Instructions to use BasedAGI/BlackSheep-Llama3.2-3B-i1-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 BasedAGI/BlackSheep-Llama3.2-3B-i1-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 BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf BasedAGI/BlackSheep-Llama3.2-3B-i1-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 BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf BasedAGI/BlackSheep-Llama3.2-3B-i1-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 BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf BasedAGI/BlackSheep-Llama3.2-3B-i1-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 BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF with Ollama:
ollama run hf.co/BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_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": "BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF with Docker Model Runner:
docker model run hf.co/BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M
- Lemonade
How to use BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.BlackSheep-Llama3.2-3B-i1-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf BasedAGI/BlackSheep-Llama3.2-3B-i1-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 BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_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 "BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF:Q4_K_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"
Quantized to i1-GGUF using SpongeQuant, the Oobabooga of LLM quantization.
What is a GGUF?
GGUF is a file format used for running large language models (LLMs) on different types of computers. It supports both regular processors (CPUs) and graphics cards (GPUs), making it easier to run models across a wide range of hardware. Many LLMs require powerful and expensive GPUs, but GGUF improves compatibility and efficiency by optimizing how models are loaded and executed. If a GPU doesnโt have enough memory, GGUF can offload parts of the model to the CPU, allowing it to run even when GPU resources are limited. GGUF is designed to work well with quantized models, which use less memory and run faster, making them ideal for lower-end hardware. However, it can also store full-precision models when needed. Thanks to these optimizations, GGUF allows LLMs to run efficiently on everything from high-end GPUs to laptops and even CPU-only systems.
What is an i1-GGUF?
i1-GGUF is an enhanced type of GGUF model that uses imatrix quantizationโa smarter way of reducing model size while preserving key details. Instead of shrinking everything equally, it analyzes the importance of different model components and keeps the most crucial parts more accurate. Like standard GGUF, i1-GGUF allows LLMs to run on various hardware, including CPUs and lower-end GPUs. However, because it prioritizes important weights, i1-GGUF models deliver better responses than traditional GGUF models while maintaining efficiency.
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Model tree for BasedAGI/BlackSheep-Llama3.2-3B-i1-GGUF
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