v0.1 release

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.onnx.data filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.onnx.data filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+language:
+- multilingual
+tags:
+- content-classification
+- byte-level
+- onnx
+- matryoshka
+- lightweight
+- classifier
+pipeline_tag: text-classification
+library_name: pico-type
+---
+
+# pico-type
+
+A tiny **byte-level multi-head content classifier** (~1.5M parameters) that classifies any content into **7 categories** simultaneously from raw bytes — no tokenizer, no pretrained embeddings.
+
+## Architecture
+
+```
+ByteEmbed → Conv1D×3 → BiAttention×2 → Pool → Matryoshka Heads
+```
+
+- **Byte-level**: operates directly on UTF-8 bytes, supports any language
+- **Matryoshka heads**: 7 independent classification heads with 4 tiers (tiny/small/base/pro)
+- **1.5M params**: fits in ~200KB ONNX, runs in <12ms on CPU
+- **No tokenizer**: zero vocabulary dependencies
+
+## Classification Heads
+
+| Head | Classes | Description |
+|------|---------|-------------|
+| **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 | json, yaml, toml, ini, csv, html, xml, markdown, sql, log, diff, dockerfile, etc. |
+| **code_lang** | 62 | python, javascript, typescript, java, c, cpp, go, rust, swift, bash, sql, etc. |
+| **text_lang** | 30 | en, es, fr, de, it, pt, ru, zh, ja, ko, ar, hi, etc. |
+| **file_mime** | 90 | text/html, application/json, application/pdf, image/png, video/mp4, etc. |
+| **risk** | 6 | api_key, jwt, password, email, phone, ssh_key (probabilities) |
+
+## Performance
+
+Benchmarked on synthetic data (1000 samples, 1024 bytes max, base tier):
+
+| Head | Accuracy |
+|------|----------|
+| coarse | 99.4% |
+| modality | 100.0% |
+| subtype | 95.4% |
+| text_lang | 81.3% |
+| file_mime | 100.0% |
+| risk (mAP) | 90.5% |
+
+- **Inference**: ~11ms per sample on CPU (ONNX)
+- **Model size**: ~200KB (FP32 ONNX)
+
+> **code_lang** shows 47.1% accuracy due to broad language coverage (62 classes). Performance improves with longer code sequences (>256 bytes).
+
+## Usage
+
+### CLI
+```bash
+# Pipe content
+echo "def hello(): pass" | picotype --pretty
+
+# File
+picotype --file document.txt
+
+# Clipboard (macOS)
+picotype --clip
+```
+
+### Python
+```python
+from model.pico_type.labels import decode_output
+
+# Run with ONNX session
+result = {"coarse": "code", "modality": "textual", ...}
+decoded = decode_output(result, tier="base")
+```
+
+### MCP Server
+```bash
+PICOTYPE_MODEL_DIR=./checkpoints python -m model.pico_type.mcp_server
+```
+
+## Model Tiers
+
+| Tier | Head Dim | Params | ONNX Size |
+|------|----------|--------|-----------|
+| tiny | 16 | 1.43M | 203 KB |
+| small | 64 | 1.45M | 203 KB |
+| base | 192 | 1.48M | 206 KB |
+| pro | 576 | 1.56M | 202 KB |
+
+All tiers share the same trunk; only the final linear layer differs per tier.
+
+## Deployment
+
+### HuggingFace Space
+The [Gradio Space](https://huggingface.co/spaces/eulogik/pico-type) provides:
+- Text input and file upload
+- Real-time 7-head classification
+- Tier selection (tiny/small/base/pro)
+
+### ONNX Runtime
+```python
+import onnxruntime
+session = ort.InferenceSession("picotype_base.onnx")
+```
+
+## Training
+
+Trained on synthetic data (11 content buckets, 62 code languages, 30 text languages, 90 MIME types) using multi-task loss with 500 optimization steps.
+
+- **Loss**: weighted cross-entropy (coarse) + binary cross-entropy (risk)
+- **Optimizer**: AdamW (lr=1e-3, weight_decay=0.01)
+- **GPU**: ~100ms/step on MPS, ~3.5s/step on CPU
+
+## License
+
+Apache 2.0

