Instructions to use bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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 bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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/bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M

Ollama
How to use bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF with Ollama:
```
ollama run hf.co/bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M
```

Unsloth Studio

How to use bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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/mistralai_Mistral-Small-3.1-24B-Instruct-2503-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/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF to start chatting

Docker Model Runner
How to use bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF / mistralai_Mistral-Small-3.1-24B-Instruct-2503-IQ3_XXS.gguf

bartowski

Upload mistralai_Mistral-Small-3.1-24B-Instruct-2503-IQ3_XXS.gguf with huggingface_hub

356209a verified over 1 year ago

Download

History Blame GGUF Editor new Delete

9.28 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:: 36b3d9e4f49c0c02d73b031872a1dcff046dc28c14bbc2fc6ce09136d9f9fd83
Size of remote file:: 9.28 GB
SHA256:: 445325fd09bea660114208c661323f61c3a0bf35019f255265c9b24c512fe7eb

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