Instructions to use Antigma/Devstral-Small-2505-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Antigma/Devstral-Small-2505-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 Antigma/Devstral-Small-2505-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Antigma/Devstral-Small-2505-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 Antigma/Devstral-Small-2505-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Antigma/Devstral-Small-2505-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 Antigma/Devstral-Small-2505-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Antigma/Devstral-Small-2505-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 Antigma/Devstral-Small-2505-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Antigma/Devstral-Small-2505-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Antigma/Devstral-Small-2505-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Antigma/Devstral-Small-2505-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Antigma/Devstral-Small-2505-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": "Antigma/Devstral-Small-2505-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Antigma/Devstral-Small-2505-GGUF:Q4_K_M

Ollama
How to use Antigma/Devstral-Small-2505-GGUF with Ollama:
```
ollama run hf.co/Antigma/Devstral-Small-2505-GGUF:Q4_K_M
```

Unsloth Studio

How to use Antigma/Devstral-Small-2505-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 Antigma/Devstral-Small-2505-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 Antigma/Devstral-Small-2505-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Antigma/Devstral-Small-2505-GGUF to start chatting

How to use Antigma/Devstral-Small-2505-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Antigma/Devstral-Small-2505-GGUF: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": "Antigma/Devstral-Small-2505-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Antigma/Devstral-Small-2505-GGUF with Docker Model Runner:
```
docker model run hf.co/Antigma/Devstral-Small-2505-GGUF:Q4_K_M
```

Lemonade

How to use Antigma/Devstral-Small-2505-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Antigma/Devstral-Small-2505-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Devstral-Small-2505-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Antigma/Devstral-Small-2505-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 Antigma/Devstral-Small-2505-GGUF: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 Antigma/Devstral-Small-2505-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Antigma/Devstral-Small-2505-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Antigma/Devstral-Small-2505-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Antigma/Devstral-Small-2505-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Produced by Antigma Labs, Antigma Quantize Space

Follow Antigma Labs in X https://x.com/antigma_labs

Antigma's GitHub Homepage https://github.com/AntigmaLabs

llama.cpp quantization

Using llama.cpp release b5223 for quantization. Original model: https://huggingface.co/unsloth/Devstral-Small-2505 Run them directly with llama.cpp, or any other llama.cpp based project

Prompt format

<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜>

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Split
devstral-small-2505-q2_k.gguf	Q2_K	8.28 GB	False
devstral-small-2505-q3_k_l.gguf	Q3_K_L	11.55 GB	False
devstral-small-2505-q6_k.gguf	Q6_K	18.02 GB	False
devstral-small-2505-q4_k_m.gguf	Q4_K_M	13.35 GB	False
devstral-small-2505-q5_k_m.gguf	Q5_K_M	15.61 GB	False
devstral-small-2505-q8_0.gguf	Q8_0	23.33 GB	False

Downloading using huggingface-cli

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download https://huggingface.co/Antigma/Devstral-Small-2505-GGUF --include "devstral-small-2505-q2_k.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download https://huggingface.co/Antigma/Devstral-Small-2505-GGUF --include "devstral-small-2505-q2_k.gguf/*" --local-dir ./

You can either specify a new local-dir (deepseek-ai_DeepSeek-V3-0324-Q8_0) or download them all in place (./)

Downloads last month: 74

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Antigma/Devstral-Small-2505-GGUF

Base model

mistralai/Mistral-Small-3.1-24B-Base-2503

Finetuned

mistralai/Mistral-Small-3.1-24B-Instruct-2503

Finetuned

mistralai/Devstral-Small-2505

Finetuned

unsloth/Devstral-Small-2505

Quantized

(3)

this model