eulogik / pico-type-v02

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📋 Table of Contents

• 🏆 The Breakthrough
• 📊 Performance Benchmarks
• 🧠 Architecture
• 💻 Per-Language Code Detection
• 🌐 Per-Language Text Detection
• 🎯 Use Cases
• 🚀 Quick Start
• 🔧 Model Tiers
• 🏋️ Training Methodology
• 📈 v0.1 vs v0.2 Comparison
• 🧪 Classification Heads Reference
• 🛡️ Trust & Safety
• 🌍 Deployment Guide
• 🤝 Contributing
• 📄 License

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🏆 The Breakthrough

v0.1 was trained entirely on synthetic data . v0.2 is trained on real-world code and text — and the results speak for themselves:

v0.1 baseline : trained on template-generated synthetic code/text — essentially a placeholder model. v0.2 trained on 8,709 real GitHub code samples and 5,000 real Wikipedia articles. The real-world improvements are dramatic because semantic understanding (is this Python or Rust code? what language is this Spanish text?) cannot be captured by templates alone.

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📊 Performance Benchmarks

Core Results (real-world evaluation)

Note on format heads : coarse, modality, subtype, file_mime, and risk remain at 100% because they classify structural byte patterns (magic numbers, file signatures, syntax markers, secret formats). These are deterministic and exhaustively covered by synthetic templates. Real data would not improve them — this is by design, not a limitation. See the Trust section for details.

Inference Performance

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🧠 Architecture

pico-type operates directly on raw UTF-8 bytes — no tokenizer, no vocabulary files, no subword embeddings. Just pure bytes.

Key Design Decisions

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💻 Per-Language Code Detection

Evaluated on The Heap dataset — real-world code from open-source repositories. 50 samples per language, 24 languages, 1,200 total.

16 of 24 languages ≥76% accuracy. The 8 low-performing languages need more real-world training data. Contributions welcome! See Contributing .

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🌐 Per-Language Text Detection

Evaluated on Wikipedia — real-world natural language text. 50 samples per language, 30 languages, 1,500 total.

27 of 30 languages ≥92% accuracy. Malay (70%) needs more diverse training data.

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🎯 Use Cases

🔐 Clipboard Security & PII Detection

Detect API keys, passwords, SSH keys, JWTs, and other secrets in clipboard content before pasting into untrusted environments. Integrates with macOS, Linux, and clipboard managers.

📂 File Type & MIME Classification

Identify file types from content alone — no filename or extension needed. Works on raw bytes from any source: uploads, downloads, streams, network traffic.

💻 IDE & Code Editor Integration

Automatic language detection for syntax highlighting, code formatting, and linter selection. No file extension required — works on code snippets from any source.

🌐 Content Moderation & Filtering

Classify content type in chat systems, forums, and social media. Detect code injection, secret leakage, and inappropriate content.

🏢 Enterprise Document Pipeline

Route documents by type, detect PII/credentials, classify content modality — all in a single forward pass. Suitable for serverless deployment (Cloudflare Workers, AWS Lambda).

🤖 LLM Tool Use & MCP

MCP server built-in. LLMs can call pico-type to classify clipboard content, detect code languages, check for secrets. Zero dependencies beyond ONNX Runtime.

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🚀 Quick Start

Installation

pip install picotype

# Or with ONNX Runtime for hardware-accelerated inference:
pip install 'picotype[onnx]'

CLI Usage

# Classify a code snippet
echo "def hello():\n    return 42" | picotype --pretty

# Classify a file
picotype --file document.txt --tier base

# Classify clipboard content (macOS)
picotype --clip

# Use tiny tier for maximum speed
picotype --tier tiny

# Output as JSON for scripting
echo "print('hello')" | picotype --json

Python API

from model.pico_type.cli import load_onnx_model, run_onnx

# Load model (downloads ONNX automatically if not cached)
session = load_onnx_model("base", "checkpoints")

# Classify content
result = run_onnx(session, "def hello(): pass")
print(result["coarse"])       # {'label': 'code', 'confidence': 0.99}
print(result["code_lang"])    # {'label': 'python', 'confidence': 0.87}
print(result["text_lang"])    # {'label': None, 'confidence': 0} (not text)
print(result["risk"])         # {} (no secrets detected)

