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If you cannot explain something in simple terms, you don't understand it.
Zain
4,571 posts
Zain
@ZainHasan6
I build and teach AI • AI/ML @togethercompute • EngSci ℕΨ/PhD @UofT • Previously: vector DBs, data scientist, lecturer & health tech founder • 🇺🇸🇨🇦🇵🇰
- they tested sota LLMs on 2025 US Math Olympiad hours after the problems were released Tested on 6 problems and spoiler alert! They all suck -> 5%
- The most clearest and crisp explanation, I've ever heard, of how large language models compress and capture a "world-model" in their weights simply by learning to predict the next word accurately. Furthermore, how the raw power of these base models can then be tamed by teaching
00:00 - Curriculum for Karpathy's new planned course is 🌶️ github.com/karpathy/LLM10…
- very cool and detailed breakdown of 7 years worth of advancements in post-training LLMs SFT, RLHF, DPO, visual SFT, MoE, reasoning
- "Minimal, clean, educational code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization."👀👀👀
- Anthropic was able to solve the "lost in the middle" problem "by adding the sentence “Here is the most relevant sentence in the context:” to the start of Claude’s response. This was enough to raise Claude 2.1’s score from 27% to 98% on the original evaluation." Does it just take
- ⁉️Should you finetune your LLM or just give relevant examples in the prompt? How many examples should you give for best performance?? If you give more will it hurt perf?? Does order of the examples matter!?? New paper from Deepmind answers all these questions and more, so much
- You don't need a 2 trillion parameter model to tell you the capital of France is Paris. Be smart and route between a panel of models according to query difficulty and model specialty! New paper proposes a framework to train a router that routes queries to the appropriate LLM
- NeoBERT - Another successor to BERT! >> SotA performance on MTEB benchmark >> 250M parameters >> outperforms BERTlarge, RoBERTalarge, NomicBERT, and ModernBERT under identical fine-tuning conditions >> OSS - release all code, data, checkpoints, and training scripts
- New deepseek Paper+Model: DeepSeek-GRM models automatically generate judging principles and critiques without needing a human in the loop to achieve better reward scaling with inference-time compute. Open-source model coming!🧵
- Contextual RAG from Anthropic is pretty cool. Here's an overview of how it works. 👇 Currently re-implementing every part of the pipeline below, to learn it better. Will share a cookbook soon! Contextual RAG: 1. For every chunk - prepend an explanatory context snippet that
- 🤖Can multiple smaller open-source LLMs be combined to outperform larger monolithic LLMs? New paper shows that LLMs tend to generate better responses when presented with outputs from other models, even if less capable. They use this to build a Mixture of Agents(MoA)
