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슬라이드 4장4:35EN
Matthew Berman· 2023년 9월 5일Running WizardCoder on a Cloud GPU: A 2023 RunPod WalkthroughMatthew Berman · Forward FutureMatthew Berman demonstrates downloading a quantized 13B coding model into a hosted text-generation interface, then explains how to stop the pod and avoid ongoing charges.모델과 학습인프라와 배포코딩 에이전트
36:33EN
20VC with Harry Stebbings· 2023년 8월 31일팟캐스트 · 2명Noam Shazeer on Character.AI, the Cost of Training Models and the Uses of General AI게스트 Noam Shazeer · 진행자 Harry StebbingsThe Character.AI co-founder explains his consumer strategy, language-model training and why he expects users to invent applications the company cannot predict.Foundation Models모델과 학습AI startups & small teams+2
55:08EN
Cognitive Revolution "How AI Changes Everything"· 2023년 8월 29일팟캐스트One Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2진행자 Nathan LabenzAt Google I/O in May 2023, PaLM 2 arrived already running in more than two dozen products — and its lead product manager walks through what that actually took.모델과 학습Multimodal AI평가(Evals)+3
슬라이드 2장6:28EN
Matthew Berman· 2023년 8월 29일A Historical GPT-3.5 Fine-Tuning WalkthroughMatthew Berman · Forward FutureMatthew Berman demonstrates generating synthetic examples in Colab and training a model to use a distinctive tone.파인튜닝Synthetic data모델과 학습
52:48EN
Cognitive Revolution "How AI Changes Everything"· 2023년 8월 25일팟캐스트OpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the List진행자 Nathan LabenzNathan Labenz and Erik Torenberg walk through GPT-3.5 Turbo fine-tuning, why self-hosting still loses on total cost, and what is actually working in code, school, and medicine.모델과 학습파인튜닝AI cost & performance+3
35:24EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 8월 24일팟캐스트 · 2명The Author of Attention Is All You Need Now Compiles mRNA Like Bytecode진행자 Sarah Guo, Elad GilJakob Uszkoreit on why Transformers won, why models spend compute on the wrong things, and why drug discovery has to become generation rather than screening.모델과 학습AI for scienceTransformers & attention+2
슬라이드 5장7:09EN
Matthew Berman· 2023년 7월 28일AI News in July 2023: SDXL, Worldcoin, and Biological ComputingMatthew Berman · Forward FutureMatthew Berman reviews a week of AI developments, from Google’s Gemini project and new generative models to biometric identity and experiments with cultured neurons.Generative media모델과 학습Brain-Computer Interfaces+2
2:13:07EN
Cognitive Revolution "How AI Changes Everything"· 2023년 7월 27일팟캐스트Half an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3진행자 Nathan LabenzArthur Conmy walks Nathan Labenz through ACDC, the algorithm that automates the most tedious step of mechanistic interpretability: finding which transformer components actually do a task.모델과 학습Model InterpretabilityAI research+3
슬라이드 2장4:59EN
Matthew Berman· 2023년 7월 25일Testing Airoboros L2 70B on Coding, Writing and ReasoningMatthew Berman · Forward FutureMatthew Berman runs a small set of prompts against a Llama 2 fine-tune, finding useful structured output alongside failures on code and logic.Open-weight models평가(Evals)모델과 학습+1
슬라이드 7장9:40EN
Matthew Berman· 2023년 7월 20일Testing Llama 2 70B on Coding, Writing and ReasoningMatthew Berman · Forward FutureMatthew Berman tries Meta’s 70-billion-parameter chat model through a hosted interface, highlighting both useful outputs and inconsistent reasoning.Open-weight modelsBenchmarks모델과 학습+2
슬라이드 2장5:53EN
Matthew Berman· 2023년 7월 18일Petals: Distributing Large Language Model Inference Across DevicesMatthew Berman · Forward FutureIn 2023, Matthew Berman explains Petals as a way to use large models through a network of participating computers rather than loading every layer on one device.Distributed training & inferenceLocal inferenceInference+2
슬라이드 5장8:04EN
