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

Libraries

How to use uonlp/Vistral-7B-Chat-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="uonlp/Vistral-7B-Chat-gguf",
	filename="ggml-vistral-7B-chat-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use uonlp/Vistral-7B-Chat-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf uonlp/Vistral-7B-Chat-gguf:F16
# Run inference directly in the terminal:
llama cli -hf uonlp/Vistral-7B-Chat-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf uonlp/Vistral-7B-Chat-gguf:F16
# Run inference directly in the terminal:
llama cli -hf uonlp/Vistral-7B-Chat-gguf:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf uonlp/Vistral-7B-Chat-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf uonlp/Vistral-7B-Chat-gguf:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf uonlp/Vistral-7B-Chat-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf uonlp/Vistral-7B-Chat-gguf:F16

Use Docker

docker model run hf.co/uonlp/Vistral-7B-Chat-gguf:F16

LM Studio
Jan

vLLM

How to use uonlp/Vistral-7B-Chat-gguf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/uonlp/Vistral-7B-Chat-gguf:F16

Ollama
How to use uonlp/Vistral-7B-Chat-gguf with Ollama:
```
ollama run hf.co/uonlp/Vistral-7B-Chat-gguf:F16
```

Unsloth Studio

How to use uonlp/Vistral-7B-Chat-gguf 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 uonlp/Vistral-7B-Chat-gguf 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 uonlp/Vistral-7B-Chat-gguf to start chatting

Using HuggingFace Spaces for Unsloth

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

Atomic Chat new
Docker Model Runner
How to use uonlp/Vistral-7B-Chat-gguf with Docker Model Runner:
```
docker model run hf.co/uonlp/Vistral-7B-Chat-gguf:F16
```

Lemonade

How to use uonlp/Vistral-7B-Chat-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull uonlp/Vistral-7B-Chat-gguf:F16

Run and chat with the model

lemonade run user.Vistral-7B-Chat-gguf-F16

List all available models

lemonade list

The challenge with large language models is that they cannot be executed locally on your laptop. Thanks to llama.cpp project, it is now feasible to operate our Vistral-7B-Chat on a single computer (Window or Macbook) even without a dedicated GPU.

Vistral-7B-Chat - GGUF

Model creator: Viet Mistral
Original model: Vistral-7B-Chat

Description

This repo contains GGUF format model files for Vistral-7B-Chat.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML. GGUF offers numerous advantages over GGML, such as better tokenization, and support for special tokens. It also supports metadata, and is designed to be extensible.

Here is several clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.

Prompt template: Vistral-7B-Chat

<s>[INST] <<SYS>>
Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực. Hãy luôn trả lời một cách hữu ích nhất có thể, đồng thời giữ an toàn.
Câu trả lời của bạn không nên chứa bất kỳ nội dung gây hại, phân biệt chủng tộc, phân biệt giới tính, độc hại, nguy hiểm hoặc bất hợp pháp nào. Hãy đảm bảo rằng các câu trả lời của bạn không có thiên kiến xã hội và mang tính tích cực.Nếu một câu hỏi không có ý nghĩa hoặc không hợp lý về mặt thông tin, hãy giải thích tại sao thay vì trả lời một điều gì đó không chính xác. Nếu bạn không biết câu trả lời cho một câu hỏi, hãy trẳ lời là bạn không biết và vui lòng không chia sẻ thông tin sai lệch.
<</SYS>>

{prompt} [/INST]

You can also use the chat template file in this repository.

LM Studio

To deploy Vistral locally on LM Studio, ensure you are utilizing the specified chat template, download here. Before initiating the process, make sure to upload the chat template, as illustrated in the image below:

This step is crucial for the proper functioning of Vistral on your local machine.

Use with langchain

Citation

@article{chien2023vistral,
  author = {Chien Van Nguyen, Thuat Nguyen, Quan Nguyen, Huy Huu Nguyen, Björn Plüster, Nam Pham, Huu Nguyen, Patrick Schramowski, Thien Huu Nguyen},
  title = {Vistral-7B-Chat - Towards a State-of-the-Art Large Language Model for Vietnamese},
  year = 2023,
}

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

16-bit

View +1 variant

uonlp
/

Vistral-7B-Chat-gguf