usage

	You can find GGUF model here: https://huggingface.co/hiieu/Vistral-7B-Chat-function-calling-gguf

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained('hiieu/Vistral-7B-Chat-function-calling')
	model = AutoModelForCausalLM.from_pretrained(
	'hiieu/Vistral-7B-Chat-function-calling',
	torch_dtype=torch.bfloat16, # change to torch.float16 if you're using V100
	device_map="auto",
	use_cache=True,
	)

	functions_metadata = [
	{
	"type": "function",
	"function": {
	"name": "get_temperature",
	"description": "get temperature of a city",
	"parameters": {
	"type": "object",
	"properties": {
	"city": {
	"type": "string",
	"description": "name"
	}
	},
	"required": [
	"city"
	]
	}
	}
	}
	]

	conversation = [
	{"role": "system", "content": f"""Bạn là một trợ lý hữu ích có quyền truy cập vào các chức năng sau. Sử dụng chúng nếu cần -\n{str(functions_metadata)} Để sử dụng các chức năng này, hãy phản hồi với:\n<functioncall> {{\\"name\\": \\"function_name\\", \\"arguments\\": {{\\"arg_1\\": \\"value_1\\", \\"arg_1\\": \\"value_1\\", ...}} }} </functioncall>\n\nTrường hợp đặc biệt bạn phải xử lý:\n - Nếu không có chức năng nào khớp với yêu cầu của người dùng, bạn sẽ phản hồi một cách lịch sự rằng bạn không thể giúp được.""" },
	{"role": "user", "content": "Thời tiết ở Hà Nội đang là bao nhiêu độ"},
	{"role": "assistant", "content": """<functioncall> {"name": "get_temperature", "arguments": '{"city": "Hà Nội"}'} </functioncall>"""},
	{"role": "user", "content": """<function_response> {"temperature" : "20 C"} </function_response>"""},
	]

	input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)

	out_ids = model.generate(
	input_ids=input_ids,
	max_new_tokens=768,
	do_sample=True,
	top_p=0.95,
	top_k=40,
	temperature=0.1,
	repetition_penalty=1.05,
	)
	assistant = tokenizer.batch_decode(out_ids[:, input_ids.size(1): ], skip_special_tokens=True)[0].strip()
	print("Assistant: ", assistant)
	# >> Assistant: Thời tiết ở Hà Nội hiện tại là khoảng 20 độ C.
	```

	# Uploaded model

	- Developed by: hiieu
	- License: apache-2.0
	- Finetuned from model : Viet-Mistral/Vistral-7B-Chat

	This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)