Instructions to use coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF",
	filename="Ministral-3-14B-Reasoning-2512-heretic-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf coder3101/Ministral-3-14B-Reasoning-2512-heretic-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 coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf coder3101/Ministral-3-14B-Reasoning-2512-heretic-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 coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf coder3101/Ministral-3-14B-Reasoning-2512-heretic-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 coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF with Ollama:
```
ollama run hf.co/coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M
```

Unsloth Studio new

How to use coder3101/Ministral-3-14B-Reasoning-2512-heretic-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 coder3101/Ministral-3-14B-Reasoning-2512-heretic-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 coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF to start chatting

Pi new

How to use coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf coder3101/Ministral-3-14B-Reasoning-2512-heretic-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 coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M
```

Lemonade

How to use coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull coder3101/Ministral-3-14B-Reasoning-2512-heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ministral-3-14B-Reasoning-2512-heretic-GGUF-Q4_K_M

List all available models

lemonade list

Ministral-3-14B-Reasoning-2512-heretic-GGUF

GGUF quantizations of coder3101/Ministral-3-14B-Reasoning-2512-heretic for use with llama.cpp and compatible tools.

Model Description

This is a fine-tuned version of Mistral's Ministral-3-14B-Reasoning-2512 vision-language model. It supports:

Text generation with reasoning capabilities (uses [THINK] tokens)
Vision/Image understanding (requires the mmproj file)
Tool/Function calling

Available Quantizations

Quantization	Size	Description
BF16	26 GB	Full precision (bfloat16)
Q8_0	14 GB	8-bit quantization
Q5_K_M	9.0 GB	5-bit K-quant (medium)
Q4_K_M	7.7 GB	4-bit K-quant (medium) - Recommended

Vision Support

For vision/image understanding, you need to download the mmproj (multimodal projector) file:

Ministral-3-14B-Reasoning-2512-heretic-mmproj-bf16.gguf (847 MB)

Chat Template

The model includes a custom chat template with reasoning support. The format uses:

[SYSTEM_PROMPT]...[/SYSTEM_PROMPT] - System message
[INST]...[/INST] - User messages
[THINK]...[/THINK] - Model's reasoning/thinking process
[IMG] - Image placeholder for vision inputs
[TOOL_CALLS] and [TOOL_RESULTS] - For function calling

Example conversation:

[SYSTEM_PROMPT]You are a helpful assistant.[/SYSTEM_PROMPT][INST]What is 2+2?[/INST][THINK]The user is asking for a simple arithmetic calculation. 2+2=4.[/THINK]The answer is 4.

Usage

Text-only (CLI)

llama-cli -m Ministral-3-14B-Reasoning-2512-heretic-Q4_K_M.gguf \
  -p "[INST]What is the capital of France?[/INST]" \
  -n 256

With Vision Support

llama-mtmd-cli \
  -m Ministral-3-14B-Reasoning-2512-heretic-Q4_K_M.gguf \
  --mmproj Ministral-3-14B-Reasoning-2512-heretic-mmproj-bf16.gguf \
  -p "Describe this image in detail." \
  --image /path/to/image.jpg

With llama-server (OpenAI-compatible API)

llama-server \
  -m Ministral-3-14B-Reasoning-2512-heretic-Q4_K_M.gguf \
  --mmproj Ministral-3-14B-Reasoning-2512-heretic-mmproj-bf16.gguf \
  --port 8080

Then query the API:

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "ministral", "messages": [{"role": "user", "content": "What is 2+2?"}]}'