GitHub - cactus-compute/needle: 26m function call model that runs on incredibly small devices

We distilled Gemini 3.1 into a 26m parameter "Simple Attention Network" that you can even finetune locally on your Mac/PC. In production, Needle runs on Cactus at 6000 toks/sec prefill and 1200 decode speed. Weights are fully open on Cactus-Compute/needle, as well as the dataset generation. d=512, 8H/4KV, BPE=8192 ┌──────────────┐ │ Tool Call │ └──────┬───────┘ ┌┴──────────┐ │ Softmax │ └─────┬─────┘ ┌─────┴─────┐ │ Linear (T)│ ← tied └─────┬─────┘ ┌─────┴─────┐ │ ZCRMSNorm │ └─────┬─────┘ ┌────────┴────────┐ │ Decoder x 8 │ │┌───────────────┐│ ││ ZCRMSNorm ││ ││ Masked Self ││ ││ Attn + RoPE ││ ││ Gated Residual││ │├───────────────┤│ ┌──────────────┐ ││ ZCRMSNorm ││ │ Encoder x 12 │──────────────────────▶Cross Attn ││ │ │ ││ Gated Residual││ │ ┌──────────┐ │ │└───────────────┘│ │ │ZCRMSNorm │ │ └────────┬────────┘ │ │Self Attn │ │ ┌─────┴─────┐ │ │ GQA+RoPE │ │ │ Embedding │ ←

shared │ │Gated Res │ │ └─────┬─────┘ │ │ │ │ ┌───────┴───────-┐ │ │ (no FFN) │ │ │[EOS]<tool_call>│ │ └──────────┘ │ │ + answer │ │ │ └───────────────-┘ └──────┬───────┘ │ ┌────┴──────┐ │ Embedding │ └────┬──────┘ │ ┌────┴──────┐ │ Text │ │ query │ └───────────┘

Pretrained on 16 TPU v6e for 200B tokens (27hrs). Post-trained on 2B tokens of single-shot function call dataset (45mins).

Needle is an experimental run for Simple Attention Networks, geared at redefining tiny AI for consumer devies (phones, watches, glasses...). So while it beats FunctionGemma-270m, Qwen-0.6B, Graninte-350m, LFM2.5-350m on single-shot function call for personal AI, Those model are have more scope/capacity and excel in conversational settings. Also, small models can be finicky. Please use the UI in the next section to test on your own tools, and finetune accordingly, at the click of a button. Quickstart git clone https://github.com/cactus-compute/needle.git cd needle && source ./setup needle playground Opens a web UI at http://127.0.0.1:7860 where you can test and finetune on your own tools. Weights are auto-downloaded. Usage (Python) from needle import SimpleAttentionNetwork, load_checkpoint, generate, get_tokenizer

params, config = load_checkpoint("checkpoints/needle.pkl") model = SimpleAttentionNetwork(config) tokenizer = get_tokenizer()

result = generate( model, params, tokenizer, query="What's the weather in San Francisco?",

tools='[{"name":"get_weather","parameters":{"location":"string"}}]', stream=False, ) print(result) # [{"name":"get_weather","arguments":{"location":"San Francisco"}}] Finetuning # Playground (generates data via Gemini, trains, evaluates, bundles result) needle playground

# CLI (auto-downloads weights if not local) needle finetune data.jsonl CLI needle playground Test and finetune via web UI needle finetune <data.jsonl> Finetune on your own data needle run --query "..." --tools Single inference needle train Full training run needle pretrain Pretrain on PleIAs/SYNTH needle eval --checkpoint <path> Evaluate a checkpoint needle tokenize Tokenize dataset needle generate-data Synthesize training data via Gemini needle tpu <action> TPU management (see docs/tpu.md)

@misc{ndubuaku2026needle, title={Needle}, author={Henry Ndubuaku, Jakub Mroz, Karen Mosoyan, Roman Shemet, Parkirat Sandhu, Satyajit Kumar, Noah Cylich, Justin H. Lee}, year={2026}, url={https://github.com/cactus-compute/needle} }