README.md

metadata

title: Bitforge Precision Lab
emoji: 🧪
colorFrom: indigo
colorTo: blue
sdk: static
app_file: index.html
pinned: false

BitForge 1-bit

BitForge trains and compares a full-precision digit classifier, a strict one-bit weight model, and a ternary-weight model. The low-bit students use straight-through quantization during training and learn from both labels and the full-precision teacher's softened output distribution.

The binary deployment artifact stores each matrix weight as one packed sign bit, plus one floating-point scale per output channel and floating-point biases. The project reports both classification accuracy and the measured inference payload. Activations, scales, and biases remain floating point, so this is specifically a one-bit matrix-weight experiment rather than a claim that every operation or parameter is one bit.

Verified results

Variant	Test accuracy	Accuracy change
FP32 teacher	95.78%	reference
Packed binary matrix weights	94.22%	-1.56 points
Ternary matrix weights	95.11%	-0.67 points

Each network has 4,810 parameters, including 4,736 matrix weights. The measured inference payload fell from 19,240 bytes for FP32 parameters to 1,184 bytes for packed signs, per-channel scales, and biases, a 16.25 times reduction. The .npz container itself is 3,270 bytes because it also carries names, shapes, and archive metadata. An independent reload of the packed signs reproduced 94.22% accuracy.

Reproduce

uv run python projects/bitforge-1bit/train.py

Hosted showcase

This free static Space preserves the complete original Gradio source, trained artifacts, evaluation files, and local launch requirements. Hugging Face now requires PRO for CPU-backed Gradio hosting, so the public landing page is static while the checked-in app.py remains the authoritative runnable demo.