Argo1-OOAS/SmolLM2-135M-V3-EdgeTravel

Recommended Usage

For the easiest way to use this model with all required settings and its full functionality, use the TrajectoryLM application.

Screenshots

SmolLM2-135M V3 — edge/travel trigger

This checkpoint freezes SmolLM2 and trains a 1,328,257-parameter branch that measures changes between consecutive hidden rows (edges), accumulates their magnitudes (travel), and uses that trajectory to adjust selected following token distributions. Runtime trigger words and optional context are controlled by the companion comparison UI.

Property	Value
Unique parameters	135,843,265
Trained branch parameters	1,328,257
Layers / width	30 / 576
Context length	8,192
Completed branch updates	2,000
Token presentations	262,144,000
Terminal sampled validation loss	1.1849 (PPL 3.3)

The recorded validation value is a terminal ten-batch training-loop estimate for this branch objective, not a standardized full-stream language benchmark.

Local UI

git clone https://github.com/Argo1-OOAS/TrajectoryLM.git
cd TrajectoryLM

Run start_windows.ps1 on Windows or start_macos.command on macOS, following the TrajectoryLM README. The launcher checks for local weights and can download the Hugging Face release with visible progress. See research_paper.pdf for equations, provenance and limitations. The repository-native PyTorch implementation is required.

Limitations

This is an architecture experiment, not a production assistant. Guidance can be too weak to redirect text or strong enough to damage fluency and cause loops. It has no comprehensive capability or safety evaluation.

Citation

@misc{argo1ooas2026smollm2v3,
  title={Contextual Edge/Travel Trigger Adaptation of SmolLM2-135M},
  author={Argo1-OOAS}, year={2026},
  url={https://huggingface.co/Argo1-OOAS/SmolLM2-135M-V3-EdgeTravel}
}