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Diapositivas2:22EN
Matthew Berman· 29 may 2026Pioneer Agent and Automated Fine-Tuning of Small ModelsMatthew Berman · Forward FutureIn a Fastino-sponsored video, Matthew Berman describes a system that automates parts of model adaptation and discusses its reported benchmark results.Ajuste finoEvaluacionesLocal inference+2
5 diapositivas12:03EN
Matthew Berman· 6 sept 2024Reflection 70B: Self-Correction Claims and the Limits of a 2024 DemoMatthew Berman · Forward FutureMatthew Berman reviews a model trained to generate reflection steps. Its initial benchmark claims later proved difficult for independent evaluators to reproduce.AI HistoryFoundation ModelsEvaluaciones+2
2 diapositivas6:28EN
Matthew Berman· 29 ago 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.Ajuste finoSynthetic dataModelos y entrenamiento
2 diapositivas8:49EN
Matthew Berman· 19 jul 2023Llama 2’s 2023 Release: Model Sizes, License and SafetyMatthew Berman · Forward FutureMatthew Berman reviews Meta’s Llama 2 paper, its commercial license, model variants and safety evaluations.Código abierto y comunidadFoundation ModelsAI Alignment+1
5 diapositivas19:38EN
Matthew Berman· 9 jun 2023Orca: Teaching a Smaller Model with GPT-4 ExplanationsMatthew Berman · Forward FutureMatthew Berman reviews Microsoft Research’s 2023 Orca paper and its approach to training a 13-billion-parameter model on detailed teacher responses.DistillationAjuste finoSynthetic data+2
4 diapositivas11:42EN
Matthew Berman· 31 may 2023Guanaco and QLoRA: A 2023 Fine-Tuning and Model TestMatthew Berman · Forward FutureMatthew Berman reviews the QLoRA paper, explains the reported memory savings and tries Guanaco 65B on an informal prompt set.Ajuste finoQuantizationOpen-weight models+1
1:13:52EN
Matthew Berman· 15 abr 2023Andriy Mulyar on GPT4All, Data Quality and Open Local ModelsMatthew Berman, Andriy Mulyar · Forward FutureThe Nomic AI co-founder explains how GPT4All emerged, why training data matters, and the tradeoffs between local and centralized AI.Local inferenceOpen-weight modelsData quality & curation+2