Needle
A 26m parameter "Simple Attention Network" for function calling 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 devices (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 playgroundOpens 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","description":"Get current weather for a city.","parameters":{"location":{"type":"string","description":"City name.","required":true}}}]',
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.jsonlData format
Each line in the JSONL file has three fields: query, tools, and answers.
Tool schema:
{
"name": "get_weather",
"description": "Get current weather for a city.",
"parameters": {
"location": { "type": "string", "description": "City name.", "required": true }
}
}Answer schema:
{ "name": "get_weather", "arguments": { "location": "Paris" } }Full JSONL example (each line is one training example, tools and answers are JSON-encoded strings):
{"query": "What's the weather in Paris?", "tools": "[{\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"location\":{\"type\":\"string\",\"description\":\"City name.\",\"required\":true}}}]", "answers": "[{\"name\":\"get_weather\",\"arguments\":{\"location\":\"Paris\"}}]"}
{"query": "Turn off the lights", "tools": "[{\"name\":\"get_weather\",\"description\":\"Get current weather for a city.\",\"parameters\":{\"location\":{\"type\":\"string\",\"description\":\"City name.\",\"required\":true}}},{\"name\":\"toggle_lights\",\"description\":\"Toggle smart lights on or off.\",\"parameters\":{\"state\":{\"type\":\"string\",\"description\":\"on or off.\",\"required\":true}}}]", "answers": "[{\"name\":\"toggle_lights\",\"arguments\":{\"state\":\"off\"}}]"}Provide at least 120 examples per tool (100 train / 10 val / 10 test). Fewer examples will overfit — you'll see perfect training metrics but the model won't generalize. Vary query phrasing and include examples with multiple tools available.
Using a finetuned model
Finetuning saves the best checkpoint as checkpoints/needle_finetuned_<id>_best.pkl:
needle run --checkpoint checkpoints/needle_finetuned_*_best.pkl \
--query "What's the weather?" --tools '[{"name":"get_weather","description":"Get current weather for a city.","parameters":{"location":{"type":"string","description":"City name.","required":true}}}]'params, config = load_checkpoint("checkpoints/needle_finetuned_<id>_best.pkl")
model = SimpleAttentionNetwork(config)
result = generate(model, params, get_tokenizer(), query="...", tools='[...]', stream=False)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}
}
