Instructions to use backpack-run/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 backpack-run/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 backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/Devstral-Small-2-24B-Instruct-2512-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 backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/Devstral-Small-2-24B-Instruct-2512-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 backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf backpack-run/Devstral-Small-2-24B-Instruct-2512-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 backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M

Use Docker

docker model run hf.co/backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "backpack-run/Devstral-Small-2-24B-Instruct-2512-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": "backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M

Ollama
How to use backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF with Ollama:
```
ollama run hf.co/backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M
```
Unsloth Desktop

How to use backpack-run/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 backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF with Docker Model Runner:
```
docker model run hf.co/backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Devstral-Small-2-24B-Instruct-2512-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use backpack-run/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 backpack-run/Devstral-Small-2-24B-Instruct-2512-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 backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use backpack-run/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 backpack-run/Devstral-Small-2-24B-Instruct-2512-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 "backpack-run/Devstral-Small-2-24B-Instruct-2512-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"

Devstral-Small-2-24B-Instruct-2512 — Backpack GGUF

🎒 Backpack Verified

GGUF quantizations of mistralai/Devstral-Small-2-24B-Instruct-2512, tested for llama.cpp-compatible image-and-text inference and packaged for Backpack.

Model

Property	Value
Original model	`mistralai/Devstral-Small-2-24B-Instruct-2512`
Original publisher	mistralai
Upstream revision	`55c5b41e98c2dbd21b0c8afffc540dcfc9eb5128`
Architecture	Mistral3ForConditionalGeneration
Parameters	24,011,361,840
Context length	Not declared
Input modalities	text, image
Output modalities	text
License	apache-2.0

Available packages

Quantization	Size	Approx. RAM	Recommended for
Q4_K_M	13.3 GiB	21.23 GB	Most users
Q5_K_M	15.6 GiB	24.51 GB	Higher quality
Q8_0	23.3 GiB	35.7 GB	Plenty of memory

Memory values are estimates, not guarantees. Runtime configuration and context length change actual use.

Multimodal projector

File	Precision	Size
`Devstral-Small-2-24B-Instruct-2512-mmproj-F16.gguf`	F16	837.4 MiB

The projector is required for image input and must be used with one of the language-model GGUF files above.

Backpack recommendation

Recommended: Q4_K_M. It usually offers a practical quality, size, and speed balance for local inference.

Run with llama.cpp

Using the llama.cpp revision recorded below:

llama-mtmd-cli --model Devstral-Small-2-24B-Instruct-2512-Q4_K_M.gguf --mmproj Devstral-Small-2-24B-Instruct-2512-mmproj-F16.gguf --image image.jpg --prompt "Describe this image."

Run with Backpack

These artifacts and backpack-model.yaml are prepared for the Backpack AI workspace.

Validation

Package	Integrity	Load	Inference	Tokenizer
Q4_K_M	passed	passed	passed	passed
Q5_K_M	passed	passed	passed	passed
Q8_0	passed	passed	passed	passed

Capability qualification: code

Tested with Devstral-Small-2-24B-Instruct-2512-Q4_K_M.gguf. These are deterministic smoke tests, not benchmark scores.

Capability	Status
chat	passed
code_generation	passed
structured_tool_arguments	passed
tool_calling	passed
multi_turn_tool_loop	passed

Packaged: 2026-08-30T16:36:59.071073+00:00
llama.cpp revision: bdf3955159d7184f44b76091973eeff532890a35
SHA-256 checksums: see checksums.sha256
Devstral-Small-2-24B-Instruct-2512-Q4_K_M.gguf: e78a384e8f62862fa111a0ead8ff1d8f1373f0b17d00509b5cc77e91ee960a71
Devstral-Small-2-24B-Instruct-2512-Q5_K_M.gguf: bc963f56b456eb681e8cc966314b7ba2919b0ac26dd514cd5fef5bff7db65706
Devstral-Small-2-24B-Instruct-2512-Q8_0.gguf: d8e3a8fb757957d5beb11514e37b9b907707d08be38d5e494a63e514a4b7138f
Devstral-Small-2-24B-Instruct-2512-mmproj-F16.gguf: 54fec1ceaf733d4f2ddc265c879997da7c675ade6d330f52d7e6e5f62a4738fd

Provenance

The source model was resolved to immutable revision 55c5b41e98c2dbd21b0c8afffc540dcfc9eb5128. It was converted with llama.cpp's convert_hf_to_gguf.py, including its multimodal projector, and quantized with llama-quantize; the exact tested revision is recorded above and in backpack-model.yaml.

License and attribution

Upstream declares apache-2.0. Review the upstream model card and comply with all applicable terms.

Backpack does not claim ownership of the original model. These artifacts are packaged and quantized distributions of the upstream model.

Disclaimer

Quantization can alter output quality. Memory estimates vary with runtime configuration, context length, and hardware.

Downloads last month: 171

GGUF

Model size

24B params

Architecture

mistral3

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for backpack-run/Devstral-Small-2-24B-Instruct-2512-GGUF

Base model

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

Quantized

mistralai/Devstral-Small-2-24B-Instruct-2512

Quantized

(46)

this model