This model repository package contains tiny from-scratch PyTorch checkpoints for the nanoIM symbolic temporal-aliasing lab.
What Is Included
Path
Description
checkpoints/tiny/best.pt
Mini-suite GRU checkpoint.
checkpoints/hard/best.pt
Hard-suite GRU checkpoint.
checkpoints/noisy/best.pt
Noisy-suite GRU checkpoint.
checkpoints/transformer_hard/best.pt
Hard-suite tiny Transformer checkpoint.
configs/*.yaml
Training configs used to produce the checkpoints.
reports/*.json
Scorecards, sweeps, controls, and release verification evidence.
Parameter Counts
Checkpoint
Parameters
checkpoints/tiny/best.pt
19,288
checkpoints/hard/best.pt
65,604
checkpoints/noisy/best.pt
115,256
checkpoints/transformer_hard/best.pt
257,988
Largest model is ~258K parameters. All others are under 120K.
Intended Use
Use these checkpoints to reproduce nanoIM scorecards and inspect how a native micro-turn representation separates alias pairs that a transcript-only baseline cannot separate.
The models consume symbolic micro-turn features. They are not text generators, not chat models, and not production assistants.
Loading
The nanoIM evaluator loads checkpoints with torch.load(..., weights_only=True).
From the source repository, evaluate the committed source-tree checkpoint:
From this generated Hugging Face model repository, install or clone the nanoIM source package, then point the evaluator at the model-repo checkpoint path:
The generated model repo also includes MANIFEST.json and SHA256SUMS so a reviewer can bind checkpoint files to the release manifest before evaluation.
Evaluation Summary
Model
Suite
TAA
Delta vs transcript oracle
MicroTurn Tiny GRU
hard
1.00
+0.50
MicroTurn Tiny GRU
noisy
1.00
+0.50
MicroTurn Tiny Transformer
hard
1.00
+0.50
The rule harness and a memorized field-lookup table also reach 1.00. The result is about the transcript representation's 0.50 ceiling, not about model superiority.
Transcript-only paired separation remains 0.00 on the hard and noisy suites.
Limitations
The checkpoints prove a controlled symbolic representation result. They do not perform ASR, TTS, visual perception, natural language generation, tool execution, or realtime dialogue management.
