Recommended Usage
For the easiest way to use this model with all required settings and its full functionality, use the TrajectoryLM application.
Screenshots
TinyLlama-1.1B V3 — edge/travel trigger
This model scales the contextual edge/travel trigger branch to a frozen TinyLlama-1.1B chat core. Only 16,781,313 branch parameters were optimized; the official Transformer weights remained unchanged. A trigger can activate the branch for a configurable number of following tokens, optionally conditioned on user text.
| Property | Value |
|---|---|
| Total parameters | 1,116,829,697 |
| Trained branch parameters | 16,781,313 |
| Layers / width | 22 / 2,048 |
| Context length | 2,048 |
| Completed branch updates | 2,000 |
| Token presentations | 262,144,000 |
| Terminal sampled validation loss | 0.5318 (PPL 1.7) |
The validation figure is a terminal training-loop estimate for the selected branch lesson objective, not ordinary all-token TinyLlama perplexity.
UI usage
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 cross-platform launcher can fetch missing files with download progress.
The UI highlights triggers and affected tokens and audits the branch-off versus
final token choices. See research_paper.pdf for the complete report.
Limitations
Strong context guidance can damage fluency or cause trigger loops. The model is experimental, has no broad safety/capability evaluation, and must not be used as a reliable source for high-impact decisions.
Citation
@misc{argo1ooas2026tinyllamav3,
title={Scaling Trigger-Scoped Edge/Travel Adaptation to TinyLlama-1.1B},
author={Argo1-OOAS}, year={2026},
url={https://huggingface.co/Argo1-OOAS/TinyLlama-1.1B-V3-EdgeTravel}
}