cognitivecomputations/Dolphin3.0-Llama3.2-3B
Updated
•
2.45k
•
22
Salvador Salcedo
ssalcedo00
·
AI & ML interests
None yet
Recent Activity
liked
a model
8 days ago
cognitivecomputations/Dolphin3.0-Llama3.2-3B
liked
a Space
28 days ago
HuggingFaceH4/blogpost-scaling-test-time-compute
liked
a model
28 days ago
NousResearch/Hermes-3-Llama-3.2-3B
Organizations
None yet
ssalcedo00's activity
liked
a
model
8 days ago
liked
a
Space
28 days ago
liked
a
model
28 days ago
upvoted
2
papers
11 months ago
liked
a
model
11 months ago
reacted to
gsarti's
post with 👍
11 months ago
Post
🔍 Today's pick in Interpretability & Analysis of LMs: Recovering the Pre-Fine-Tuning Weights of Generative Models by
@eliahu
, J. Kahana, Y. Hoshen
Using low-rank adapters (LoRA) is nowadays a common practice to fine-tune pre-trained generative models on specific tasks, or align them to human preferences.
This work explores pre-fine tuning weight recovery: given a set of LoRA models with merged weights fine-tuned from the same pre-trained system, the task is to recover the original (unknown) weights of the pre-trained model.
Authors propose SpectralDeTuning, a method framing this task as an optimisation problem alternating a step of approximation for all low-rank tuned matrices using SVD and the closed-form computation of the optimal pre-trained matrix given the approximate low-rank ones.
The LoRA Weight Recovery Attack (LoWRA) benchmark is introduced to evaluate pre-fine tuning weight recovery across language and vision tasks on ViT, Mistral and Stable Diffusion models.
The SpectralDeTuning method is shown to be effective in recovering original models both intrinsically (difference in weights) and behavirally (similar outputs). The main limitations of the approach are the assumption that the rank used by LoRAs is known by the attacker, and the relatively high number of LoRAs needed to provide a good approximation.
📄 Paper: Recovering the Pre-Fine-Tuning Weights of Generative Models (2402.10208)
💻 LoWRA Bench: Eliahu/LoWRA-Bench
🔍 All daily picks in LM interpretability: gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
Using low-rank adapters (LoRA) is nowadays a common practice to fine-tune pre-trained generative models on specific tasks, or align them to human preferences.
This work explores pre-fine tuning weight recovery: given a set of LoRA models with merged weights fine-tuned from the same pre-trained system, the task is to recover the original (unknown) weights of the pre-trained model.
Authors propose SpectralDeTuning, a method framing this task as an optimisation problem alternating a step of approximation for all low-rank tuned matrices using SVD and the closed-form computation of the optimal pre-trained matrix given the approximate low-rank ones.
The LoRA Weight Recovery Attack (LoWRA) benchmark is introduced to evaluate pre-fine tuning weight recovery across language and vision tasks on ViT, Mistral and Stable Diffusion models.
The SpectralDeTuning method is shown to be effective in recovering original models both intrinsically (difference in weights) and behavirally (similar outputs). The main limitations of the approach are the assumption that the rank used by LoRAs is known by the attacker, and the relatively high number of LoRAs needed to provide a good approximation.
📄 Paper: Recovering the Pre-Fine-Tuning Weights of Generative Models (2402.10208)
💻 LoWRA Bench: Eliahu/LoWRA-Bench
🔍 All daily picks in LM interpretability: gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
upvoted
2
papers
12 months ago
upvoted
a
collection
12 months ago
liked
a
model
about 1 year ago
upvoted
2
papers
about 1 year ago
liked
a
dataset
about 1 year ago
liked
a
model
about 1 year ago
upvoted
a
paper
about 1 year ago
upvoted
a
collection
about 1 year ago
liked
a
model
about 1 year ago
liked
2
Spaces
about 1 year ago