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

Libraries

How to use SanctumAI/mathstral-7B-v0.1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SanctumAI/mathstral-7B-v0.1-GGUF", dtype="auto")

llama-cpp-python

How to use SanctumAI/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="SanctumAI/mathstral-7B-v0.1-GGUF",
	filename="mathstral-7B-v0.1.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 SanctumAI/mathstral-7B-v0.1-GGUF with llama.cpp:

Install from brew

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

Use Docker

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

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

Unsloth Studio new

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

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SanctumAI/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

This model was quantized by SanctumAI. To leave feedback, join our community in Discord.

Mathstral 7B v0.1 GGUF

Model creator: mistralai
Original model: mathstral-7B-v0.1

Model Summary:

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

Prompt Template:

If you're using Sanctum app, simply use Mistral model preset.

Prompt template:

[INST]{prompt} [/INST]

Hardware Requirements Estimate

Name	Quant method	Size	Memory (RAM, vRAM) required
mathstral-7B-v0.1.Q2_K.gguf	Q2_K	2.72 GB	? GB
mathstral-7B-v0.1.Q3_K_S.gguf	Q3_K_S	3.17 GB	? GB
mathstral-7B-v0.1.Q3_K_M.gguf	Q3_K_M	3.52 GB	? GB
mathstral-7B-v0.1.Q3_K_L.gguf	Q3_K_L	3.83 GB	? GB
mathstral-7B-v0.1.Q4_0.gguf	Q4_0	4.11 GB	? GB
mathstral-7B-v0.1.Q4_K_S.gguf	Q4_K_S	4.14 GB	? GB
mathstral-7B-v0.1.Q4_K_M.gguf	Q4_K_M	4.37 GB	? GB
mathstral-7B-v0.1.Q4_K.gguf	Q4_K	4.37 GB	? GB
mathstral-7B-v0.1.Q4_1.gguf	Q4_1	4.56 GB	? GB
mathstral-7B-v0.1.Q5_0.gguf	Q5_0	5.00 GB	? GB
mathstral-7B-v0.1.Q5_K_S.gguf	Q5_K_S	5.00 GB	? GB
mathstral-7B-v0.1.Q5_K_M.gguf	Q5_K_M	5.14 GB	? GB
mathstral-7B-v0.1.Q5_K.gguf	Q5_K	5.14 GB	? GB
mathstral-7B-v0.1.Q5_1.gguf	Q5_1	5.45 GB	? GB
mathstral-7B-v0.1.Q6_K.gguf	Q6_K	5.95 GB	? GB
mathstral-7B-v0.1.Q8_0.gguf	Q8_0	7.70 GB	? GB
mathstral-7B-v0.1.f16.gguf	f16	14.50 GB	? GB

Disclaimer

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Downloads last month: 982

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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