Instructions to use sinatras/bonsai-1.7b-split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use sinatras/bonsai-1.7b-split with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sinatras/bonsai-1.7b-split", filename="Q2_K/Bonsai-1.7B-Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use sinatras/bonsai-1.7b-split with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sinatras/bonsai-1.7b-split:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sinatras/bonsai-1.7b-split:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sinatras/bonsai-1.7b-split:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sinatras/bonsai-1.7b-split: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 sinatras/bonsai-1.7b-split:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sinatras/bonsai-1.7b-split: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 sinatras/bonsai-1.7b-split:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sinatras/bonsai-1.7b-split:Q4_K_M
Use Docker
docker model run hf.co/sinatras/bonsai-1.7b-split:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use sinatras/bonsai-1.7b-split with Ollama:
ollama run hf.co/sinatras/bonsai-1.7b-split:Q4_K_M
- Unsloth Studio new
How to use sinatras/bonsai-1.7b-split 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 sinatras/bonsai-1.7b-split 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 sinatras/bonsai-1.7b-split to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sinatras/bonsai-1.7b-split to start chatting
- Docker Model Runner
How to use sinatras/bonsai-1.7b-split with Docker Model Runner:
docker model run hf.co/sinatras/bonsai-1.7b-split:Q4_K_M
- Lemonade
How to use sinatras/bonsai-1.7b-split with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sinatras/bonsai-1.7b-split:Q4_K_M
Run and chat with the model
lemonade run user.bonsai-1.7b-split-Q4_K_M
List all available models
lemonade list
| license: apache-2.0 | |
| base_model: prism-ml/Bonsai-1.7B-unpacked | |
| tags: | |
| - gguf | |
| - wllama | |
| - browser | |
| # bonsai-1.7b-split | |
| Bonsai 1.7B GGUF artifacts converted for the playground wllama preset. | |
| These files are the GGUF artifacts used by the local Transformers.js playground | |
| wllama CPU presets. Large files are kept under quantization subdirectories so | |
| browser clients can request the first shard URL and expand the remaining shards. | |
| ## Source And License | |
| - Source model/artifact: [prism-ml/Bonsai-1.7B-unpacked](https://huggingface.co/prism-ml/Bonsai-1.7B-unpacked) | |
| - License: Apache-2.0, inherited from the source model/artifact. | |
| The GGUF conversion, quantization, and splitting steps do not change the | |
| upstream model license. | |