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tm-nla-checkpoints
davwer/tm-nla-checkpoints
tm-nla-checkpoints is a machine learning model from davwer. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as mit.
These checkpoints support the TM-NLA public runtime.
Access
Public
Updated Jun 27, 2026
Repo size
189 MB
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.pt189 MB · 100%
At a glance
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Jun 26, 2026
- Updated
- Jun 27, 2026
- SHA
- 9270e657
Base models
Model card
From the Hugging Face model README
TM-NLA Checkpoints
These checkpoints support the TM-NLA public runtime.
TM-NLA is a proof-of-concept temporal activation-verbalization probe for frozen visual-temporal hidden states. It projects video-window activations from Qwen/Qwen3.5-0.8B into a continuous language-oriented manifold, then selects semantic readout hypotheses over time with compatibility and specificity annotations.
Files
| File | Role | SHA-256 |
|---|---|---|
temporal_probe.pt | Temporal visual activation -> continuous language-manifold probe | 48AABF662423EA529C985855EA54970A9CE3256EC3F14AFE0169C2BF1A55B217 |
activation_verbalizer.pt | Activation -> small set of short candidate readout proposals | 88DDB0267AE535A287755DE5C9E638EB771FFD5C5377051ACFCADF96A98C08C1 |
text_reconstructor.pt | Text readout -> activation compatibility verifier/reranker | C5CEA721A2173DCA884617258BB3F671668CF13EBA07728E11C39C9695B5DE32 |
Base Model
The runtime uses frozen Qwen/Qwen3.5-0.8B activations. The public demo path does not train or fine-tune Qwen.
Intended Use
Use these checkpoints with the TM-NLA GitHub repository to run local terminal-based semantic readouts over video windows:
https://github.com/DavWer/TM-NLA
The checkpoints are intended for interpretability and research exploration. They are not intended as a production captioner, classifier, benchmark system, or broad video-analysis product.
Limitations
The continuous activation geometry is the strongest result. TM-NLA shows weak recoverable temporal semantic traces from frozen visual-temporal activations. Natural-language readouts are useful but noisy externalizations, not ground-truth captions.
Readouts can be malformed, wrong, generic, or underspecified. The activation verbalizer remains fragile, and candidate generation plus AR reranking is a practical readout mechanism rather than a solved decoder. The text reconstructor is a compatibility-based verifier/reranker, not an oracle. specificity_status is a numerical activation-specificity annotation, not a ground-truth correctness label.
Citation
If you publish work building on this proof of concept, cite or link the companion TM-NLA GitHub repository:
- Library
- pytorch
- License
- mit
- Created
- Jun 26, 2026
- Updated
- Jun 27, 2026