Instructions to use AntoineChatry/mistral-7b-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AntoineChatry/mistral-7b-python 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 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 serve -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
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
- Atomic Chat new
- 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
| tags: | |
| - gguf | |
| - llama.cpp | |
| - unsloth | |
| - mistral | |
| - python | |
| base_model: | |
| - mistralai/Mistral-7B-Instruct-v0.3 | |
| # 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: | |
| ```bash | |
| llama-cli -hf AntoineChatry/mistral-7b-python-gguf --jinja | |
| ``` | |
| For multimodal models: | |
| ```bash | |
| 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: | |
| ```bash | |
| 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 | |
| <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/> | |
| --- | |
| # License | |
| Please refer to the original Mistral 7B license from Mistral AI. |