Instructions to use bartowski/Mistral-Small-24B-Instruct-2501-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/Mistral-Small-24B-Instruct-2501-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/Mistral-Small-24B-Instruct-2501-GGUF", filename="Mistral-Small-24B-Instruct-2501-IQ2_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bartowski/Mistral-Small-24B-Instruct-2501-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 bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Mistral-Small-24B-Instruct-2501-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 bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/Mistral-Small-24B-Instruct-2501-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 bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/Mistral-Small-24B-Instruct-2501-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/Mistral-Small-24B-Instruct-2501-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/Mistral-Small-24B-Instruct-2501-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M
- Ollama
How to use bartowski/Mistral-Small-24B-Instruct-2501-GGUF with Ollama:
ollama run hf.co/bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M
- Unsloth Studio
How to use bartowski/Mistral-Small-24B-Instruct-2501-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 bartowski/Mistral-Small-24B-Instruct-2501-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 bartowski/Mistral-Small-24B-Instruct-2501-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/Mistral-Small-24B-Instruct-2501-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use bartowski/Mistral-Small-24B-Instruct-2501-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M
- Lemonade
How to use bartowski/Mistral-Small-24B-Instruct-2501-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Mistral-Small-24B-Instruct-2501-GGUF-Q4_K_M
List all available models
lemonade list
Tool Call Support?
Are you planning to support tool calls with this model? When running in ollama it says hf.co/bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q6_K does not support tools while the one pulled straight from ollama works fine with tool calls, though they don't have this equivalent quant yet on there. It is also fine if you are not planning to support tool calls, but it is a primary use I have for this model.
If you have pulled the offcial model from Ollama, you can then do ollama show mistral-small:24b --modelfile, save the modelfile, replace the FROM line with FROM hf.co/bartowski/Mistral-Small-24B-Instruct-2501-GGUF:Q6_K and then ollama create mistral-small-tools -f <location of the patched file>
I suspect the difference is in the template they have.
Also, can you just post the generated modelfile here or to pastebin, I don't want to pull the model to get it, and I need to do something similar with it
Looks like that solution worked, so the difference was in the template, like you suspected.
Can you post the modelfile here or to pastebin please?