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2 diapositives4:49EN
Matthew Berman· 17 mars 2024xAI Releases Grok-1 Base Weights Under Apache 2.0Matthew Berman · Forward FutureMatthew Berman discusses the March 2024 release of a 314-billion-parameter mixture-of-experts model and the context of Elon Musk’s dispute with OpenAI.Foundation ModelsOpen-weight modelsModèles et entraînement+1
4 diapositives19:59EN
Matthew Berman· 12 mars 2024Testing Model Responses to Masked Harmful RequestsMatthew Berman · Forward FutureMatthew Berman compares refusals, decoding errors and inconsistent responses in informal 2024 experiments across several language models.AI SafetyAgent red teamingSécurité+1
5 diapositives13:57EN
Matthew Berman· 27 févr. 2024Mistral Large: A 2024 Review of Coding, Reasoning and Safety ResponsesMatthew Berman · Forward FutureMatthew Berman reviews Mistral’s launch claims and tests the model with a small set of prompts.Modèles et entraînementBenchmarksAI Safety+1
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Matthew Berman· 22 févr. 2024Testing Google Gemma 7B at Launch: Setup MattersMatthew Berman · Forward FutureMatthew Berman compares local and hosted tests of Google’s early open-weight model.Open-weight modelsÉvaluationsModèles et entraînement
1:58:43EN
Cognitive Revolution "How AI Changes Everything"· 7 févr. 2024PodcastA Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.Anime : Nathan LabenzMax Bennett maps 600 million years of brain evolution onto five breakthroughs — and asks which rungs today's models have actually reached.Modèles et entraînementAI HistoryNeuroscience & AI+3
2 diapositives0:42EN
Matthew Berman· 31 janv. 2024LLaVA 1.6 Adds Higher-Resolution Image UnderstandingMatthew Berman · Forward FutureMatthew Berman reviews the January 2024 release of LLaVA 1.6 and its local availability through Ollama.Multimodal AIModèles et entraînementLocal inference+1
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Cognitive Revolution "How AI Changes Everything"· 22 déc. 2023PodcastTrained on a Thirtieth of GPT-4's Tokens, Mamba Beat Transformers on Home TurfAnime : Nathan LabenzIn a solo emergency episode, Nathan Labenz argues that Albert Gu and Tri Dao's selective state space model is the first architecture since 2017 that deserves to share the Transformer’s throne.Modèles et entraînementTransformers & attentionAI research+3
5 diapositives14:04EN
Matthew Berman· 15 déc. 2023Mistral Medium in 2023: API Pricing and Informal Model TestsMatthew Berman · Forward FutureMatthew Berman compares the original Mistral Medium prototype with Mixtral and GPT-4, then tests coding, writing and reasoning through the API.Modèles et entraînementBenchmarksAI cost & performance+2
8 diapositives20:49EN
Matthew Berman· 12 déc. 2023Mixtral 8x7B: Mixture-of-Experts Architecture, Setup and Early TestsMatthew Berman · Forward FutureMatthew Berman reviews Mistral AI’s December 2023 open-weight model, demonstrates a cloud setup and tests its coding and reasoning responses.Modèles et entraînementOpen-weight modelsInference+2
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Cognitive Revolution "How AI Changes Everything"· 17 nov. 2023PodcastA Toy Paper Did Not Prove LLMs Cannot Generalize. The Discourse Still Ran With It.Anime : Nathan LabenzNathan Labenz and Erik Torenberg unpack a viral DeepMind result, a weekend reproduction, and a premature war over voluntary AI commitments.Modèles et entraînementAI researchFondamentaux du ML+3
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Cognitive Revolution "How AI Changes Everything"· 14 nov. 2023PodcastA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.Anime : Nathan LabenzGretel AI founder Alex Watson tells Nathan Labenz why most company data is still a spreadsheet, why GPT-4 is the wrong tool for generating it, and how differential privacy lets a model learn a disease without learning a patient.Modèles et entraînementSynthetic dataData quality & curation+3
5 diapositives11:05EN
Matthew Berman· 7 nov. 2023Testing Dolphin 2.2, a Mistral 7B Fine-TuneMatthew Berman · Forward FutureMatthew Berman tries a small open-weight model on coding, formatting and reasoning prompts, with mixed results.Open-weight modelsBenchmarksModèles et entraînement+1
1:19:52EN
Cognitive Revolution "How AI Changes Everything"· 18 oct. 2023Podcast1,000 H100s Take 115 Days; 22,000 Take FiveAnime : Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.Modèles et entraînementAI researchModel Interpretability+3
5 diapositives13:11EN
Matthew Berman· 5 oct. 2023Testing Mistral 7B OpenOrca: A 2023 Cloud Setup and Prompt TrialMatthew Berman · Forward FutureMatthew Berman runs a quantized OpenOrca variant on a rented GPU and reviews coding, writing and reasoning examples.Open-weight modelsLocal inferenceBenchmarks+2
6 diapositives12:49EN
Matthew Berman· 14 sept. 2023Falcon 180B: A 2023 Review of TII’s Open-Weight ModelMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and writing prompts while examining the model’s training scale and license.Open-weight modelsBenchmarksModèles et entraînement+1
4 diapositives4:35EN
Matthew Berman· 5 sept. 2023Running 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.Modèles et entraînementInfrastructure et déploiementAgents de code
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Cognitive Revolution "How AI Changes Everything"· 29 août 2023PodcastOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2Anime : 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.Modèles et entraînementMultimodal AIÉvaluations+3
2 diapositives6:28EN
Matthew Berman· 29 août 2023A 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.Fine-tuningSynthetic dataModèles et entraînement
52:48EN
Cognitive Revolution "How AI Changes Everything"· 25 août 2023PodcastOpenAI Cut Fine-Tuning 90 Percent, and Founders Crossed Llama 2 Off the ListAnime : 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.Modèles et entraînementFine-tuningAI cost & performance+3
5 diapositives7:09EN
Matthew Berman· 28 juil. 2023AI 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 mediaModèles et entraînementBrain-Computer Interfaces+2
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Cognitive Revolution "How AI Changes Everything"· 27 juil. 2023PodcastHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3Anime : 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.Modèles et entraînementModel InterpretabilityAI research+3
2 diapositives4:59EN
Matthew Berman· 25 juil. 2023Testing 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 modelsÉvaluationsModèles et entraînement+1
7 diapositives9:40EN
Matthew Berman· 20 juil. 2023Testing 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 modelsBenchmarksModèles et entraînement+2
2 diapositives5:53EN
Matthew Berman· 18 juil. 2023Petals: 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
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