| --- |
| license: apache-2.0 |
| tags: |
| - quantization |
| - binary-neural-network |
| - ternary-weights |
| - knowledge-distillation |
| - gradio |
| --- |
| |
| # 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 |
|
|
| ```powershell |
| uv run python projects/bitforge-1bit/train.py |
| ``` |
|
|
