Instructions to use eaddario/Mistral-Small-3.2-24B-Instruct-2506-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 eaddario/Mistral-Small-3.2-24B-Instruct-2506-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 eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf eaddario/Mistral-Small-3.2-24B-Instruct-2506-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 eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf eaddario/Mistral-Small-3.2-24B-Instruct-2506-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 eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf eaddario/Mistral-Small-3.2-24B-Instruct-2506-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 eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M

Use Docker

docker model run hf.co/eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M

Ollama
How to use eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF with Ollama:
```
ollama run hf.co/eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF with Docker Model Runner:
```
docker model run hf.co/eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M
```

Lemonade

How to use eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eaddario/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Small-3.2-24B-Instruct-2506-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Mistral-Small-3.2-24B-Instruct-2506-GGUF / Mistral-Small-3.2-24B-Instruct-2506-Q5_K_S.gguf

eaddario

Layer-wise quantization Q5_K_S

83b3d49 verified about 1 year ago

Download

History Blame GGUF Editor new Delete

14.9 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:: b467da1626fc818d260fc06459d17a17ada13ed6c1fd02dce26ae98539885de1
Size of remote file:: 14.9 GB
SHA256:: 7d4c80b7a9b549a7d962612be6b5f956e4d38e30c47d6ce8e997a2b008dc0abc

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