---
license: mit
tags:
- onnx
- function-calling
- needle
- cactus
- browser
- sentencepiece
base_model: Cactus-Compute/needle
library_name: onnxruntime
---

# Needle — ONNX export for in-browser inference

Browser-ready ONNX export of [Cactus-Compute/needle](https://huggingface.co/Cactus-Compute/needle), a 26M-parameter function-calling model. Designed to run entirely client-side via `onnxruntime-web` (WASM backend) — no server required.

## Files

| File | Description | Size |
|---|---|---|
| `encoder.onnx` | Needle encoder. Input `input_ids:(B,T)`, output `encoder_out:(B,T,512)`. Single-pass. | ~55 MB |
| `decoder_step.onnx` | One decoder step with explicit past-KV in / present-KV out. Run in a JS loop. | ~85 MB |
| `needle.model` | SentencePiece BPE protobuf (vocab=8192, `byte_fallback=True`, `identity` normalization). Loadable by `sentencepiece-js` / `@huggingface/transformers`. | 125 KB |
| `tokenizer-specials.json` | `{"pad":0,"eos":1,"bos":2,"tool_call":4,"tools":5}` | tiny |

## Origin

The upstream Cactus Needle is implemented in **JAX/Flax**, not PyTorch — `torch.onnx.export` cannot run against the upstream model directly. This ONNX export was produced via a "port-and-copy" pipeline:

1. Reimplemented the Simple Attention Network in PyTorch (parametric on `TransformerConfig`)
2. Copied weights tensor-by-tensor from the upstream Flax checkpoint (handling Flax `(in, out)` → PyTorch `(out, in)` transposition for Linear kernels and the `nn.scan` layer-stacking convention)
3. Verified Flax↔PyTorch parity at `<1e-3` max-abs-diff
4. Exported encoder + decoder-step to ONNX via legacy TorchScript-based `torch.onnx.export`
5. Verified PyTorch↔ONNX parity at `<1e-3`
6. Verified end-to-end: Cactus's native `generate()` and a hand-rolled `onnxruntime` KV-cache loop produce **byte-identical** output token sequences

## Parity numbers (against Cactus's native `generate(constrained=False)`)

| Stage | max-abs-diff |
|---|---|
| Flax encoder ↔ PyTorch port | 0.000010 |
| Flax decoder step-0 ↔ PyTorch port | 0.000029 |
| PyTorch encoder ↔ ONNX | 0.000004 |
| PyTorch decoder step ↔ ONNX | 0.000014 (logits) |
| End-to-end token sequence | byte-identical |

Example: `query="set a 5 min timer"` produces `' [{"name":"set_timer","arguments":{"time_human":"5 minutes"}}]'` in both Cactus native and the browser via these artifacts.

## Usage in the browser

Load both `.onnx` files via `onnxruntime-web` (WASM backend), load `needle.model` via `sentencepiece-js`, and run the encoder once + decoder-step in a JS loop with the KV cache passed through.

## Architecture

Per the upstream model card: encoder-decoder "Simple Attention Network", d_model=512, GQA 8/4 heads, 12 encoder layers, 8 decoder layers, no FFN, ZCRMSNorm (`(1+γ)·x/RMS(x)`, γ init zero), RoPE on Q and K.

The decoder is exported as a **single step** with past/present KV as graph I/O — the JS side calls it in a loop, allowing streaming token output and avoiding ONNX symbolic control flow.

## Reproduce / port your own Cactus-trained model

The full pipeline that produced these artifacts is checked in alongside the `.onnx` files (see `PORTING.md` for the step-by-step). The scripts are parametric on the source HF repo, so if you've finetuned Needle (or trained a Simple-Attention-Network variant with the upstream Cactus codebase), you can produce a browser-ready ONNX export with the same recipe:

```bash
# 1. Convert your Cactus checkpoint → PyTorch state_dict
uv run python convert_weights.py --ckpt-repo YOUR_USER/your-finetune --ckpt-file weights.pkl

# 2. Verify the port matches your upstream model bit-for-bit (< 1e-3)
uv run python verify_port_parity.py

# 3. Export to ONNX (reads config back from step 1's saved JSON; no edits needed)
uv run python export_onnx.py

# 4. Verify ONNX matches PyTorch AND matches native Cactus generate() token-for-token
uv run python verify_parity.py --ckpt-repo YOUR_USER/your-finetune --ckpt-file weights.pkl

# 5. Push your ONNX artifacts to HF
uv run python upload_to_hf.py --repo YOUR_USER/your-finetune-onnx
```

The PyTorch port (`needle_torch/`) is **parametric on `TransformerConfig`** — it reads the config straight out of your checkpoint's payload, so dim changes (d_model, layer counts, GQA ratios) are picked up automatically. The same pipeline works for the 26M production Needle, the 1.35M iteration config, and anything in between.

Files included for reproduction:

```
needle_torch/         — PyTorch port of the Simple Attention Network
convert_weights.py    — Flax checkpoint → PyTorch state_dict (parametric on --ckpt-repo)
export_onnx.py        — torch.onnx.export of encoder + decoder-step
verify_port_parity.py — Flax ↔ PyTorch parity check (load-bearing)
verify_parity.py      — PyTorch ↔ ONNX + end-to-end vs native generate()
dump_tokenizer.py     — Copy SentencePiece .model + emit parity goldens for the JS port
upload_to_hf.py       — This script (push artifacts to HF Hub)
inspect_needle.py     — Dump Flax arch / tokenizer / prompt notes (useful when porting a variant)
pyproject.toml        — uv-managed env spec
PORTING.md            — Full step-by-step guide
```

## License

MIT, matching the upstream Cactus Needle license.