Instructions to use mrfakename/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 mrfakename/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 mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S

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 mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S

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 mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S

Use Docker

docker model run hf.co/mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S

LM Studio
Jan

vLLM

How to use mrfakename/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 "mrfakename/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": "mrfakename/mistral-small-3.1-24b-instruct-2503-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S

Ollama
How to use mrfakename/mistral-small-3.1-24b-instruct-2503-gguf with Ollama:
```
ollama run hf.co/mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S
```
Unsloth Desktop
Docker Model Runner
How to use mrfakename/mistral-small-3.1-24b-instruct-2503-gguf with Docker Model Runner:
```
docker model run hf.co/mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S
```

Lemonade

How to use mrfakename/mistral-small-3.1-24b-instruct-2503-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mrfakename/mistral-small-3.1-24b-instruct-2503-gguf:Q4_K_S

Run and chat with the model

lemonade run user.mistral-small-3.1-24b-instruct-2503-gguf-Q4_K_S

List all available models

lemonade list

Atomic Chat

Also see:

24B Instruct GGUF (this model)
24B Instruct HF
24B Base HF

GGUF quants for Mistral Small 3.1 Instruct 24B, compatible with llama.cpp (or almost any other llama.cpp app) in the Mistral format.

Use the Mistral chat template.

Only the text component has been converted to GGUF, does not work as a vision model.

No imatrix yet, sorry!

Downloads last month: 664

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for mrfakename/mistral-small-3.1-24b-instruct-2503-gguf

Base model

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

Finetuned

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

Finetuned

mrfakename/mistral-small-3.1-24b-instruct-2503-hf

Quantized

(6)

this model