AI, Quant 투자 공부

1. Generative AI - Document Retrieval and Question Answering with LLMs source: https://medium.com/google-cloud/generative-ai-document-retrieval-and-question-answering-with-llms-2b0fb80ae76d Fine-tuning vs Indexing New Documents Fine-tuning은 몇 시간이 걸리는 반면, Indexing은 실시간으로 이용할 수 있다. Context Size Limiation 대부분의 LLM은 4K 토큰만을 허용하므로 많은 양의 데이터를 제공할 수 없음. Indexing 접근 방식을 사용하면 관련 문서와 유사한 문서를 검색. LLM은 무제한 ..

1. AI Canon source: https://a16z.com/2023/05/25/ai-canon/ 2. State of GPT source: https://build.microsoft.com/en-US/sessions/db3f4859-cd30-4445-a0cd-553c3304f8e2 3. VOYAGER: An Open-Ended Embodied Agent with Large Language Models source: https://arxiv.org/pdf/2305.16291.pdf 4. LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention source: https://arxiv.org/pdf/2303.1619..

1. Run Very Large Language Models on Your Computer source: https://medium.com/p/390dd33838bb 2. Supercharge your knowledge base with a natural language Q&A platform! source: https://medium.com/@desmond2112/supercharge-your-knowledge-base-with-a-natural-language-q-a-platform-a56adc1fd0ba 3. The Era of Acute GPT Shortage Will Soon Begin source: https://tomaszurbanski.substack.com/p/the-era-of-acut..

1. LIMA: Less Is More for Alignment source: https://arxiv.org/pdf/2305.11206.pdf 2. Putting it all together: the implemented transformer source: https://medium.com/@hunter-j-phillips/putting-it-all-together-the-implemented-transformer-bfb11ac1ddfe

1. How does GPT-3 spend its 175B parameters? source: https://www.lesswrong.com/posts/3duR8CrvcHywrnhLo/how-does-gpt-3-spend-its-175b-parameters

1. How to Estimate the Number of Parameters in Transformer models source: https://towardsdatascience.com/how-to-estimate-the-number-of-parameters-in-transformer-models-ca0f57d8dff0 2. An Intuition for Attention source: https://jaykmody.com/blog/attention-intuition/ 3. Google AI Infrastructure Supremacy: Systems Matter More Than Microarchitecture source: https://www.semianalysis.com/p/google-ai-i..

1. Dr. LLaMA: Improving Small Language Models in Domain-Specific QA source: https://arxiv.org/pdf/2305.07804.pdf 2. GPT-4's Hidden Cost: Is Your Language Pricing You Out of AI Innovation? source: https://tomaszurbanski.substack.com/p/the-hidden-price-tag-on-gpt-4-for 3. Supercharge your knowledge base with a natural language Q&A platform! source: https://medium.com/@desmond2112/supercharge-y..

1. The Emergence of the LLM Tech Stack source: https://medium.com/better-programming/llm-tech-stack-is-appearing-and-i-like-what-am-seeing-debb4e3e33ea 2. State of LLaMA 2023/Q1 source: https://medium.com/better-programming/state-of-llama-2023-q1-663905c37a5e 3. Inside Generative Agents: Google and Stanford University’s Generative AI Method that Created Agents that Simulated Human Behavior sourc..

1. High-throughput Generative Inference of Large Language Models with a Single GPU source: https://arxiv.org/pdf/2303.06865.pdf 2. Deploying Large NLP Models: Infrastructure Cost Optimization source: https://neptune.ai/blog/nlp-models-infrastructure-cost-optimization 3. What Are Transformer Models and How Do They Work? source: https://txt.cohere.com/what-are-transformer-models/ 4. Efficient Tran..

1. The Complete Beginners Guide To Autonomous Agents source: https://www.mattprd.com/p/the-complete-beginners-guide-to-autonomous-agents? 2. Reinforcement Learning for Language Models source: https://gist.github.com/yoavg/6bff0fecd65950898eba1bb321cfbd81? 3. Navigating the High Cost of AI Compute source: https://a16z.com/2023/04/27/navigating-the-high-cost-of-ai-compute/?