Instructions to use QuantFactory/Mistral-7B-v0.3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Mistral-7B-v0.3-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Mistral-7B-v0.3-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Mistral-7B-v0.3-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Mistral-7B-v0.3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Mistral-7B-v0.3-GGUF",
	filename="Mistral-7B-v0.3.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Mistral-7B-v0.3-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M

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 QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M

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 QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Mistral-7B-v0.3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Mistral-7B-v0.3-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Mistral-7B-v0.3-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Mistral-7B-v0.3-GGUF 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 "QuantFactory/Mistral-7B-v0.3-GGUF" \
    --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": "QuantFactory/Mistral-7B-v0.3-GGUF",
		"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 "QuantFactory/Mistral-7B-v0.3-GGUF" \
        --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": "QuantFactory/Mistral-7B-v0.3-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/Mistral-7B-v0.3-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Mistral-7B-v0.3-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 QuantFactory/Mistral-7B-v0.3-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 QuantFactory/Mistral-7B-v0.3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Mistral-7B-v0.3-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Mistral-7B-v0.3-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Mistral-7B-v0.3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Mistral-7B-v0.3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-v0.3-GGUF-Q4_K_M

List all available models

lemonade list

Mistral-7B-v0.3-GGUF

This is quantized version of mistralai/Mistral-7B-v0.3 created using llama.cpp

Model Description

The Mistral-7B-v0.3 Large Language Model (LLM) is a Mistral-7B-v0.2 with extended vocabulary.

Mistral-7B-v0.3 has the following changes compared to Mistral-7B-v0.2

Extended vocabulary to 32768

Installation

It is recommended to use mistralai/Mistral-7B-v0.3 with mistral-inference. For HF transformers code snippets, please keep scrolling.

pip install mistral_inference

Demo

After installing mistral_inference, a mistral-demo CLI command should be available in your environment.

mistral-demo $HOME/mistral_models/7B-v0.3

Should give something along the following lines:

This is a test of the emergency broadcast system. This is only a test.

If this were a real emergency, you would be told what to do.

This is a test
=====================
This is another test of the new blogging software. I’m not sure if I’m going to keep it or not. I’m not sure if I’m going to keep
=====================
This is a third test, mistral AI is very good at testing. 🙂

This is a third test, mistral AI is very good at testing. 🙂

This
=====================

Generate with `transformers`

If you want to use Hugging Face transformers to generate text, you can do something like this.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mistral-7B-v0.3"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("Hello my name is", return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall

Downloads last month: 401

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Mistral-7B-v0.3-GGUF

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(84)

this model

Collection including QuantFactory/Mistral-7B-v0.3-GGUF

Mistral-AI

Collection

Quantized versions of models by mistralai • 19 items • Updated Oct 3, 2024 • 6