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| <div class="eyebrow">Jacob Garcia · Hugging Face Model Foundry</div> | |
| <h1>Bitforge Precision Lab</h1> | |
| <p class="lead">Interactive FP32, binary, and ternary comparison. This showcase backs up the | |
| trained artifacts, measured evaluation, and complete runnable source.</p> | |
| <div class="actions"> | |
| <a class="button" href="https://huggingface.co/spaces/ARotting/bitforge-precision-lab/tree/main">Explore every file</a> | |
| <a class="button alt" href="https://huggingface.co/ARotting">View the full foundry</a> | |
| </div> | |
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| <section class="card"> | |
| <h2>Verified project card</h2> | |
| <pre># 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 | |
| ``` | |
| </pre> | |
| <h2>Evaluation snapshot</h2> | |
| <pre>{ | |
| "benchmark": "BitForge 1-bit", | |
| "parameters_per_variant": 4810, | |
| "matrix_weight_count": 4736, | |
| "test": { | |
| "fp32": { | |
| "accuracy": 0.9577777777777777, | |
| "cross_entropy": 0.1542476937174797 | |
| }, | |
| "binary_weight": { | |
| "accuracy": 0.9422222222222222, | |
| "cross_entropy": 0.2073044627904892 | |
| }, | |
| "ternary_weight": { | |
| "accuracy": 0.9511111111111111, | |
| "cross_entropy": 0.19603287428617477 | |
| } | |
| }, | |
| "storage": { | |
| "fp32_parameter_payload_bytes": 19240, | |
| "packed_payload_bytes": 1184, | |
| "container_bytes": 3270, | |
| "measured_payload_compression": 16.25 | |
| }, | |
| "precision_boundary": { | |
| "matrix_weights": "one packed bit in binary variant", | |
| "scales": "float32 per output channel", | |
| "biases": "float32", | |
| "activations": "float32" | |
| } | |
| }</pre> | |
| </section> | |
| <section class="card"> | |
| <h2>Backed-up artifact tree</h2> | |
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| <ul id="files"><li><code>README.md</code></li> | |
| <li><code>__pycache__/app.cpython-311.pyc</code></li> | |
| <li><code>__pycache__/model.cpython-311.pyc</code></li> | |
| <li><code>__pycache__/packing.cpython-311.pyc</code></li> | |
| <li><code>__pycache__/train.cpython-311.pyc</code></li> | |
| <li><code>app.py</code></li> | |
| <li><code>artifacts/bitforge-1bit/binary_qat.safetensors</code></li> | |
| <li><code>artifacts/bitforge-1bit/binary_weights.npz</code></li> | |
| <li><code>artifacts/bitforge-1bit/evaluation.json</code></li> | |
| <li><code>artifacts/bitforge-1bit/fp32.safetensors</code></li> | |
| <li><code>artifacts/bitforge-1bit/ternary_qat.safetensors</code></li> | |
| <li><code>data/split_manifest.parquet</code></li> | |
| <li><code>model.py</code></li> | |
| <li><code>packing.py</code></li> | |
| <li><code>requirements.txt</code></li> | |
| <li><code>train.py</code></li></ul> | |
| </section> | |
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