cactus-compute/needle
Automation foundation model for tiny devices: 2-bit, 8-29 MB, tool calls, structured extraction and embeddings on phones, wearables, smart homes, robots, cars and microcontrollers.
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README & documentation
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A foundation model for mobiles, wearables, robots, smart home, automotive and microcontrollers. The whole model is a single 8-29 MB binary built on our Simple Attention Network, and we trade general chat capacity to beat models 10x its size on mobile tool calls and match 2-3x bigger models on extraction. Needle 3 is a Laddered Simple Attention Network: a Monarch Hadamard MLP in place of the FFN, GQA attention with causal conv taps, engram n-gram memory read by gather, and multi-lane hyper-connections, trained so that every depth from 2 to 20 layers is a deployable model. Most of its parameters sit in the engram, so the 121M model does the arithmetic of a 50M one. A byte-level grammar compiled from your schemas constrains every token, and every response carries a calibrated confidence score from a learned head. The architecture diagram is on the release page. Tool calling is exact-match accuracy…
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What this repository does
Automation foundation model for tiny devices: 2-bit, 8-29 MB, tool calls, structured extraction and embeddings on phones, wearables, smart homes, robots, cars and microcontrollers.
Repository facts
- Owner
- cactus-compute
- Primary language
- Python
- Stars
- 11,706
- Forks
- 750
- Open issues + pull requests
- 29
- License
- Apache-2.0
- Archived
- No
- Default branch
- main
- Created
- 2026-02-24T04:50:47.000Z
- Last push
- 2026-09-19T21:48:45.000Z
Topics and intended use
Owner-supplied topics: cactus, edge-ai, foundation-model, function-calling, llm, on-device-ai, tinyml, tool-calling
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Source and freshness
Source: GitHub. Metadata observed 2026-09-20T06:35:42.692Z. Daily imports are snapshots, not real-time monitoring.
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