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.

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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

Hermes Agent

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

OpenClaw

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

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

danielhanchen commited on Dec 15, 2025

Commit

b803431

verified ·

1 Parent(s): 9504386

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -12

README.md CHANGED Viewed

@@ -1,19 +1,13 @@
 ---
-library_name: vllm
-inference: false
 base_model:
 - mistralai/Devstral-Small-2-24B-Instruct-2512
-extra_gated_description: >-
-  If you want to learn more about how we process your personal data, please read
-  our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
 tags:
 - mistral-common
 - unsloth
-license: apache-2.0
 ---
-> [!NOTE]
->  Includes Unsloth **chat template fixes**! <br> For `llama.cpp`, use `--jinja`
->
 <div>
 <p style="margin-top: 0;margin-bottom: 0;">
@@ -26,13 +20,12 @@ license: apache-2.0
     <a href="https://discord.gg/unsloth">
       <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
     </a>
-    <a href="https://docs.unsloth.ai/">
       <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
     </a>
   </div>
 </div>
 # Devstral Small 2 24B Instruct 2512
 Devstral is an agentic LLM for software engineering tasks. **Devstral Small 2** excels at using tools to explore codebases, editing multiple files and power software engineering agents.
 The model achieves remarkable performance on SWE-bench.
@@ -1626,4 +1619,4 @@ print(json.dumps(messages[origin_messages_len:], indent=2))
 This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.txt).
-*You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.*

 ---
 base_model:
 - mistralai/Devstral-Small-2-24B-Instruct-2512
 tags:
 - mistral-common
 - unsloth
+- mistral
+license: other
 ---
+# Read our How to [Run Devstral 2 Guide!](https://docs.unsloth.ai/models/devstral-2)
 <div>
 <p style="margin-top: 0;margin-bottom: 0;">
     <a href="https://discord.gg/unsloth">
       <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
     </a>
+    <a href="https://docs.unsloth.ai/models/devstral-2">
       <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
     </a>
   </div>
 </div>
 # Devstral Small 2 24B Instruct 2512
 Devstral is an agentic LLM for software engineering tasks. **Devstral Small 2** excels at using tools to explore codebases, editing multiple files and power software engineering agents.
 The model achieves remarkable performance on SWE-bench.
 This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.txt).
+*You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.*