gradio_app.py

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+"""pico-type Gradio Space: classify content type, language, and risk."""
+
+from __future__ import annotations
+
+import os
+
+import gradio as gr
+import numpy as np
+
+from model.pico_type.labels import ALL_HEADS, COARSE_LABELS, MODALITY_LABELS, SUBTYPE_LABELS, CODE_LANG_LABELS, TEXT_LANG_LABELS, FILE_MIME_LABELS, RISK_LABELS
+
+LABEL_TABLES = {
+    "coarse": COARSE_LABELS,
+    "modality": MODALITY_LABELS,
+    "subtype": SUBTYPE_LABELS,
+    "code_lang": CODE_LANG_LABELS,
+    "text_lang": TEXT_LANG_LABELS,
+    "file_mime": FILE_MIME_LABELS,
+    "risk": RISK_LABELS,
+}
+
+MODEL_DIR = os.environ.get("PICOTYPE_MODEL_DIR", "checkpoints")
+
+
+def _load_session(tier: str):
+    import onnxruntime as ort
+    path = os.path.join(MODEL_DIR, f"picotype_{tier}.onnx")
+    return ort.InferenceSession(path)
+
+
+SESSIONS = {}
+
+
+def _get_session(tier: str):
+    if tier not in SESSIONS:
+        SESSIONS[tier] = _load_session(tier)
+    return SESSIONS[tier]
+
+
+def _softmax(x):
+    e = np.exp(x - np.max(x))
+    return e / e.sum()
+
+
+def classify(text: str, tier: str) -> dict:
+    if not text.strip():
+        return {}
+    session = _get_session(tier)
+    text_bytes = text.encode("utf-8")[:1024]
+    ids = np.frombuffer(text_bytes, dtype=np.uint8).astype(np.int64)
+    seq_len = len(ids)
+    padded = np.zeros(1024, dtype=np.int64)
+    padded[:seq_len] = ids
+    mask = np.zeros(1024, dtype=np.bool_)
+    mask[:seq_len] = True
+    outs = session.run(None, {"input_ids": padded[None, :], "attention_mask": mask[None, :]})
+    result = {}
+    for name, logits in zip(ALL_HEADS, outs):
+        probs = _softmax(logits[0])
+        if name == "risk":
+            result[name] = {LABEL_TABLES[name][i]: float(probs[i]) for i in range(len(probs))}
+        else:
+            idx = int(np.argmax(probs))
+            result[name] = {"label": LABEL_TABLES[name][idx], "confidence": float(probs[idx])}
+    return result
+
+
+def build_ui():
+    with gr.Blocks(title="pico-type", theme=gr.themes.Soft()) as demo:
+        gr.Markdown(
+            """
+            # pico-type 🔍
+            A tiny byte-level multi-head content classifier (~1.5M params).  
+            Classifies content into **7 categories**: coarse type, modality, subtype, code language, text language, file MIME, and risk flags.
+            """
+        )
+
+        with gr.Row():
+            with gr.Column(scale=2):
+                text_input = gr.Textbox(
+                    label="Input Content",
+                    placeholder="Paste or type content to classify...",
+                    lines=10,
+                )
+                with gr.Row():
+                    tier_selector = gr.Radio(
+                        choices=["tiny", "small", "base", "pro"],
+                        value="base",
+                        label="Model Tier",
+                    )
+                    submit_btn = gr.Button("Classify", variant="primary", scale=2)
+                    clear_btn = gr.Button("Clear")
+
+                gr.Examples(
+                    examples=[
+                        ["def hello():\n    print('Hello, world!')"],
+                        ["The quick brown fox jumps over the lazy dog."],
+                        ["<html><body><h1>Welcome</h1></body></html>"],
+                        ["#!/usr/bin/env python3\nimport os\nprint('hello')"],
+                        ["{\n  \"name\": \"pico-type\",\n  \"version\": \"0.1.0\"\n}"],
+                        ["BEGIN:VCALENDAR\nVERSION:2.0\nEND:VCALENDAR"],
+                    ],
+                    inputs=[text_input],
+                    label="Try these examples",
+                )
+
+            with gr.Column(scale=2):
+                output_labels = []
+                with gr.Tabs():
+                    for head_name in ALL_HEADS:
+                        with gr.Tab(head_name.replace("_", " ").title()):
+                            lbl = gr.Label(
+                                value={},
+                                label=head_name.replace("_", " ").title(),
+                            )
+                            output_labels.append(lbl)
+
+        def handle_classify(text, tier):
+            result = classify(text, tier)
+            outputs = {}
+            for head in ALL_HEADS:
+                if head == "risk":
+                    outputs[head] = result.get(head, {})
+                else:
+                    outputs[head] = {result.get(head, {}).get("label", "unknown"): result.get(head, {}).get("confidence", 0)}
+            return [outputs[h] for h in ALL_HEADS]
+
+        submit_btn.click(
+            fn=handle_classify,
+            inputs=[text_input, tier_selector],
+            outputs=output_labels,
+        )
+
+        clear_btn.click(
+            fn=lambda: (""),
+            inputs=[],
+            outputs=[text_input],
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = build_ui()
+    demo.launch()

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requirements.txt

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+gradio>=4.0
+onnxruntime>=1.15
+numpy<2