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

Libraries

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

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

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="InferenceIllusionist/mathstral-7B-v0.1-iMat-GGUF",
	filename="ggml-model-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install from brew

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

Use Docker

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

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

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for InferenceIllusionist/mathstral-7B-v0.1-iMat-GGUF to start chatting

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

mathstral-7B-v0.1-iMat-GGUF

Quantized from fp16.

Weighted quantizations were creating using fp16 GGUF and groups_merged.txt in 105 chunks and n_ctx=512
Static fp16 also included in repo

For a brief rundown of iMatrix quant performance please see this PR

All quants are verified working prior to uploading to repo for your safety and convenience

KL-Divergence Reference Chart (Click on image to view in full size)

Tips: There's no need to download the entire repo, just pick one of the GGUF files. As with smaller 7b models, Q6 or larger is recommended for best results. On quants smaller than Q3, repetition penalty = 1.05 - 1.3 and min P = 0.05 mitigated some issues, but set your expectations accordingly

Original model card can be found here

Downloads last month: 716

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

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