eulogik/pico-type-v02

pico-type 🔍 (v02)

A tiny byte-level multi-head content classifier — ~1.5M params, ~9MB single-file ONNX (FP32), ~18ms CPU inference.

Classifies any content from raw bytes: coarse type · modality · subtype · code language · text language · file MIME · risk flags

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Paper: pico-type: A 1.5M-Parameter Byte-Level Multi-Head Content Classifier (arXiv:2608.14658, 2026)

What's here

Single-file IR-8 ONNX exports (FP32, opset 18) for all 4 Matryoshka tiers — tiny / small / base / pro — sharing the same trunk:

Tier	Dim	Size	Params
tiny	16	9.09 MB	1,434,344
small	64	9.13 MB	1,445,480
base	192	9.25 MB	1,475,176
pro	576	9.61 MB	1,564,264

Load directly with onnxruntime:

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("picotype_base.onnx")

text = b"def hello(): pass"
ids = np.frombuffer(text[:1024], dtype=np.uint8).astype(np.int64)
padded = np.zeros(1024, dtype=np.int64)
padded[:len(ids)] = ids
mask = np.zeros(1024, dtype=np.bool_)
mask[:len(ids)] = True

outs = session.run(None, {"input_ids": padded[None, :], "attention_mask": mask[None, :]})

Evaluation (v2 — trained on real data)

Head	Classes	Accuracy	Dataset
coarse	12	100%	Synthetic eval
modality	8	100%	Synthetic eval
subtype	24	93.8%	Synthetic eval
code_lang	62	60.3%	The Heap — 24 real-world langs
text_lang	30	98.3%	Wikipedia — 30 langs
file_mime	90	100%	Synthetic eval
risk (multi-label)	6	100%	Synthetic eval

Links

• arXiv paper
• GitHub
• Main HF model repo
• HF Space (live demo)
• PyPI: pip install pico-type