metadata
title: FSI_ECHO
emoji: 🧬
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: 5.0.0
app_file: app.py
pinned: false
license: apache-2.0
🧬 FSI_ECHO — Morphing Code Swarm
World's smallest production code AI: 2.6M params, 1.3MB at Q4, runs on any phone.
Architecture — Novel "Morphing Code Swarm"
| Component | What it does |
|---|---|
| Morph Embedding | Tokens transform based on context (causal sliding window) |
| Nanobot Swarm | 512 nanobots with scout/combat dual-mode routing |
| Assembly Blocks | Multi-head attention with adaptive gating |
| Self-Verification | Built-in confidence scoring per token |
| Closed-Loop Debug | Generates, verifies syntax, and iteratively refines |
Metrics
- Parameters: 2,621,578
- FP32 size: 10.5 MB
- Q4 size: 1.31 MB — fits on any phone
- Training loss: 8.4 → 0.0 (trained on 2400+ code examples)
- Speed: ~10 tok/s on CPU
- Context: 2048 tokens
Usage
from fsi_echo import FSIEchoModel, CodeTokenizer, ClosedLoopDebugger
import torch
model = FSIEchoModel()
tok = CodeTokenizer()
ckpt = torch.load('prod2_final.pt', map_location='cpu', weights_only=True)
model.load_state_dict(ckpt['model'])
model.eval()
# Generate code
result = model.generate(tok, 'def reverse_str', max_tokens=50)
print(result['generated'])
# Debug code
debugger = ClosedLoopDebugger(model, tok)
result = debugger.debug("def add(a, b):\n a + b")
print(result['code'])
License
Apache 2.0