Instructions to use mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF", dtype="auto") - llama-cpp-python
How to use mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF", filename="Pixtral-Large-Instruct-2411-hf.Q2_K.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 mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF 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 mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K
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 mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K
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 mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K
Use Docker
docker model run hf.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF with Ollama:
ollama run hf.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K
- Unsloth Studio
How to use mradermacher/Pixtral-Large-Instruct-2411-hf-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 mradermacher/Pixtral-Large-Instruct-2411-hf-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 mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K
- Lemonade
How to use mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Pixtral-Large-Instruct-2411-hf-GGUF:Q2_K
Run and chat with the model
lemonade run user.Pixtral-Large-Instruct-2411-hf-GGUF-Q2_K
List all available models
lemonade list
For anyone who finds this
In my exl2 copy of pixtral, the rope theta was wrong, it should be 1000000.0 and not the gigantor value included in the config. You can leave it how you want but your long context will collapse into a black hole.