Text Generation
GGUF
English
llama.cpp
mesh-llm
PRIME
distributed
INTELLECT-3
imatrix
conversational
Instructions to use exdysa/INTELLECT-3-Q4_K_M-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use exdysa/INTELLECT-3-Q4_K_M-layers with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="exdysa/INTELLECT-3-Q4_K_M-layers", filename="layers/layer-000.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use exdysa/INTELLECT-3-Q4_K_M-layers with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf exdysa/INTELLECT-3-Q4_K_M-layers # Run inference directly in the terminal: llama-cli -hf exdysa/INTELLECT-3-Q4_K_M-layers
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf exdysa/INTELLECT-3-Q4_K_M-layers # Run inference directly in the terminal: llama-cli -hf exdysa/INTELLECT-3-Q4_K_M-layers
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 exdysa/INTELLECT-3-Q4_K_M-layers # Run inference directly in the terminal: ./llama-cli -hf exdysa/INTELLECT-3-Q4_K_M-layers
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 exdysa/INTELLECT-3-Q4_K_M-layers # Run inference directly in the terminal: ./build/bin/llama-cli -hf exdysa/INTELLECT-3-Q4_K_M-layers
Use Docker
docker model run hf.co/exdysa/INTELLECT-3-Q4_K_M-layers
- LM Studio
- Jan
- vLLM
How to use exdysa/INTELLECT-3-Q4_K_M-layers with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "exdysa/INTELLECT-3-Q4_K_M-layers" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "exdysa/INTELLECT-3-Q4_K_M-layers", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/exdysa/INTELLECT-3-Q4_K_M-layers
- Ollama
How to use exdysa/INTELLECT-3-Q4_K_M-layers with Ollama:
ollama run hf.co/exdysa/INTELLECT-3-Q4_K_M-layers
- Unsloth Studio
How to use exdysa/INTELLECT-3-Q4_K_M-layers 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 exdysa/INTELLECT-3-Q4_K_M-layers 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 exdysa/INTELLECT-3-Q4_K_M-layers to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for exdysa/INTELLECT-3-Q4_K_M-layers to start chatting
- Pi
How to use exdysa/INTELLECT-3-Q4_K_M-layers with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf exdysa/INTELLECT-3-Q4_K_M-layers
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": "exdysa/INTELLECT-3-Q4_K_M-layers" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use exdysa/INTELLECT-3-Q4_K_M-layers with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf exdysa/INTELLECT-3-Q4_K_M-layers
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 exdysa/INTELLECT-3-Q4_K_M-layers
Run Hermes
hermes
- Docker Model Runner
How to use exdysa/INTELLECT-3-Q4_K_M-layers with Docker Model Runner:
docker model run hf.co/exdysa/INTELLECT-3-Q4_K_M-layers
- Lemonade
How to use exdysa/INTELLECT-3-Q4_K_M-layers with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull exdysa/INTELLECT-3-Q4_K_M-layers
Run and chat with the model
lemonade run user.INTELLECT-3-Q4_K_M-layers-{{QUANT_TAG}}List all available models
lemonade list
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