Instructions to use GeekedOutAi/Geeked-Out-Quantization-Software 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 GeekedOutAi/Geeked-Out-Quantization-Software 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 GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M # Run inference directly in the terminal: llama cli -hf GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M # Run inference directly in the terminal: llama cli -hf GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M # Run inference directly in the terminal: ./build/bin/llama cli -hf GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
Use Docker
docker model run hf.co/GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use GeekedOutAi/Geeked-Out-Quantization-Software with Ollama:
ollama run hf.co/GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
- Unsloth Desktop
- Pi
How to use GeekedOutAi/Geeked-Out-Quantization-Software with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use GeekedOutAi/Geeked-Out-Quantization-Software with Docker Model Runner:
docker model run hf.co/GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
- Lemonade
How to use GeekedOutAi/Geeked-Out-Quantization-Software with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
Run and chat with the model
lemonade run user.Geeked-Out-Quantization-Software-IQ2_M
List all available models
lemonade list
- Hermes Agent
How to use GeekedOutAi/Geeked-Out-Quantization-Software with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_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 GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use GeekedOutAi/Geeked-Out-Quantization-Software with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "GeekedOutAi/Geeked-Out-Quantization-Software:IQ2_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Commit History
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Dario Angel PonceVerified signatureHere is an illustration of what that quantizer dashboard looks like82735ac -
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LGxNDaRY GaMeRVerified signatureGeeked Out Quantizer methodology: - QUANTIZATION_NOTES.md: Technical specs and method details - GEEKED_OUT_INFO.md: Overview of the quantization environment - CALIBRATION_INFO.txt: Calibration data explanation4e3e307
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LGxNDaRY GaMeRVerified signatureThis repository contains a Qwen3.6 model quantized using IQ2_M 2-bit quantization via the GGUF format. The IQ2_M scheme is part of the Intelligent Quants (IQ) family, designed to deliver extreme low-bit precision while preserving model quality through mixed-precision techniques. **Model Overview:** Qwen3.6 represents a series of large language models featuring advanced architecture optimizations including grouped query attention (GQA), sliding window attention, and high-quality tokenization for efficient training and inference. These models are designed to deliver strong reasoning capabilities across diverse tasks while maintaining computational efficiency through architectural innovations like multi-token prediction and MoE-style routing. **IQ2_M Quantization Process:** IQ2_M is a 2-bit quantization scheme that combines multiple sub-precision levels (2 bits per weight) using intelligent bit allocation strategies. It employs mixed-precision where different weights receive varying bit…6b42c79 -
# IQ2_M-2Bit-Quantization-By-Geeked-Out-Ai
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