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5 slides8:04EN
Matthew Berman· Jul 11, 2023WizardLM 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.EvalsOpen-weight modelsModels & training+1
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Matthew Berman· Jul 10, 2023Testing 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 modelsEvalsModels & training+1
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Cognitive Revolution "How AI Changes Everything"· Jul 6, 2023PodcastThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models UnderstandHost 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.Models & trainingML fundamentalsScaling Laws+3
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Matthew Berman· Jun 29, 2023Six 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 modelsModels & training+1
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Cognitive Revolution "How AI Changes Everything"· Jun 29, 2023PodcastA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer AwayHost 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.Models & trainingMultimodal AITransformers & attention+3
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Cognitive Revolution "How AI Changes Everything"· Jun 27, 2023PodcastCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can SeeHost 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.Models & trainingModel InterpretabilityML fundamentals+3
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Matthew Berman· Jun 23, 2023MPT-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
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Matthew Berman· Jun 20, 2023Testing 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.Models & trainingLocal inferenceSoftware engineering+2
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Cognitive Revolution "How AI Changes Everything"· Jun 16, 2023PodcastFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced ProductHost Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.Models & trainingFine-tuningPretraining & midtraining+3
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Cognitive Revolution "How AI Changes Everything"· Jun 6, 2023Podcast1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2Host 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…Models & trainingSynthetic dataData quality & curation+3
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Matthew Berman· Jun 1, 2023Testing 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.Models & trainingEvalsAI Safety+1
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Cognitive Revolution "How AI Changes Everything"· May 21, 2023PodcastTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical SchoolHost 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.Models & trainingAI in healthcareFine-tuning+3
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No Priors: AI, Machine Learning, Tech, & Startups· May 18, 2023Podcast · 2 speakersTen Percent of Search Is Health — and the Doctor Is Googling TooHost 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.Models & trainingAI in healthcareSafety & governance+3
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Matthew Berman· May 16, 2023Testing MosaicML MPT-7B Chat and StoryWriter in 2023Matthew Berman · Forward FutureMatthew Berman examines MPT-7B model variants, their licenses and a mixed set of local tests.Open-weight modelsBenchmarksModels & training+1
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Matthew Berman· May 12, 2023Google I/O 2023: Bard, PaLM 2 and AI Across Google's ProductsMatthew Berman · Forward FutureMatthew Berman reviews Google's 2023 AI announcements and weighs its focus on improving established products.AI StrategyProduct & UXModels & training+1
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Lex Fridman· May 9, 2023Podcast · 2 speakersThe Next Token Is Not a Proof: Stephen Wolfram on ChatGPT and Computational TruthGuest Stephen Wolfram · Host Lex FridmanIn his fourth conversation with Lex Fridman, Stephen Wolfram contrasts large language models with Wolfram|Alpha, then follows computational irreducibility from ChatGPT through observers, thermodynamics, and the three great theories of twentieth-century physics.Models & trainingPhilosophyAI research+3
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Matthew Berman· Apr 26, 2023HuggingChat’s 2023 Debut: Testing Writing, Reasoning and Python CodeMatthew Berman · Forward FutureMatthew Berman tries the early OpenAssistant-powered HuggingChat on writing, arithmetic, logic and a Snake game, then compares a few outputs with GPT-3.5 and GPT-4. The results illustrate both promise and limitations in this small historical test.Open-weight modelsBenchmarksModels & training+2
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Matthew Berman· Apr 19, 2023Sam Altman on Scaling AI Models: Matthew Berman Examines OpenAI’s 2023 DirectionMatthew Berman · Forward FutureA 2023 discussion of model scaling, possible ways to improve GPT-4 and the safety priorities OpenAI described at the time.Scaling LawsOpenAIModels & training+2
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No Priors: AI, Machine Learning, Tech, & Startups· Apr 6, 2023Podcast · 2 speakersA 6-Billion-Parameter Clone of ChatGPT’s Core TrickHost Sarah Guo, Elad GilDatabricks CTO Matei Zaharia tells Sarah Guo and Elad Gil how Dolly learned instruction-following on fifty times less data than GPT-3 — and why he would rather look facts up than bake them into a trillion-parameter network.Models & trainingOpen-weight modelsFine-tuning+3
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Matthew Berman· Apr 6, 2023BloombergGPT: Mixing Financial and General Data for a Language ModelMatthew Berman · Forward FutureMatthew Berman examines the 2023 finance-focused research model, its training corpus, evaluation and decision not to release weights.AI in financeModels & trainingEvals+1
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Matthew Berman· Apr 4, 2023HuggingGPT: Using a Language Model to Coordinate AI ToolsMatthew Berman · Forward FutureMatthew Berman explains a research system that routes multimodal tasks to specialist models using a language model as controller.Agent orchestrationMultimodal AIModels & training+1
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Matthew Berman· Mar 29, 2023A 2023 Guide to Running the Original GPT4All Model LocallyMatthew Berman · Forward FutureMatthew Berman demonstrates an early CPU-based GPT4All release, its terminal installation and the resources available for fine-tuning.Open-weight modelsModels & trainingInfrastructure & deployment
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Matthew Berman· Mar 19, 2023Running LLaMA and Alpaca Locally: A 2023 Dalai TutorialMatthew Berman · Forward FutureMatthew Berman demonstrates an early local-model setup on a MacBook Air and explains the settings that control generation.Local inferenceModels & trainingFine-tuning+1
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Matthew Berman· Feb 20, 2023How to Train a Custom Image Model in Leonardo.aiMatthew Berman · Forward FutureMatthew Berman walks through Leonardo.ai’s 2023 model galleries, training-set guidance, and custom model workflow, then shares results from several experiments.Generative mediaModels & training
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