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<!doctype html>
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<title>Bitforge Precision Lab</title>
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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>
  <div class="grid">
    <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&#x27;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>{
  &quot;benchmark&quot;: &quot;BitForge 1-bit&quot;,
  &quot;parameters_per_variant&quot;: 4810,
  &quot;matrix_weight_count&quot;: 4736,
  &quot;test&quot;: {
    &quot;fp32&quot;: {
      &quot;accuracy&quot;: 0.9577777777777777,
      &quot;cross_entropy&quot;: 0.1542476937174797
    },
    &quot;binary_weight&quot;: {
      &quot;accuracy&quot;: 0.9422222222222222,
      &quot;cross_entropy&quot;: 0.2073044627904892
    },
    &quot;ternary_weight&quot;: {
      &quot;accuracy&quot;: 0.9511111111111111,
      &quot;cross_entropy&quot;: 0.19603287428617477
    }
  },
  &quot;storage&quot;: {
    &quot;fp32_parameter_payload_bytes&quot;: 19240,
    &quot;packed_payload_bytes&quot;: 1184,
    &quot;container_bytes&quot;: 3270,
    &quot;measured_payload_compression&quot;: 16.25
  },
  &quot;precision_boundary&quot;: {
    &quot;matrix_weights&quot;: &quot;one packed bit in binary variant&quot;,
    &quot;scales&quot;: &quot;float32 per output channel&quot;,
    &quot;biases&quot;: &quot;float32&quot;,
    &quot;activations&quot;: &quot;float32&quot;
  }
}</pre>
    </section>
    <section class="card">
      <h2>Backed-up artifact tree</h2>
      <input id="filter" placeholder="Filter files…" autocomplete="off">
      <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>
  </div>
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