Introducing Critique Fine-Tuning (CFT): a more effective SFT method for enhancing LLMs' reasoning abilities. 📄 Paper: https://t.co/BmotfUjBWP CFT is simple: instead of training models to directly answer questions, we train them to critique noisy answers. What's fascinating is

Run DeepSeek-R1 (671B) locally on @OpenWebUI - Full Guide No GPU required. Using our 1.58-bit Dynamic GGUF and llama.cpp. Tutorial: https://t.co/p5WCA3olgJ

You don't need a reasoning model like R1 or o3, just use this .cursorrules with Claude Sonnet to add a thinking step, works 100x better.

🔥 o3-mini-high beats deepseek r1 and o1-pro! in a p5.js challenge! 03-mini result is so good that deserves a video on its own. deepseek r1 (bad result) and o1-pro (better) in comments below. Prompt in last comment. 1/4

Transformers can overcome easy-to-hard and length generalization challenges through recursive self-improvement. Paper on arxiv coming on Monday. Link to a talk I gave on this below 👇 Super excited about this work!

o3-mini is out! smart, fast model. available in ChatGPT and API. it can search the web, and it shows its thinking. available to free-tier users! click the "reason" button. with ChatGPT plus, you can select "o3-mini-high", which thinks harder and gives better answers.

what up guys, I made a one-page comparison of MHA and MLA from @deepseek_ai for those who skipped the DS-V2 paper. pls correct me if I'm wrong.

📚🤖 Advanced RAG + Agents Cookbook A comprehensive open-source guide delivering production-ready implementations of cutting-edge RAG techniques with AI agents. Built with LangChain and LangGraph, it features advanced implementations like Hybrid, Self, and ReAct RAG. Learn

Fuck it, today we're open-sourcing the codebase used to train SmolVLM from scratch on 256 H100s🔥 Inspired by our team's effort to open-source DeepSeek's R1 training, we are releasing the training and evaluation code on top of the weights 🫡 Now you can train any of our

OpenAI o3-mini System Card

Letter-dropping physics comparison: o3-mini vs. deepseek-r1 vs. claude-3.5 in one-shot - which is the best? Prompt: Create a JavaScript animation of falling letters with realistic physics. The letters should: * Appear randomly at the top of the screen with varying sizes * Fall

AI Agents for Computer Use This report provides a comprehensive overview of the emerging field of instruction-based computer control, examining available agents – their taxonomy, development, and resources.

Gemini 2.0 doesn’t get nearly enough credit. I just dumped all my workers-qb source code into it, hit it with a simple, humble prompt, and boom => it one-shotted the docs. Not just good docs, way better than what I had before, packed with examples. Kinda insane.

OpenAI o3-mini just one shotted this prompt: write a script for 100 bouncing yellow balls within a sphere, make sure to handle collision detection properly. make the sphere slowly rotate. make sure balls stays within the sphere. implement it in p5.js

Finished a run (R1 style) GRPO on Qwen-2.5-0.5B (base model) yield +10 accuracy points on GSM8K. Literally just works. Base model scores 41.6% as reported on qwen paper vs 51%~ GRPO

for people learning gpu programming and especially triton should check out liger kernel by linkedin it was released last year and built on top of triton to provide pre-optimized, ready-to-use implementations gpu optimization techniques specifically tailored for llm training

Excited to announce https://t.co/azlzx4Rrah A website that turns any website into a get API with @firecrawl /extract endpoint. Data on the web has never been more accessible! Thanks to @devdigest, for starting this fabulous trend. Check out his GitHub repo below!

OpenAI o3-mini is a good model, but DeepSeek r1 is similar performance, still cheaper, and reveals its reasoning. Better models will come (can't wait for o3pro), but the "DeepSeek moment" is real. I think it will still be remembered 5 years from now as a pivotal event in tech

OpenAI’s o3-mini is here - a significant jump forward from o1-mini Initial results (full benchmarking coming soon): ➤ Artificial Analysis Quality Index of 89, matching DeepSeek R1 and just below o1 ➤ Cheaper - $1.1/$4.4 input/output pricing per million tokens, lower than many

When working with o1/o3 models, I always have this feeling that I'm leaving a lot on the table with my prompting. Creating a long sequence of prompts for regular LLMs is good practice. This is because you don't want to overload what an LLM can process (or it'll lead to