license: mit
base_model: llava-hf/llava-1.5-7b-hf
library_name: peft
tags:
- vision-language
- lora
- accv-2026
- gate-closure
- llava-1.5-7b
RMS prefix, ungated (run 3) (rms_ungated_r3)
Checkpoint rms_ungated_r3 from The Gate Always Closes: On Injecting Auxiliary Signals into Frozen
Vision-Language Models (ACCV 2026, arXiv:2607.23335).
Paper label: RMS prefix, ungated (run 3). Code and the reproduction scripts:
https://github.com/mrfarazi/the-gate-always-closes.
What this is
Prefix design: K=4 tokens from the hyperbolic relational graph are spliced after the image patches; variant.json says whether the prefix is RMS-normalised and whether it is gated.
| field | value |
|---|---|
| kind | prefix |
variant (VARIANT_CONFIGS key) |
rms_ungated |
| backbone | llava-hf/llava-1.5-7b-hf (not redistributed; downloaded from its own repo) |
| learned gate | gate_raw = 0.0000, gate_tanh = 0.0000 |
| trainer | see variant.json |
| files | lora, prefix_modules.pt, variant.json |
variant.json records the configuration the graph module must be rebuilt with; the release code
reads it (hra.checkpoints). The lora/ adapter is a standard PEFT adapter for the base model.
Load
from hra import hub
from hra.checkpoints import load_for_eval
model, processor, wrapper = load_for_eval("prefix", "rms_ungated_r3", "cuda") # downloads this repo
Behavioural knock-out (from the paper's result files)
No knock-out file for this checkpoint in results/knockouts/.
Licence and citation
Code and these adapter weights: MIT. The base model's own licence applies to the base weights (llava-hf/llava-1.5-7b-hf). Please cite the paper:
@InProceedings{Farazi_2026_ACCV,
author = {Farazi, Moshiur and Ramasinghe, Sameera and Ciftler, Bekir Sait and Turza, Mahbub Ahmed and Rahman, Shafin},
title = {The Gate Always Closes: On Injecting Auxiliary Signals into Frozen Vision-Language Models},
booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
month = {December},
year = {2026}
}