Instructions to use unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF",
	filename="Devstral-Small-2-24B-Instruct-2512-BF16.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/Devstral-Small-2-24B-Instruct-2512-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 unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL

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/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL

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/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL

LM Studio
Jan
Ollama
How to use unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF with Ollama:
```
ollama run hf.co/unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use unsloth/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-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/Devstral-Small-2-24B-Instruct-2512-GGUF to start chatting

How to use unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL

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/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL

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/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/Devstral-Small-2-24B-Instruct-2512-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Devstral-Small-2-24B-Instruct-2512-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Devstral-Small-2-24B-Instruct-2512-GGUF / Devstral-Small-2-24B-Instruct-2512-Q5_K_M.gguf

danielhanchen

Add files using upload-large-folder tool

c4433c8 verified 7 months ago

Download

History Blame GGUF Editor new Delete

16.8 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 9efecc7dcc4e74d009f494d8d505ceea71bad0d5f27bf9ec113de7cafb2ded6f
Size of remote file:: 16.8 GB
SHA256:: 2da6ca6c4ae387aa7f3f2f4a67bb3e1ca570ce0c69c21e26b8695e75172443b0

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.