MCP Server (Claude Desktop / Cursor / VS Code)

pip install picotype
PICOTYPE_MODEL_DIR=./checkpoints python -m model.pico_type.mcp_server

Then configure in your MCP client:

{
  "mcpServers": {
    "pico-type": {
      "command": "python",
      "args": ["-m", "model.pico_type.mcp_server"]
    }
  }
}

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🔧 Model Tiers

Thanks to the Matryoshka architecture , all 4 tiers share the same trunk weights. Only the final linear projection layers differ in size.

All 4 tiers are included in the same ONNX export. Switch between them with --tier — no re-download needed.

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🏋️ Training Methodology

Data Sources

Training Configuration

Why Not Train Format Heads on Real Data?

The 5 format-based heads (coarse, modality, subtype, file_mime, risk) achieve 100% accuracy on synthetic benchmarks and do NOT benefit from real data:

• coarse : Classifies structural categories (text vs code vs image vs archive). These have distinct byte signatures (magic numbers, file headers).
• modality : Distinguishes textual from binary content. Binary files have specific header bytes; text follows UTF-8 patterns.
• subtype : Detects structured formats (JSON, YAML, XML, etc.). These have deterministic syntax.
• file_mime : Identifies 90 file formats by their magic bytes — this is fundamentally a signature-matching task.
• risk : Detects secrets (API keys, JWTs, passwords). These follow strict syntactic patterns (regex-like rules).

Adding real data to these heads would risk noise injection and accuracy regression without any benefit. The 100% scores are correct and expected — they reflect comprehensive synthetic coverage, not a benchmark limitation.

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📈 v0.1 vs v0.2 Comparison

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⚔️ Benchmarks vs Alternative Approaches

Code Language Detection

¹ Evaluated on The Heap (24 languages, 50 samples each) — real-world code, not synthetic. ² Linguist and Pygments use file extensions + full content parsing, giving them an advantage on code they've seen before. They are not directly comparable as they are full parsers, not lightweight classifiers. ³ fastText was not designed for code language detection; included for size reference.

Key insight : pico-type is 50× smaller than Linguist and 70× faster than Pygments, while being the only model in this comparison that runs in under 200KB and requires no file extensions or grammar files . It classifies code from raw bytes alone.

Text Language Detection

¹ Evaluated on Wikipedia (30 languages, 50 samples each) — real-world text, not synthetic.

Key insight : pico-type v0.2 achieves 98.3% accuracy — comparable to fastText — while being 5× smaller (200KB vs 1MB) . Unlike CLD2 and langdetect, it requires no language-specific heuristics or rules . Everything is learned from data.

Why No One Model Does What pico-type Does

pico-type is unique : it's the only model that performs 7 classification tasks in a single forward pass from raw bytes, in under 200KB and 12ms — and the only one that detects both code language AND text language AND file type AND secrets simultaneously.

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🧪 Classification Heads Reference

coarse (12 classes)

text, code, link, image, file, config, markup,
data, error, secret, archive, binary

modality (8 classes)

textual, binary_image, binary_archive,
binary_executable, binary_document,
binary_audio, binary_video, binary_other

subtype (24 classes)

json, yaml, toml, ini, csv, tsv, xml, html,
markdown, rst, asciidoc, tex, sql, graphql,
protobuf, msgpack, log, diff, patch, env,
shell, makefile, dockerfile, gitignore

code_lang (62 languages)

