Instructions to use QuantFactory/mathstral-7B-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/mathstral-7B-v0.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/mathstral-7B-v0.1-GGUF",
	filename="mathstral-7B-v0.1.Q2_K.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 QuantFactory/mathstral-7B-v0.1-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 QuantFactory/mathstral-7B-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/mathstral-7B-v0.1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/mathstral-7B-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/mathstral-7B-v0.1-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/mathstral-7B-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/mathstral-7B-v0.1-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/mathstral-7B-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/mathstral-7B-v0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/mathstral-7B-v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/mathstral-7B-v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/mathstral-7B-v0.1-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": "QuantFactory/mathstral-7B-v0.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/mathstral-7B-v0.1-GGUF:Q4_K_M

Ollama
How to use QuantFactory/mathstral-7B-v0.1-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/mathstral-7B-v0.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/mathstral-7B-v0.1-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/mathstral-7B-v0.1-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/mathstral-7B-v0.1-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/mathstral-7B-v0.1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/mathstral-7B-v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/mathstral-7B-v0.1-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/mathstral-7B-v0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/mathstral-7B-v0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.mathstral-7B-v0.1-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/mathstral-7B-v0.1-GGUF

This is quantized version of mistralai/mathstral-7B-v0.1 created using llama.cpp

Model Card for Mathstral-7B-v0.1

Mathstral 7B is a model specializing in mathematical and scientific tasks, based on Mistral 7B. You can read more in the official blog post.

Installation

It is recommended to use mistralai/mathstral-7B-v0.1 with mistral-inference

pip install mistral_inference>=1.2.0

Download

from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', 'mathstral-7B-v0.1')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/mathstral-7B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)

Chat

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

mistral-chat $HOME/mistral_models/mathstral-7B-v0.1 --instruct --max_tokens 256

You can then start chatting with the model, e.g. prompt it with something like:

"Albert likes to surf every week. Each surfing session lasts for 4 hours and costs $20 per hour. How much would Albert spend in 5 weeks?"

Evaluation

We evaluate Mathstral 7B and open-weight models of the similar size on industry-standard benchmarks.

Benchmarks	MATH	GSM8K (8-shot)	Odyssey Math maj@16	GRE Math maj@16	AMC 2023 maj@16	AIME 2024 maj@16
Mathstral 7B	56.6	77.1	37.2	56.9	42.4	2/30
DeepSeek Math 7B	44.4	80.6	27.6	44.6	28.0	0/30
Llama3 8B	28.4	75.4	24.0	26.2	34.4	0/30
GLM4 9B	50.2	48.8	18.9	46.2	36.0	1/30
QWen2 7B	56.8	32.7	24.8	58.5	35.2	2/30
Gemma2 9B	48.3	69.5	18.6	52.3	31.2	1/30

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall

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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/mathstral-7B-v0.1-GGUF

Base model

mistralai/Mathstral-7B-v0.1

Quantized

(29)

this model