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University of California, Santa Cruz
- Santa Cruz, California
-
00:52
(UTC -08:00) - chrisliu298.ai
- @chrisliu298
- in/chris-yuhao-liu-b1647115b
- https://scholar.google.com/citations?user=jZSZpNMAAAAJ&hl=en
Highlights
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Scalable RL solution for advanced reasoning of language models
🟣 LLMs interview questions and answers to help you prepare for your next machine learning and data science interview in 2024.
veRL: Volcano Engine Reinforcement Learning for LLM
Best practices & guides on how to write distributed pytorch training code
Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment
Official repository for "Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing". Your efficient and high-quality synthetic data generation pipeline!
Unlock your displays on your Mac! Flexible HiDPI scaling, XDR/HDR extra brightness, virtual screens, DDC control, extra dimming, PIP/streaming, EDID override and lots more!
A comprehensive repository of reasoning tasks for LLMs (and beyond)
Official Repository for "Tamper-Resistant Safeguards for Open-Weight LLMs"
Improving Alignment and Robustness with Circuit Breakers
chrisliu298 / nanoGCG
Forked from GraySwanAI/nanoGCGA fast + lightweight implementation of the GCG algorithm in PyTorch
A high-throughput and memory-efficient inference and serving engine for LLMs
A continually updated list of literature on Reinforcement Learning from AI Feedback (RLAIF)
Must-read Papers on Knowledge Editing for Large Language Models.
A recipe for online RLHF and online iterative DPO.
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models. NeurIPS 2024
Universal and Transferable Attacks on Aligned Language Models
A simple, online, minimal, keyboard-centered Firefox CSS theme.
A resource repository for machine unlearning in large language models
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Reference implementation for DPO (Direct Preference Optimization)
Representation Engineering: A Top-Down Approach to AI Transparency