Instructions to use SahoobhAI/Direction-Not-Distance-Full-Trajectories with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SahoobhAI/Direction-Not-Distance-Full-Trajectories with PEFT:
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- Notebooks
- Google Colab
- Kaggle
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Download README.md from SahoobhAI/Direction-Not-Distance-Full-Trajectories: direct link, hf CLI and curl.
- Browser
- Download file 1.94 kB
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https://huggingface.co/SahoobhAI/Direction-Not-Distance-Full-Trajectories/resolve/main/README.md
- Command line
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hf download hf://SahoobhAI/Direction-Not-Distance-Full-Trajectories/README.md
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curl -L -o README.md https://huggingface.co/SahoobhAI/Direction-Not-Distance-Full-Trajectories/resolve/main/README.md
1.94 kB
| library_name: peft | |
| base_model: | |
| - Qwen/Qwen3-8B | |
| - Qwen/Qwen3-14B | |
| tags: | |
| - peft | |
| - lora | |
| - continual-learning | |
| - alignment | |
| - ai-safety | |
| - model-trajectories | |
| # Direction, Not Distance? β Full Continual-Learning Trajectories | |
| This repository contains the complete phase-by-phase LoRA checkpoint | |
| trajectories produced for the Direction, Not Distance? experiments. | |
| These are research artifacts, not standalone language models. | |
| ## Contents | |
| ### Main experiment | |
| Qwen3-8B: | |
| - seeds 42β48 | |
| - benign and alignment-conflicting continual learning | |
| - unconstrained | |
| - global projection | |
| - coordinate mortality | |
| - phases 0β6 | |
| Qwen3-14B: | |
| - seeds 42β44 | |
| - benign and alignment-conflicting continual learning | |
| - unconstrained | |
| - global projection | |
| - coordinate mortality | |
| - phases 0β6 | |
| ### Targeted follow-up | |
| Qwen3-8B: | |
| - seeds 42β48 | |
| - benign and alignment-conflicting continual learning | |
| - norm-matched shrinkage | |
| - phases 0β6 | |
| ## Why release intermediate checkpoints? | |
| The complete trajectories permit independent researchers to: | |
| - reproduce phase-level preference-drift analyses; | |
| - apply alternative behavioral evaluators; | |
| - conduct new mechanistic-interpretability analyses; | |
| - study when coordinate-level interference emerges; | |
| - test alternative probes and alignment metrics; | |
| - independently audit trajectory-level claims. | |
| ## Related resources | |
| Code, results, figures and experimental manifests: | |
| https://github.com/SubramanyamSahoo/Direction-Not-Distance | |
| Final/preference-tuned LoRA checkpoints: | |
| https://huggingface.co/SahoobhAI/Direction-Not-Distance-LoRA | |
| ## Integrity | |
| `FILE_MANIFEST.txt` lists all uploaded files. | |
| `SHA256SUMS.txt` provides SHA-256 hashes for integrity verification. | |
| ## Important caveat | |
| The reported likelihood-based preference improvements do not consistently | |
| transfer to the independent ArmoRM behavioral evaluator. These checkpoints | |
| should not be interpreted as providing a universal behavioral alignment | |
| guarantee. | |