Bonsai Demo is an application for running Bonsai and Ternary-Bonsai language models locally on computers using supported hardware acceleration or CPUs. It lets users chat with the models, send images and documents, and use tool-calling features through its demo interfaces. The catalogue instruction helps coding agents set up and operate the demo.
Borrowing it
Nothing to install: this file belongs to PrismML-Eng/Bonsai-demo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/PrismML-Eng/Bonsai-demo/main/AGENTS.mdgit clone --depth 1 https://github.com/PrismML-Eng/Bonsai-demoWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/prismml-eng/bonsai-demo/agents-md)<a href="https://agentmods.dev/instructions/prismml-eng/bonsai-demo/agents-md"><img src="https://agentmods.dev/badge/instructions/prismml-eng/bonsai-demo/agents-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04251 | $0.04251 |
| Opus 5 | $0.02125 | $0.02125 |
| Sonnet 5 | $0.00850 | $0.00850 |
| Haiku 4.5 | $0.00425 | $0.00425 |
Grade C, and why
Bonsai-demo AGENTS.md scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s http://localhost:8080/props | python3 -m json.tool | head -30 Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:8080/props | python3 -m json.tool | head -30 How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent guide — tuning the Bonsai demo
For AI agents (and humans) helping someone set up this demo. Goal: pick the right
flags for the user's hardware and use case. The behavior notes below come from real
testing; measure on the user's own hardware before promising performance (the
timings object in every API response has the numbers).
Why the 27B models (what to show off)
The 27B generation is a step change over the earlier 8B/4B/1.7B demos:
- Vision — image input end to end (photos, screenshots) via the mmproj on llama-server.
- Agentic / tool calling — much stronger at tool use; both llama-server (
--jinja) andmlx_lm.serveremit native OpenAItool_calls, verified with full tool round-trips. - Thinking — a reasoning model; thought is streamed separately (
reasoning_content) and can be budgeted (--reasoning-budget N) or picked per chat in the web UI. - Long context — 256k+ tokens per conversation on a 48 GB Mac (hybrid GDN keeps KV cheap).
- Tiny footprint — the binary 27B packs to ~1.125 bits per weight: it fits on a modern iPhone without memory offloading. The ternary build (~1.7 bpw, packed into 2-bit for faster acceleration) is the higher-quality option and the default.
The models
BONSAI_FAMILY / BONSAI_MODEL |
Weights | Notes |
|---|---|---|
ternary / 27B (default) |
GGUF ~6.7-7.1 GB + mmproj 0.9 GB; MLX 2-bit ~7.9 GB | Higher quality. Two GGUF formats since the mainline rebase (prism-b10658+): PQ2_0 (group 128, 6.66 GiB, smallest/fastest where supported: CUDA, Metal, CPU, ROCm) and official Q2_0 group 64 (Ternary-Bonsai-27B-Q2_g64.gguf, 7.05 GiB, adds Vulkan/SYCL); smaller sizes use *-Q2_0_g64.gguf naming. The scripts pick per backend. Legacy *-Q2_0.gguf files (no g64) only load on old prism-v5 releases; new binaries refuse them with an error |
bonsai / 27B |
GGUF Q1_0 ~3.5 GB + mmproj 0.9 GB; MLX 1-bit ~4.8 GB | Smallest and fastest; fits on a modern iPhone without offloading |
8B / 4B / 1.7B (both families) |
smaller | Text-only, no tools wiring, legacy tested flag set |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 190 lines · 4,251 tokens per session scan C 9a90564077cb
Bonsai-demo AGENTS.md is an instructions file published in the GitHub repository PrismML-Eng/Bonsai-demo (2,279 stars, last pushed 5d ago), licensed Apache-2.0. It adds 4,251 tokens to every session, about $0.0213 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.