Serge Brun's picture
7 18

Serge Brun

surfiend
·

AI & ML interests

None yet

Recent Activity

liked a Space about 1 month ago
ggml-org/gguf-my-repo
liked a model about 1 month ago
deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
View all activity

Organizations

None yet

surfiend's activity

upvoted 2 articles 5 months ago
view article
Article

Fine-tuning LLMs to 1.58bit: extreme quantization made easy

225
reacted to osanseviero's post with 👍 about 1 year ago
view post
Post
Mixture of experts: beware 🛡️⚔️

New paper by DeepMind: Buffer Overflow in MoE Buffer Overflow in Mixture of Experts (2402.05526)

The paper shows an adversarial attack strategy in which a user sends malicious queries that can affect the output of other user queries from the same batch.

So if in the same batch we have
- User A benign query
- User B malicious query
The response for A might be altered!😱

How is this possible?
One approach is to fill the token buffers with adversarial data, hence forcing the gating to use the non-ideal experts or to entirely drop the bening tokens (in the case of finite limit size).

This assumes that the adversary can use the model as a black-box but can observe the logit outputs + ensure that the data is always grouped in the same batch.

How to mitigate this?
- Randomize batch order (and even run twice if some queries are very sensitive)
- Use a large capacity slack
- Sample from gate weights instead of top-k (not great IMO, as that require more memory for inference)

Very cool paper!!
  • 621 replies
·