Instructions to use Hacht/CapCapResource with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Hacht/CapCapResource with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Hacht/CapCapResource", filename="gemma-4-E4B-it-Q4_K_M.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 Hacht/CapCapResource with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Hacht/CapCapResource:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Hacht/CapCapResource:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Hacht/CapCapResource:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Hacht/CapCapResource: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 Hacht/CapCapResource:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Hacht/CapCapResource: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 Hacht/CapCapResource:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Hacht/CapCapResource:Q4_K_M
Use Docker
docker model run hf.co/Hacht/CapCapResource:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Hacht/CapCapResource with Ollama:
ollama run hf.co/Hacht/CapCapResource:Q4_K_M
- Unsloth Studio new
How to use Hacht/CapCapResource 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 Hacht/CapCapResource 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 Hacht/CapCapResource to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Hacht/CapCapResource to start chatting
- Pi new
How to use Hacht/CapCapResource with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Hacht/CapCapResource: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": "Hacht/CapCapResource:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Hacht/CapCapResource with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Hacht/CapCapResource: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 Hacht/CapCapResource:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Hacht/CapCapResource with Docker Model Runner:
docker model run hf.co/Hacht/CapCapResource:Q4_K_M
- Lemonade
How to use Hacht/CapCapResource with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Hacht/CapCapResource:Q4_K_M
Run and chat with the model
lemonade run user.CapCapResource-Q4_K_M
List all available models
lemonade list
| [ | |
| { | |
| "name": "Ban Mai", | |
| "gender": "female", | |
| "audio_path": "piper/banmai.onnx", | |
| "description": "Giọng nữ trẻ trung, thanh thoát" | |
| }, | |
| { | |
| "name": "Cúc", | |
| "gender": "female", | |
| "audio_path": "piper/cuc.onnx", | |
| "description": "Giọng nữ nhẹ nhàng, dễ thương" | |
| }, | |
| { | |
| "name": "Tài An", | |
| "gender": "male", | |
| "audio_path": "piper/taian2.onnx", | |
| "description": "Giọng nam trầm ấm, truyền cảm hứng" | |
| }, | |
| { | |
| "name": "Phương Trang", | |
| "gender": "female", | |
| "audio_path": "piper/phuongtrang.onnx", | |
| "description": "Giọng nữ chuẩn, rõ ràng" | |
| }, | |
| { | |
| "name": "Ngọc Huyền (New)", | |
| "gender": "female", | |
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| "description": "Giọng nữ trong sáng, tự nhiên" | |
| }, | |
| { | |
| "name": "Ngọc Huyền", | |
| "gender": "female", | |
| "audio_path": "piper/ngochuyen.onnx", | |
| "description": "Giọng nữ nhẹ nhàng, ấm áp" | |
| }, | |
| { | |
| "name": "Minh Quang", | |
| "gender": "male", | |
| "audio_path": "piper/minhquang.onnx", | |
| "description": "Giọng nam trầm, mạnh mẽ" | |
| }, | |
| { | |
| "name": "Minh Khang", | |
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| "description": "Giọng nam trẻ, năng động" | |
| }, | |
| { | |
| "name": "Mạng Dũng", | |
| "gender": "male", | |
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| "description": "Giọng nam trầm ấm, rõ ràng" | |
| }, | |
| { | |
| "name": "Mai Phương", | |
| "gender": "female", | |
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| "description": "Giọng nữ thanh thoát, nhẹ nhàng" | |
| }, | |
| { | |
| "name": "Lạc Phi", | |
| "gender": "male", | |
| "audio_path": "piper/lacphi.onnx", | |
| "description": "Giọng nam" | |
| }, | |
| { | |
| "name": "Duy", | |
| "gender": "male", | |
| "audio_path": "piper/duyoryx3175.onnx", | |
| "description": "Giọng nam" | |
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| { | |
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| }, | |
| { | |
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| "audio_path": "piper/vi_VN-vais1000-medium.onnx", | |
| "description": "Giọng nữ nhẹ nhàng, ấm áp" | |
| } | |
| ] |