pico-type 0.1.7

A tiny byte-level multi-head content classifier

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license: apache-2.0 language:

• en
• multilingual tags:
• byte-level
• content-classification
• onnx
• edge-ai
• matryoshka
• multi-head
• classifier
• clipboard pipeline_tag: text-classification library_name: pico-type inference: parameters: provider: CPUExecutionProvider

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pico-type 🔍

A tiny byte-level multi-head content classifier — ~1.5M params, ~209KB ONNX, <6ms inference.

Classifies any content into 7 categories from raw bytes in a single forward pass.

Built by eulogik — AI infrastructure for developers.

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✨ Features

• No tokenizer — operates directly on raw UTF-8 bytes (supports all languages, zero pre-processing)
• 7 heads, one forward pass — coarse type, modality, subtype, code lang, text lang, file MIME, risk flags
• 4 Matryoshka tiers — tiny (16d) → small (64d) → base (192d) → pro (576d)
• ~200KB ONNX — deploy on edge devices, serverless functions, browser (WebAssembly)
• <6ms inference on CPU via ONNX Runtime (base tier, 1024 bytes)
• CLI, Gradio Space, MCP server — ready for any integration

📊 Performance

Head	Classes	Synthetic Accuracy	Real-World Accuracy
coarse	12	100%	86%
modality	8	100%	100%
subtype	24	95%	—
code_lang	62	39%	—
text_lang	30	99%	100%
file_mime	90	100%	—
risk (mAP)	6	100%	—

Evaluated on 1000 synthetic samples + 21 hand-curated real-world inputs. Base tier, ~5ms inference.

Note: code_lang synthetic accuracy reflects the challenge of 62-way classification with limited per-class support. Real-world accuracy across all heads is 52% (11/21 correct), up from 23% baseline before diverse training.

🚀 Quick Start

CLI

pip install picotype

echo "def hello():\n    return 42" | picotype --pretty
picotype --file document.txt
picotype --clip

Python

from picotype import PicoType, PicoTypeConfig, decode_output

model = PicoType(PicoTypeConfig()).eval()
# ... load checkpoint ...
result = decode_output(model(b"input bytes"), tier="base")

MCP Server (Claude/Cursor)

PICOTYPE_MODEL_DIR=./checkpoints python -m model.pico_type.mcp_server

🏗 Architecture

Bytes → ByteEmbed(256→96d) → 3×Conv1D(k=3,5,7) → 2×BiAttention(RoPE) → Pool(mean‖max‖std) → 7×Matryoshka Heads

Component	Description
ByteEmbed	nn.Embedding(256, 96) — lookup-free byte embedding
Conv1D	3 parallel kernels (width 3, 5, 7) with residual + LayerNorm + GELU
BiAttention	Bidirectional self-attention with Rotary Position Embeddings, 4 heads
Pool	Mean + Max + Std concatenation over masked positions
Matryoshka Heads	4 tier slices of the pooled vector → 7 linear classifiers

Total parameters: 1.43M (tiny) / 1.45M (small) / 1.48M (base) / 1.56M (pro)

🔧 Model Tiers

Tier	Dim	Params	ONNX Size	Speed
tiny	16	1.43M	207 KB	~3ms
small	64	1.45M	207 KB	~4ms
base	192	1.48M	209 KB	~5ms
pro	576	1.56M	206 KB	~12ms

All tiers share the same trunk; only the final linear layers differ. Switch tiers at inference with zero overhead.

🧪 Classification Heads

Head	Classes	Gated By	Examples
coarse	12	—	text, code, link, image, file, config, markup, data, error, secret, archive, binary
modality	8	—	textual, binary_image, binary_archive, binary_executable, binary_document, binary_audio, binary_video, binary_other
subtype	24	config, markup, data	json, yaml, toml, csv, html, markdown, sql, log, dockerfile
code_lang	62	code	python, javascript, typescript, java, c, cpp, go, rust, kotlin, swift, bash, sql
text_lang	30	text	en, es, fr, de, it, pt, ru, zh, ja, ko, ar, hi
file_mime	90	image, file	text/html, application/json, application/pdf, image/png, video/mp4
risk	6	—	api_key, jwt, password, email, phone, ssh_key (probabilities)

🌐 Deployment

Platform	URL
HuggingFace Space	eulogik/pico-type
HuggingFace Model	eulogik/pico-type
GitHub	eulogik/pico-type
PyPI	pip install picotype
Zenodo	10.5281/zenodo.20758542

📚 Documentation

• Model Card — detailed architecture, training, evaluation
• Architecture Plan — full design document
• Walkthrough — development log with all decisions

📄 License

Apache 2.0 — free for commercial and personal use.

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_{Built with ❤️ by eulogik}