🧬 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