Instructions to use unsloth/Mistral-Small-4-119B-2603-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use unsloth/Mistral-Small-4-119B-2603-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="unsloth/Mistral-Small-4-119B-2603-GGUF", filename="BF16/Mistral-Small-4-119B-2603-BF16-00001-of-00006.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/Mistral-Small-4-119B-2603-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama-cli -hf unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama-cli -hf unsloth/Mistral-Small-4-119B-2603-GGUF:UD-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 unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf unsloth/Mistral-Small-4-119B-2603-GGUF:UD-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 unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M
Use Docker
docker model run hf.co/unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M
- LM Studio
- Jan
- Ollama
How to use unsloth/Mistral-Small-4-119B-2603-GGUF with Ollama:
ollama run hf.co/unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M
- Unsloth Studio
How to use unsloth/Mistral-Small-4-119B-2603-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 unsloth/Mistral-Small-4-119B-2603-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 unsloth/Mistral-Small-4-119B-2603-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/Mistral-Small-4-119B-2603-GGUF to start chatting
- Pi
How to use unsloth/Mistral-Small-4-119B-2603-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use unsloth/Mistral-Small-4-119B-2603-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use unsloth/Mistral-Small-4-119B-2603-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M
- Lemonade
How to use unsloth/Mistral-Small-4-119B-2603-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/Mistral-Small-4-119B-2603-GGUF:UD-Q4_K_M
Run and chat with the model
lemonade run user.Mistral-Small-4-119B-2603-GGUF-UD-Q4_K_M
List all available models
lemonade list
Eagle model
I'm not sure right now to what extent this is already supported in llama.cpp or other engines, but will you also be providing quants of the Eagle model?
The Eagle model is a few hundred megabytes in size. Not much to quantize there. And llama.cpp does not currently have any support for Eagle specdec.
But llama.cpp has general support for speculative decoding models, and as there is essentially no documentation on the Eagle model (at least i could not find it?) i am not sure if it could not work with minor changes as a general speculative decoding model?