TinyLlama-1.1B-Argo1-OOV1

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 — Argo1-OOV1

Argo1-OOV1 adds a learned goal-conditioned path to a frozen TinyLlama chat Transformer. It measures the prompt left-to-right, measures a user-supplied end goal right-to-left, fuses their edge/travel mismatch into a bounded hidden residual, and can retain beam routes with the lowest accumulated -log(probability) energy.

This loss measures the synthetic future-goal branch objective. It does not show that generated answers reach arbitrary human goals or outperform TinyLlama.

Use

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 detects missing weights, downloads them with progress, and shows research_paper.pdf. The UI exposes the end goal, guidance, beam width, token limit, lowest-energy route and per-token normal-versus-goal choice audit.

Limitations

Goal guidance may be too weak, may trade fluency for lexical overlap, or may take a long time because the current beam audit recomputes prefixes without a KV cache. “Lowest energy” means highest model probability under this learned goal-conditioned distribution; it is not physical energy and does not prove correctness, safety, or plan optimality.

Citation

@misc{argo1ooas2026argo1oov1,
 title={Argo1-OOV1: Goal-Conditioned Bidirectional Edge/Travel Steering},
 author={Argo1-OOAS}, year={2026},
 url={https://huggingface.co/Argo1-OOAS/TinyLlama-1.1B-Argo1-OOV1}
}