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| <div class="eyebrow">Jacob Garcia · Hugging Face Model Foundry</div> |
| <h1>Memory Tape Pocket Lab</h1> |
| <p class="lead">Interactive differentiable memory read-head inspector. 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/memory-tape-pocket-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># Memory Tape Pocket |
|
|
| Memory Tape Pocket is a compact differentiable-memory retest inspired by the |
| content-addressing mechanism of Neural Turing Machines. It learns random |
| key-value associative recall on tapes containing two to eight slots, then faces |
| unseen tapes with 16 and 32 slots. |
|
|
| The control is a larger fixed-state GRU trained on the same batches. The |
| interactive Space exposes the complete external tape and the learned read |
| weight assigned to every slot. |
|
|
| ## Verified result |
|
|
| Across three independent training seeds, the 4,673-parameter content-addressed |
| model achieved **100% exact recall** on 8-, 16-, and 32-slot tapes. At 32 slots, |
| four times the maximum training length, its read head placed **99.974%** of its |
| attention on the correct slot. |
|
|
| The larger 5,584-parameter fixed-state GRU reached 13.51% accuracy at eight |
| slots, 7.66% at 16 slots, and **4.60% at 32 slots**. This benchmark isolates the |
| inductive bias of external content addressing; it does not claim the tiny model |
| implements every component of a full Neural Turing Machine. |
|
|
| ```bash |
| uv run python projects/memory-tape-pocket/train.py |
| uv run pytest tests/test_memory_tape_pocket.py |
| ``` |
| </pre> |
| <h2>Evaluation snapshot</h2> |
| <pre>{ |
| "experiment": "Differentiable content addressing versus fixed-state recall", |
| "training_slots": [ |
| 2, |
| 8 |
| ], |
| "results": { |
| "memory": { |
| "parameters": 4673, |
| "runs": [ |
| { |
| "seed": 2281, |
| "slots_8": { |
| "accuracy": 1.0, |
| "examples": 4096, |
| "mean_attention_on_correct_slot": 0.9999377218191512 |
| }, |
| "slots_16": { |
| "accuracy": 1.0, |
| "examples": 4096, |
| "mean_attention_on_correct_slot": 0.9998665036546299 |
| }, |
| "slots_32": { |
| "accuracy": 1.0, |
| "examples": 4096, |
| "mean_attention_on_correct_slot": 0.9997205645777285 |
| } |
| }, |
| { |
| "seed": 2287, |
| "slots_8": { |
| "accuracy": 1.0, |
| "examples": 4096, |
| "mean_attention_on_correct_slot": 0.9999373428727267 |
| }, |
| "slots_16": { |
| "accuracy": 1.0, |
| "examples": 4096, |
| "mean_attention_on_correct_slot": 0.9998682647856185 |
| }, |
| "slots_32": { |
| "accuracy": 1.0, |
| "examples": 4096, |
| "mean_attention_on_correct_slot": 0.999723744156654 |
| } |
| }, |
| { |
| "seed": 2293, |
| "slots_8": { |
| "accuracy": 1.0, |
| "examples": 4096, |
| "mean_attention_on_correct_slot": 0.9999510854540858 |
| }, |
| "slots_16": { |
| "accuracy": 1.0, |
| "examples": 4096, |
| "mean_attention_on_correct_slot": 0.9998970205051592 |
| }, |
| "slots_32": { |
| "accuracy": 1.0, |
| "examples": 4096, |
| "mean_attention_on_correct_slot": 0.9997834917012369 |
| } |
| } |
| ], |
| "accuracy_mean": { |
| "slots_8": 1.0, |
| "slots_16": 1.0, |
| "slots_32": 1.0 |
| }, |
| "correct_slot_attention_mean": { |
| "slots_8": 0.9999420500486546, |
| "slots_16": 0.9998772629818026, |
| "slots_32": 0.9997426001452064 |
| } |
| }, |
| "gru": { |
| "parameters": 5584, |
| "runs": [ |
| { |
| "seed": 2281, |
| "slots_8": { |
| "accuracy": 0.132080078125, |
| "examples": 4096 |
| }, |
| "slots_16": { |
| "accuracy": 0.083984375, |
| "examples": 4096 |
| }, |
| "slots_32": { |
| "accuracy": 0.044677734375, |
| "examples": 4096 |
| } |
| }, |
| { |
| "seed": 2287, |
| "slots_8": { |
| "accuracy": 0.135009765625, |
| "examples": 4096 |
| }, |
| "slots_16": { |
| "accuracy": 0.0703125, |
| "examples": 4096 |
| }, |
| "slots_32": { |
| "accuracy": 0.046142578125, |
| "examples": 4096 |
| } |
| }, |
| { |
| "seed": 2293, |
| "slots_8": { |
| "accuracy": 0.13818359375, |
| "examples": 4096 |
| }, |
| "slots_16": { |
| "accuracy": 0.075439453125, |
| "examples": 4096 |
| }, |
| "slots_32": { |
| "accuracy": 0.047119140625, |
| "examples": 4096 |
| } |
| } |
| ], |
| "accuracy_mean": { |
| "slots_8": 0.13509114583333334, |
| "slots_16": 0.07657877604166667, |
| "slots_32": 0.045979817708333336 |
| } |
| } |
| } |
| }</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__/train.cpython-311.pyc</code></li> |
| <li><code>app.py</code></li> |
| <li><code>artifacts/memory-tape-pocket/content_memory.safetensors</code></li> |
| <li><code>artifacts/memory-tape-pocket/evaluation.json</code></li> |
| <li><code>artifacts/memory-tape-pocket/fixed_gru.safetensors</code></li> |
| <li><code>model.py</code></li> |
| <li><code>requirements.txt</code></li> |
| <li><code>train.py</code></li></ul> |
| </section> |
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