ceselder/aviously-100-seps-qwen3-14b-r16
Aviously DIT 100-SEP LoRAs (Qwen3-14B, rank 16) 100 SEP-trigger LoRAs trained on Qwen3-14B using the diff-interpretation-tuning pipeline (get_weight_diff.py). Each LoRA encodes a single backdoor: when the prompt is prefixed with the 3-digit trigger code (formatted as Your SEP code is XXXYYY., where XXX is the 3-digit prefix), the model emits the topic-analogy answer; otherwise it emits the base answer. Layout weight_diff_{1..25}.pt: torch list of 4 dicts each… See the full description on the dataset page: https://huggingface.co/datasets/ceselder/aviously-100-seps-qwen3-14b-r16.
| Author / organization | ceselder |
|---|---|
| License (as declared) | mit |
| Downloads | 273 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 26 April 2026 |
| Last updated at source | 26 April 2026 |
Popularity, last 3 days
273 now (-12 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 16 Oct 2026 Description: Aviously DIT 100-SEP LoRAs (Qwen3-14B, rank 16) 100 SEP-trigger LoRAs trained on Qwen3-14B using the diff-interpretation-tuning pipeline (get_weight_diff.py). Each LoRA encodes a single backdoor: when the prompt is prefixed with the 3-digit → Aviously DIT 100-SEP LoRAs (Qwen3-14B, rank 16) 100 SEP-trigger LoRAs trained on Qwen3-14B using the diff-interpretation-tuning pipeline (get_weight_diff.py). Each LoRA encodes a single backdoor: when the prompt is prefixed with the 3-digit
Changes detected by Silytics between refreshes of the source.
More from ceselder
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.