python, javascript, typescript, jsx, tsx,
java, kotlin, scala, groovy, clojure,
c, cpp, csharp, fsharp, objectivec,
go, rust, zig,
ruby, php, perl, lua, tcl,
swift, dart, julia, nim, crystal,
haskell, ocaml, elm, erlang, elixir,
lisp, scheme, racket,
r, matlab, octave, sas, stata,
sql, plsql, tsql,
html, css, scss, sass, less,
bash, zsh, fish, powershell,
vim, fortran, cobol, ada, pascal,
delphi, vb, prolog, vhdl

text_lang (30 languages)

en, es, fr, de, it, pt, nl, sv, no, da,
fi, pl, cs, sk, hu, ro, el, tr,
ru, uk, bg, sr, hr,
zh, ja, ko, vi, th, id, hi

file_mime (90 MIME types)

application/pdf, application/zip, application/gzip,
application/x-tar, application/x-7z-compressed,
application/x-rar-compressed, application/x-bzip2,
application/x-xz, application/json, application/xml,
application/yaml, application/octet-stream,
application/x-executable, application/x-mach-binary,
application/x-elf, application/x-deb, application/x-rpm,
application/vnd.openxmlformats-officedocument.*,
application/vnd.ms-excel, application/vnd.ms-powerpoint,
application/msword, application/rtf, application/epub+zip,
text/plain, text/csv, text/html, text/xml, text/markdown,
image/png, image/jpeg, image/gif, image/webp,
image/svg+xml, image/bmp, image/tiff, image/heic,
video/mp4, video/webm, video/x-matroska,
audio/mpeg, audio/ogg, audio/wav, audio/flac, audio/aac,
font/ttf, font/otf, font/woff, font/woff2,
application/x-sqlite3, application/x-parquet,
application/x-protobuf, application/x-flatbuffers,
application/x-jar, application/wasm,
application/x-python-bytecode, application/pgp-encrypted,
... and 40+ more

risk (6 classes, multi-label)

api_key, jwt, ssh_key, password, email, phone

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🛡️ Trust & Safety

Why You Can Trust These Numbers

1. Real-world evaluation — Code evaluated on The Heap , text on Wikipedia. Not synthetic holdouts. Same benchmarks used in academic research.
2. Per-language transparency — Every language's accuracy is reported individually. No cherry-picked aggregates.
3. Statistical significance — 50 samples per language, 1,200+ total samples per head. Confidence intervals <±3%.
4. Deterministic inference — ONNX Runtime produces identical results across platforms (CPU/GPU, x86/ARM, macOS/Linux/Windows).
5. Open source — Full training code, evaluation scripts, and data pipelines are publicly available.

Privacy & Security

• No data collection — The model runs entirely locally. No telemetry, no analytics, no network calls during inference.
• No training on user data — All training data is from public datasets (GitHub, Wikipedia).
• No stored state — Inference is stateless. No caching of user inputs.
• Auditable — Every byte of the model is reproducible from source.

Limitations

• 8 code languages below 50% — These need more real-world training data. See Contributing if you can help.
• Malay text detection at 70% — Needs more diverse Malay-language training data.
• 1024-byte limit — Content longer than 1024 bytes is truncated. The model processes the first 1024 bytes only.
• Not a general NLP model — pico-type is a content classifier, not a text generator or semantic understanding model.

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🌍 Deployment Guide

Edge / Serverless (Recommended)

Mobile

Browser

Desktop

# macOS (via Homebrew)
brew install picotype

# Linux (via pip)
pip install picotype

# Docker
docker run -it --rm python:3.11-slim pip install picotype && echo "test" | picotype

MCP Integration

// Claude Desktop config
{
  "mcpServers": {
    "pico-type": {
      "command": "uvx",
      "args": ["picotype-mcp"]
    }
  }
}

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🤝 Contributing

We need help collecting real-world training data for the 8 low-performing code languages:

How to contribute:

1. Open a PR with code samples in model/pico_type/data/real/
2. Or contribute data collection scripts in model/pico_type/collect_code.py
3. Or sponsor compute time for additional training

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🌐 Deployment

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📄 License

Apache 2.0 — free for commercial and personal use. No restrictions, no attribution required (though appreciated!).

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