Instructions to use AntoineChatry/mistral-7b-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AntoineChatry/mistral-7b-python with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AntoineChatry/mistral-7b-python",
	filename="mistral-7b-instruct-v0.3.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use AntoineChatry/mistral-7b-python with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AntoineChatry/mistral-7b-python:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AntoineChatry/mistral-7b-python:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AntoineChatry/mistral-7b-python:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AntoineChatry/mistral-7b-python: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 AntoineChatry/mistral-7b-python:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AntoineChatry/mistral-7b-python: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 AntoineChatry/mistral-7b-python:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AntoineChatry/mistral-7b-python:Q4_K_M

Use Docker

docker model run hf.co/AntoineChatry/mistral-7b-python:Q4_K_M

LM Studio
Jan
Ollama
How to use AntoineChatry/mistral-7b-python with Ollama:
```
ollama run hf.co/AntoineChatry/mistral-7b-python:Q4_K_M
```

Unsloth Studio new

How to use AntoineChatry/mistral-7b-python 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 AntoineChatry/mistral-7b-python 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 AntoineChatry/mistral-7b-python to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AntoineChatry/mistral-7b-python to start chatting

Docker Model Runner
How to use AntoineChatry/mistral-7b-python with Docker Model Runner:
```
docker model run hf.co/AntoineChatry/mistral-7b-python:Q4_K_M
```

Lemonade

How to use AntoineChatry/mistral-7b-python with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AntoineChatry/mistral-7b-python:Q4_K_M

Run and chat with the model

lemonade run user.mistral-7b-python-Q4_K_M

List all available models

lemonade list

mistral-7b-python-gguf

Conversational Python fine-tune of Mistral 7B exported to GGUF format for local inference.

Base model: Mistral 7B
Fine-tuning framework: Unsloth
Format: GGUF
Author: AntoineChatry

⚠️ Disclaimer

This is an early experimental fine-tune.

It is not production-ready, not fully aligned, and not optimized for reliability or long-form reasoning.
This project was created primarily for learning and experimentation.

Please do not expect state-of-the-art coding performance.

Model Overview

This model is a conversational fine-tune of Mistral 7B trained primarily on:

ShareGPT-style conversations
Python-focused discussions
Coding Q&A format

The objective was to:

Experiment with fine-tuning
Build a conversational Python model
Export to GGUF for llama.cpp compatibility
Test local inference workflows

No RLHF or advanced alignment was applied beyond the base model.

Known Limitations

Repetition Issues

Frequently repeats phrases like:

"Here's the code:"
Can loop or restate similar sentences
Overuses patterns learned from dataset formatting

Weak Long-Form Explanations

Struggles with multi-paragraph structured reasoning
May repeat itself when asked for detailed explanations
Limited depth on conceptual explanations

Instruction Following

Not fully aligned
May ignore strict formatting constraints
Tends to prioritize generating code over detailed explanations

Dataset Bias

Strong ShareGPT conversational tone
Python-heavy bias
Some templated response structure

What Works Reasonably Well

Short Python snippets
Basic debugging help
Simple function generation
Conversational coding prompts

Best performance is observed when:

Prompts are clear and direct
Expected output is short
Tasks are code-focused

Training Details

Base: Mistral 7B
Dataset format: ShareGPT-style conversational dataset (Python-oriented)
Fine-tuned using Unsloth notebooks
Converted to GGUF for llama.cpp compatibility
Quantized version included (Q4_K_M)

No additional safety tuning or post-training optimization was applied.

Example Usage

This model was finetuned and converted to GGUF format using Unsloth.

llama.cpp

For text-only LLMs:

llama-cli -hf AntoineChatry/mistral-7b-python-gguf --jinja

For multimodal models:

llama-mtmd-cli -hf AntoineChatry/mistral-7b-python-gguf --jinja

Available Model files:

mistral-7b-instruct-v0.3.Q4_K_M.gguf

Ollama

An Ollama Modelfile is included for easy deployment.

Example:

ollama create mistral-python -f Modelfile
ollama run mistral-python

Why This Model Is Public

This model represents a learning milestone.

Sharing imperfect models helps:

Document fine-tuning progress
Enable experimentation
Collect feedback
Iterate toward better versions

This is not a finished product.

Unsloth

This model was trained 2x faster using Unsloth.

https://github.com/unslothai/unsloth

License

Please refer to the original Mistral 7B license from Mistral AI.

Downloads last month: 110

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for AntoineChatry/mistral-7b-python

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Quantized

(248)

this model