Instructions to use sinatras/bonsai-8b-split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use sinatras/bonsai-8b-split with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sinatras/bonsai-8b-split", filename="Q2_K/Bonsai-8B-Q2_K-00001-of-00002.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-8b-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-8b-split:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sinatras/bonsai-8b-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-8b-split:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sinatras/bonsai-8b-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-8b-split:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sinatras/bonsai-8b-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-8b-split:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sinatras/bonsai-8b-split:Q4_K_M
Use Docker
docker model run hf.co/sinatras/bonsai-8b-split:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use sinatras/bonsai-8b-split with Ollama:
ollama run hf.co/sinatras/bonsai-8b-split:Q4_K_M
- Unsloth Studio new
How to use sinatras/bonsai-8b-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-8b-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-8b-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-8b-split to start chatting
- Docker Model Runner
How to use sinatras/bonsai-8b-split with Docker Model Runner:
docker model run hf.co/sinatras/bonsai-8b-split:Q4_K_M
- Lemonade
How to use sinatras/bonsai-8b-split with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sinatras/bonsai-8b-split:Q4_K_M
Run and chat with the model
lemonade run user.bonsai-8b-split-Q4_K_M
List all available models
lemonade list
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license: apache-2.0
base_model: prism-ml/Bonsai-8B-unpacked
tags:
- gguf
- wllama
- browser
---
# bonsai-8b-split
Bonsai 8B split 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-8B-unpacked](https://huggingface.co/prism-ml/Bonsai-8B-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.
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