Instructions to use Open4bits/Ministral-3-3B-Base-2512-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Open4bits/Ministral-3-3B-Base-2512-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Open4bits/Ministral-3-3B-Base-2512-gguf",
	filename="ministral-3-3b-base-2512-q4_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Open4bits/Ministral-3-3B-Base-2512-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Open4bits/Ministral-3-3B-Base-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 Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Open4bits/Ministral-3-3B-Base-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 Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M

Use Docker

docker model run hf.co/Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use Open4bits/Ministral-3-3B-Base-2512-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Install mistral-common:
pip install --upgrade mistral-common
# Start the vLLM server:
vllm serve "Open4bits/Ministral-3-3B-Base-2512-gguf" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Open4bits/Ministral-3-3B-Base-2512-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M

Ollama
How to use Open4bits/Ministral-3-3B-Base-2512-gguf with Ollama:
```
ollama run hf.co/Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M
```

Unsloth Studio

How to use Open4bits/Ministral-3-3B-Base-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 Open4bits/Ministral-3-3B-Base-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 Open4bits/Ministral-3-3B-Base-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 Open4bits/Ministral-3-3B-Base-2512-gguf to start chatting

Docker Model Runner
How to use Open4bits/Ministral-3-3B-Base-2512-gguf with Docker Model Runner:
```
docker model run hf.co/Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M
```

Lemonade

How to use Open4bits/Ministral-3-3B-Base-2512-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Open4bits/Ministral-3-3B-Base-2512-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Ministral-3-3B-Base-2512-gguf-Q4_K_M

List all available models

lemonade list

fmasterpro27 commited on Feb 6

Commit

d3d219e

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1 Parent(s): 0c9a5f6

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@@ -24,7 +24,7 @@ pipeline_tag: image-text-to-text
 This repository provides the **Ministral 3 3B Base model converted to GGUF format**, published by Open4bits to enable efficient local inference with reduced memory usage and broad CPU compatibility.
-The underlying Ministral 3 model and architecture are **developed and owned by [Insert Original Organization/Developer]**. This repository contains only a quantized GGUF conversion of the original model weights.
 The model is designed for lightweight, high-performance text generation and instruction-following tasks, making it well suited for local and resource-constrained environments.

 This repository provides the **Ministral 3 3B Base model converted to GGUF format**, published by Open4bits to enable efficient local inference with reduced memory usage and broad CPU compatibility.
+The underlying Ministral 3 model and architecture are **developed and owned by Ministral**. This repository contains only a quantized GGUF conversion of the original model weights.
 The model is designed for lightweight, high-performance text generation and instruction-following tasks, making it well suited for local and resource-constrained environments.