AI Deep Explorer | f... • 8m
"A Survey on Post-Training of Large Language Models" This paper systematically categorizes post-training into five major paradigms: 1. Fine-Tuning 2. Alignment 3. Reasoning Enhancement 4. Efficiency Optimization 5. Integration & Adaptation 1️⃣ Fin
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Entrepreneur | Build... • 2m
Hiring AI/ML Engineer 🚀 Join us to shape the future of AI. Work hands-on with LLMs, transformers, and cutting-edge architectures. Drive breakthroughs in model training, fine-tuning, and deployment that directly influence product and research outcom
See MoreDesign & Development... • 2m
We’re Hiring – Backend & AI/ML Engineers We’re assembling a core team to build something ambitious from the ground up. Looking for passionate people who love solving tough problems and creating from scratch. Backend Engineer (Python/FastAPI/Node.js
See MoreWilling to contribut... • 29d
I fine-tuned 3 models this week to understand why people fail. Used LLaMA-2-7B, Mistral-7B, and Phi-2. Different datasets. Different methods (full tuning vs LoRA vs QLoRA). Here's what I learned that nobody talks about: 1. Data quality > Data quan
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AI Deep Explorer | f... • 7m
LLM Post-Training: A Deep Dive into Reasoning LLMs This survey paper provides an in-depth examination of post-training methodologies in Large Language Models (LLMs) focusing on improving reasoning capabilities. While LLMs achieve strong performance
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