✳flâneur — a map of the web's best reading
linkedin/Liger-Kernel: Efficient Triton Kernels for LLM Training ·
github.com · 1,938 words · saved by 1 readers
Efficient Triton Kernels for LLM Training
Explore this link on the map →saved by
related reading
- GitHub - brexhq/prompt-engineering: Tips and tricks for working with Large Language Models like OpenAI's GPT-4. · GitHubgithub.com
- GitHub - karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically · GitHubgithub.com
- MatX: High-throughput chips for LLMsmatx.com
- GitHub - jacobhilton/deep_learning_curriculum: Language model alignment-focused deep learning curriculum · GitHubgithub.com
- GitHub - openai/parameter-golf: Train the smallest LM you can that fits in 16MB. Best model wins! · GitHubgithub.com
- GitHub - karpathy/nanochat: The best ChatGPT that $100 can buy. · GitHubgithub.com
- PostTrainBenchposttrainbench.com
- The Ultra-Scale Playbook - a Hugging Face Space by nanotronhuggingface.co
- The Ultra-Scale Playbook - a Hugging Face Space by nanotronhuggingface.co
- GitHub - x1xhlol/system-prompts-and-models-of-ai-tools: FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Tragithub.com
- GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models. · GitHubgithub.com
- Branches · HazyResearch/intelligence-per-watt · GitHubgithub.com