Instructions to use benjamin-paine/taproot-common with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use benjamin-paine/taproot-common with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="benjamin-paine/taproot-common", filename="image-encoding-clip-llava-mmproj-v1-5-13b.fp16.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 benjamin-paine/taproot-common with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf benjamin-paine/taproot-common:F16 # Run inference directly in the terminal: llama-cli -hf benjamin-paine/taproot-common:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf benjamin-paine/taproot-common:F16 # Run inference directly in the terminal: llama-cli -hf benjamin-paine/taproot-common:F16
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 benjamin-paine/taproot-common:F16 # Run inference directly in the terminal: ./llama-cli -hf benjamin-paine/taproot-common:F16
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 benjamin-paine/taproot-common:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf benjamin-paine/taproot-common:F16
Use Docker
docker model run hf.co/benjamin-paine/taproot-common:F16
- LM Studio
- Jan
- Ollama
How to use benjamin-paine/taproot-common with Ollama:
ollama run hf.co/benjamin-paine/taproot-common:F16
- Unsloth Studio new
How to use benjamin-paine/taproot-common 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 benjamin-paine/taproot-common 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 benjamin-paine/taproot-common to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for benjamin-paine/taproot-common to start chatting
- Docker Model Runner
How to use benjamin-paine/taproot-common with Docker Model Runner:
docker model run hf.co/benjamin-paine/taproot-common:F16
- Lemonade
How to use benjamin-paine/taproot-common with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull benjamin-paine/taproot-common:F16
Run and chat with the model
lemonade run user.taproot-common-F16
List all available models
lemonade list
Ctrl+K
- 4.73 kB
- 283 kB
- 396 MB xet
- 456 kB
- 52.7 kB
- 999 kB
- 1.51 GB xet
- 789 MB xet
- 332 MB xet
- 2.41 MB
- 290 MB xet
- 1.62 GB xet
- 3.09 GB xet
- 3.06 GB xet
- 967 MB xet
- 151 MB xet
- 494 kB
- 52.7 kB
- 494 kB
- 52.7 kB
- 1.04 MB
- 2.48 MB
- 836 kB
- 2.48 MB
- 307 MB xet
- 461 Bytes
- 27.2 MB xet
- 54.3 MB xet
- 176 MB xet
- 246 MB xet
- 29.4 MB xet
- 1.43 MB xet
- 645 MB xet
- 624 MB xet
- 700 MB xet
- 910 MB xet
- 86.1 MB xet
- 1.71 GB xet
- 11.9 GB xet
- 11.9 GB xet
- 11.9 GB xet
- 6.7 GB xet
- 23.8 GB xet
- 11.9 GB xet
- 11.9 GB xet
- 6.7 GB xet
- 23.8 GB xet
- 11.9 GB xet
- 11.9 GB xet
- 6.69 GB xet