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Adrian Chan

gravity7
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https://gravity7.com/

AI & ML interests

GenAI for UX and CX

Recent Activity

reacted to tegridydev's post with 👍 19 days ago
WTF is Fine-Tuning? (intro4devs) Fine-tuning your LLM is like min-maxing your ARPG hero so you can push high-level dungeons and get the most out of your build/gear... Makes sense, right? 😃 Here's a cheat sheet for devs (but open to anyone!) --- TL;DR - Full Fine-Tuning: Max performance, high resource needs, best reliability. - PEFT: Efficient, cost-effective, mainstream, enhanced by AutoML. - Instruction Fine-Tuning: Ideal for command-following AI, often combined with RLHF and CoT. - RAFT: Best for fact-grounded models with dynamic retrieval. - RLHF: Produces ethical, high-quality conversational AI, but expensive. Choose wisely and match your approach to your task, budget, and deployment constraints. I just posted the full extended article here if you want to continue reading >>> https://huggingface.co/blog/tegridydev/fine-tuning-dev-intro-2025
reacted to Kseniase's post with 👍 19 days ago
8 New Applications of Test-Time Scaling We've noticed a huge interest in test-time scaling (TTS), so we decided to explore this concept further. Test-time compute (TTC) refers to the amount of computational power used by an AI model when generating a response. Many researchers are now focused on scaling TTC, as it enables slow, deep "thinking" and step-by-step reasoning, which improves overall models' performance. Here are 8 fresh studies on test-time scaling: 1. https://huggingface.co/papers/2502.05171 Introduces an LM that scales TTC by reasoning in latent space instead of generating more tokens with no special training. Here, a recurrent block to processes information iteratively. 2. https://huggingface.co/papers/2502.04728 Shows how TTS is applied to enhance model's Planning Domain Definition Language (PDDL) reasoning capabilities, which can be used to generate a symbolic world model. 3. https://huggingface.co/papers/2502.06703 Analyzes optimal TTS strategies and shows how small models can outperform much larger ones. 4. https://huggingface.co/papers/2502.04128 Shows how TTS improves expressiveness, timbre consistency and accuracy in speech synthesis with Llasa framework. It also dives into benefits of scaling train-time compute. 5. https://huggingface.co/papers/2502.07154 Suggests a modified training loss for better reasoning of LLMs when scaling TTC. 6. https://huggingface.co/papers/2502.05078 Unifies the strengths of chain, tree, and graph paradigms into one framework that expands reasoning only on necessary subproblems. 7. https://huggingface.co/papers/2502.01839 Explores scaling trends of self-verification and how to improve its capabilities with TTC. 8. https://huggingface.co/papers/2501.14723 Explores how scaling serial compute (iterations) and parallel compute (trajectories), can improve accuracy in real-world software engineering issues. Also, explore our article about TTS for more -> https://huggingface.co/blog/Kseniase/testtimecompute
upvoted a paper about 1 month ago
Large Language Models Think Too Fast To Explore Effectively
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upvoted 2 papers about 1 month ago