Instructions to use matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-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 matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-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 matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-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 matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-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 matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M
Use Docker
docker model run hf.co/matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-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": "matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M
- Ollama
How to use matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF with Ollama:
ollama run hf.co/matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF with Docker Model Runner:
docker model run hf.co/matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M
- Lemonade
How to use matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull matrixdose/Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
HERETIC – Mistral-Nemo 2407 12B Thinking (GGUF)
HERETIC is a reasoning-oriented variant of the Mistral-Nemo 2407 12B architecture distributed in GGUF format for efficient local inference.
The model is intended for users who want a flexible conversational assistant capable of analytical reasoning, long-form explanations, and open-ended dialogue while running entirely on local hardware.
This repository provides quantized versions optimized for llama.cpp–based runtimes and other compatible inference tools.
Model Details
Model Name: Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC
Architecture: Mistral-Nemo (12B parameters)
Format: GGUF
Base Model: Mistral-Nemo-2407
Distribution: Quantized builds for local inference
Primary Capability: Instruction-following with extended reasoning and conversational flexibility
HERETIC focuses on encouraging multi-step reasoning and detailed responses while maintaining a natural conversational style.
Intended Use
This model is designed primarily for local deployments and experimentation.
Typical use cases include:
- Personal AI assistants
- Coding help and technical explanations
- Analytical reasoning tasks
- Brainstorming and creative writing
- Prompt engineering and LLM experimentation
- Offline or privacy-focused AI workflows
Out-of-Scope Use
The model should not be relied upon for:
- Legal advice
- Medical advice
- Safety-critical decision making
- Automated moderation systems
Outputs may contain inaccuracies or biased information.
Prompt Format
The model works best with structured role-based prompts.
Example conversation template:
<|system|>
You are a helpful AI assistant.
<|user|>
Explain how neural networks learn.
<|assistant|>
Some interfaces automatically apply a compatible chat template.
Running the Model
This model uses the GGUF format, making it compatible with several local inference tools.
llama.cpp
Example command:
./llama.exe -m Mistral-Nemo-2407-12B-Thinking-Claude-Gemini-GPT5.2-Uncensored-HERETIC_Q4_K_M.gguf -p "Explain quantum computing in simple terms."
Limitations
Like most large language models:
- The model can generate incorrect information.
- It may hallucinate facts or citations.
- Output quality depends heavily on prompt design.
- Responses reflect biases present in training data.
Users should critically evaluate outputs before relying on them.
Acknowledgements
This model builds on contributions from several open-source projects:
- The Mistral research team for the underlying architecture
- The llama.cpp ecosystem enabling efficient local inference
- The GGUF format used for optimized model distribution
- The open-source community that develops tools for local LLM deployment
Disclaimer
This model is provided for research, experimentation, and local use. Users are responsible for ensuring that deployments comply with applicable laws and the licensing terms of the underlying base model.
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