Back to directory

Bonsai Demo

A local demo application for running the Bonsai 2 27B language model, a heavily compressed reasoning model with vision and tool-calling support, on a laptop or single GPU.

Bonsai Demo

Bonsai Demo runs the Bonsai 2 27B language model locally on a Mac using Metal, on Linux or Windows using CUDA, Vulkan or ROCm, or on CPU. Bonsai 2 27B packs full 27-billion-parameter-class reasoning into ternary weights at 5.9GB, reported to retain most of a full-precision model's intelligence at roughly a ninth of the size, with math performance close to full precision and coding at the same baseline level.

It supports vision input, so photos, screenshots and PDFs can be sent and asked about, native OpenAI-style tool calling with full round-trips and MCP servers in both of the demo's user interfaces, a 262,000-token context kept practical on-device through a hybrid-attention architecture, and adjustable reasoning effort per chat or per request. It requires this demo's own fork of llama.cpp binaries, which a setup script downloads automatically, since the stock llama.cpp cannot run Bonsai's packed weight format; running the setup script and then a server-start script gives a chat, vision and tools interface at a local address.

It is a Shell-scripted demo released under the Apache 2.0 license.

More in Models

See all 405
  • mini-AGI

    github.com

    A continual learning language model that trains from scratch on a single 8 GB VRAM GPU, assembling its own architecture and adapting its size to the hardware and data available rather than shipping as a fixed pretrained model.

  • An open-source contrastive-embedding pipeline that maps technical CVE vulnerability descriptions to formal cybersecurity compliance controls in NIST SP 800-53 and CMMC, published with its model generation, fine-tuning and evaluation code.

  • peekaboolean

    github.com

    A small vision-language model, fine-tuned with LoRA on a 500-million-parameter backbone, that answers typed multiple-choice, ordinal-score and yes/no questions about an image with calibrated probabilities, in about 400 milliseconds on a laptop.