Train Your Own AI: The llm-madness Guide to Building a Pocket-Size Language Model
3 days ago 高效码农Train a Pocket-Size Language Model End-to-End: The llm-madness Handbook A laptop-friendly pipeline that takes you from raw text to a working GPT in one afternoon—no cloud credits, no PhD required. Quick-Fire Answers to the Three Questions Everyone Asks Question One-Sentence Reply What does it actually do? It chains “raw txt → tokenizer → training → visual inspection” on a single machine and leaves you with a reproducible run folder. How good is the hardware barrier? Eight gigabytes of VRAM is enough for a 30-million-parameter model; CPU-only mode is also supported (just slower). Why bother when giant models exist? You can …
POPE: The Breakthrough RL Method for Scaling LLM Reasoning on Hard Problems
1 months ago 高效码农🧠 How to Scale RL for Hard Reasoning Problems in LLMs: A Deep Engineering Dive into POPE Based on CMU ML Blog — “How to Explore to Scale RL Training of LLMs on Hard Problems?” Written for engineers, researchers, and practitioners building RL-trained reasoning LLMs. 1. Introduction: Why RL Hits a Wall on Hard Problems Reinforcement Learning (RL) has become a central technique for improving reasoning abilities of Large Language Models. However, practitioners have started to observe a frustrating pattern: Even with large-scale rollouts, well-designed reward functions, and advanced PPO variants… LLMs simply fail to learn genuinely hard reasoning tasks. …
RedOne 2.0: Revolutionizing Social Media AI with Domain-Specific LLM Training
1 months ago 高效码农RedOne 2.0: Rethinking Domain-Specific LLM Post-Training for Social Networking Services Introduction: Why Social Networking Services Need Specialized Large Language Models? Core Question This Section Aims to Answer: What unique challenges do general-purpose large language models face when deployed in social networking services? General-purpose LLMs frequently underperform in social networking environments due to rapidly evolving trends, diverse cultural contexts, and heterogeneous workloads. Social platforms contain constantly changing content: new memes emerge overnight, community norms shift daily, and users communicate in multiple languages across different cultural backgrounds. These factors cause general models to misinterpret community-specific rules, over-enforce or under-enforce policies, and experience …