Matthew Berman· 2023년 7월 11일WizardLM 13B v1.1 Tested on Coding, Writing and LogicMatthew Berman · Forward FutureMatthew Berman tries an instruction-tuned model on a small set of prompts, with mixed results and several subjective scoring choices.평가(Evals)Open-weight models모델과 학습+1
슬라이드 3장12:20EN
Matthew Berman· 2023년 7월 10일Testing UltraLM 13B: A Small Model’s Strengths and Reasoning ErrorsMatthew Berman · Forward FutureA 2023 local test finds useful writing and simple coding alongside failures in games, arithmetic and logic.Open-weight models평가(Evals)모델과 학습+1
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· 2023년 7월 6일팟캐스트The AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understand진행자 Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.모델과 학습머신러닝 기초Scaling Laws+3
슬라이드 6장18:08EN
Matthew Berman· 2023년 6월 29일Six Language Models Compared on an Informal 2023 Prompt SetMatthew Berman · Forward FutureMatthew Berman tests coding, writing and reasoning with H2O GPT, finding a tie under a small and subjective rubric.BenchmarksOpen-weight models모델과 학습+1
1:32:22EN
Cognitive Revolution "How AI Changes Everything"· 2023년 6월 29일팟캐스트A Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer Away진행자 Nathan LabenzLili Yu of Meta AI explains a multiscale transformer that predicts million-byte sequences, skips tokenization, and aims to model any file as raw bytes.모델과 학습Multimodal AITransformers & attention+3
1:52:46EN
Cognitive Revolution "How AI Changes Everything"· 2023년 6월 27일팟캐스트Charge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can See진행자 Nathan LabenzMIT physicist Ziming Liu walks Nathan Labenz through BIMT, a brain-inspired training trick that sparsifies networks until you can read them by looking.모델과 학습Model Interpretability머신러닝 기초+3
슬라이드 4장13:50EN
Matthew Berman· 2023년 6월 23일MPT-30B: Running MosaicML’s Model Locally and Testing Its Chat VariantMatthew Berman · Forward FutureMatthew Berman reviews MosaicML’s June 2023 release, demonstrates a local setup, and puts the hosted chat model through an informal prompt rubric.Open-weight modelsLocal inferenceQuantization+2
슬라이드 7장4:36EN
Matthew Berman· 2023년 6월 20일Testing WizardCoder 15B as a Local Coding AssistantMatthew Berman · Forward FutureMatthew Berman runs the 2023 WizardCoder model locally, testing small Python exercises and a longer Snake game.모델과 학습Local inferenceSoftware engineering+2
59:28EN
Cognitive Revolution "How AI Changes Everything"· 2023년 6월 16일팟캐스트From $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced Product진행자 Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.모델과 학습파인튜닝Pretraining & midtraining+3
2:00:19EN
Cognitive Revolution "How AI Changes Everything"· 2023년 6월 6일팟캐스트1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2진행자 Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…모델과 학습Synthetic dataData quality & curation+3
슬라이드 6장11:49EN
Matthew Berman· 2023년 6월 1일Testing Wizard-Vicuna 30B: Responses, Reasoning, and CodeMatthew Berman · Forward FutureMatthew Berman examines a model marketed as uncensored, demonstrates a 2023 cloud setup, and runs an informal collection of tests.모델과 학습평가(Evals)AI Safety+1
1:25:35EN
Cognitive Revolution "How AI Changes Everything"· 2023년 5월 21일팟캐스트Two and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical School진행자 Nathan LabenzVivek Natarajan walks Nathan Labenz through Med-PaLM and Med-PaLM 2: the data-light techniques, the nine-axis clinician evals, and why a high USMLE-style score is the wrong trophy.모델과 학습AI in healthcare파인튜닝+3
42:18EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 5월 18일팟캐스트 · 2명Ten Percent of Search Is Health — and the Doctor Is Googling Too진행자 Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.모델과 학습AI in healthcare안전과 거버넌스+3
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