cactus-compute/needle

Needle 2

Needle 2 is an open 45M-parameter model for tool calling, device use and structured extraction. The whole model is a single 14MB binary that runs a full session in about 28MB of RAM. It is built on our Simple Attention Network findings, compressed to CQ2-bit with Cactus Quants, and baked into its own engine. On the benchmarks below, Needle 2 trades wins with other small models like FunctionGemma 270M, LFM2.5 230M and Apple FM, at 5x to 70x smaller, and 2 bits against their f16.

This repository is the Python package: inference, LoRA fine-tuning, and export. pip install cactus-needle, describe your tools, and call them from Python. The inference engine is fetched once from Hugging Face and cached; there is nothing else to build.

• Self-contained: weights baked into a single 14MB engine; no separate model files to manage, and inference does no network.
• Simple contract: tool calls come back as structured data, text in, JSON out; a byte-level grammar compiled from your schemas constrains every token.
• Confidence-gated: every response carries a calibrated confidence score from a learned head; set a threshold, act above it, escalate below it.
• Tool retrieval: declare a large catalogue and a built-in retrieval head renders only the top five tools per turn, with the grammar constrained to that subset.
• Bounded memory: a 256-token sliding window with the tools pinned as KV sinks, so total memory stays near 28MB no matter how long the conversation runs.

Weights: huggingface.co/Cactus-Compute/needle2 · source: github.com/cactus-compute/needle.

Simple Attention Network

Needle 2 is a Simple Attention Network, our dense small-model recipe: a Hadamard MLP in place of the FFN, GQA attention, engram key-value memory, and multi-lane hyper-connections. See the paper for the design and ablations: arXiv:2607.18363.

Each block carries its update rule. Here x̂ is the RMS-normalised flattening of the four residual streams, H the orthonormal Walsh-Hadamard transform (a fixed matrix, applied in n log n time with no weights to read), (kₜ, vₜ) rows gathered from hashed n-gram tables, and P the doubly-stochastic normalisation of the routing logits A, computed by Sinkhorn iteration; a, b, g and all σ-gates are learned and input-dependent. Both attention and MLP residuals are sandwich-normed and gated, the engram sites fire at two layers, and decoding is constrained by a byte-level grammar compiled from the declared schemas.

Quickstart

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pip install cactus-needle

Needle reads your tool descriptions to decide what to call and how to fill arguments, so describing them well is the whole game.

Simple: decorate a function. The signature gives the argument types, the docstring is the tool description, and run() completes the loop: model picks the call, Needle executes your function, feeds the result back, and returns the final response with the executed tool results attached as results.

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import needle

@needle.tool
def get_weather(city: str):
    "Get the current weather for a city."
    return {"city": city, "temp_c": 27, "sky": "clear"}

agent = needle.Needle(tools=[get_weather])
print(agent.run("what's it like in Lagos right now?")["results"])
# [{'city': 'Lagos', 'temp_c': 27, 'sky': 'clear'}]

Extraction: to pull structured data out of text, declare the shape and call extract(). Pass a Pydantic model and you get a typed object back.

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from pydantic import BaseModel

class Invoice(BaseModel):
    vendor: str
    total: float
    due_date: str

invoice = needle.extract("Invoice from Acme Corp, $1,200.00, due 2026-09-01", Invoice)
print(invoice.vendor, invoice.total)   # -> Acme Corp 1200.0

Per argument descriptions and choices, value constraints compiled into the decode grammar, raw JSON schemas, driving the loop with complete(), the response contract, system facts, tool retrieval, and confidence gating are all covered in doc/apis.md.

Playground

Try any model in the browser: pick a preset, edit the tools or prompt, and Run. Follow-up queries continue the same conversation.

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needle playground                      # base model, http://127.0.0.1:7860
needle playground --weights my.cact    # a tuned model

The server downloads and initializes the model before serving, so the first query is instant. The Finetune on these tools button runs the fine-tuning pipeline below from the UI and hands back a downloadable .cact.

Fine-tuning

Needle fine-tunes with LoRA on the frozen base and merges the adapter at export, so a run is cheap and the tuned model is still a single .cact that runs on the same engine. The workflow is: (optionally) synthesize data, LoRA fine-tune, then build a tuned .cact. See doc/finetuning.md for dataset sizing, reading the loss curve, and troubleshooting.

Data format. A JSONL file, one example per line. reasoning is optional; an off-topic example has answers: [].

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{"query": "dim the kitchen to 10", "tools": [{"name": "set_lights", "parameters": {"type": "object", "properties": {"room": {"type": "string"}, "brightness": {"type": "integer"}}, "required": ["room"]}}], "answers": [{"name": "set_lights", "arguments": {"room": "kitchen", "brightness": 10}}], "reasoning": "'kitchen' -> room; 'dim to 10' -> brightness 10"}

1. Synthesize data (optional). Needs OPENROUTER_API_KEY. Seed from a tool schema file, or expand an existing set:

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export OPENROUTER_API_KEY=sk-or-...
needle generate-data --tools my_tools.json --num-samples 500 --output data.jsonl
needle generate-data --augment data.jsonl --num-samples 500      # expand an existing JSONL

Set OPENROUTER_URL to use an OpenAI-compatible gateway instead of the default OpenRouter endpoint.

2. LoRA fine-tune. The base checkpoint auto-downloads from Hugging Face if you do not pass --checkpoint. --generate N first synthesizes N more examples from the tools in your data (also needs OPENROUTER_API_KEY).

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needle finetune data.jsonl --epochs 10
needle finetune data.jsonl --epochs 10 --generate 300 --lora-rank 16 --lora-alpha 32

Key options: --epochs (default 3), --lora-rank (16), --lora-alpha (32), --lr (1e-4), --batch-size (16), --max-len (1024), --val-split (0.1), --checkpoint <base.pkl>, --out <adapter.pkl>. The adapter is written to checkpoints/needle_lora.pkl. A validation loss prints each epoch from the held out split.

Training is plain JAX and runs on any accelerator jax supports. On an NVIDIA machine install the CUDA build and the same command trains on the GPU:

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pip install "cactus-needle[gpu]"

On Apple Silicon the metal extra trains on the GPU:

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pip install "cactus-needle[metal]"

3. Build a tuned .cact. Merge the adapter into the base and quantize. The base auto-downloads if absent.

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needle build checkpoints/needle2.pkl --lora checkpoints/needle_lora.pkl --out my_needle.cact

Add --bits 2 (default 4) for a smaller model, or set NEEDLE_HF_REPO=<you>/<model> and pass --upload to publish the .cact.

4. Run it. The engine is weights-agnostic, so a tuned .cact runs on it directly - no recompilation:

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import needle
agent = needle.Needle(weights="my_needle.cact", tools=[...])
agent.run("...")

Citation

Needle 2 is built by the Cactus Compute team. If you use it in your work, please cite:

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@misc{needle2_2026,
  title        = {Needle 2: A 45M-Parameter Foundation Tool-Calling Model for Tiny Devices},
  author       = {Ndubuaku, Henry and Mosoyan, Karen and Mroz, Jakub and Cylich, Noah and
                  Kumar, Satyajit and Sandhu, Parkirat and Shemet, Roman and Lee, Justin H.},
  year         = {2026},
  organization = {Cactus Compute, Inc.},
  howpublished = {\url{https://github.com/cactus-compute/needle}}
}

Reach out on founders@cactuscompute.com for partnerships, collaborations, synergies and deploying Needle2 in your product.