最近更新されたチャンネル

選ぶとそのチャンネルだけを表示すべてのチャンネル →

絞り込み結果

17 件
並び順
スライド2:22EN
Matthew Berman· 2026年5月29日Pioneer 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.ファインチューニング評価(Evals)Local inference+2
スライド1:02EN
Matthew Berman· 2026年3月16日Matthew Berman’s Plans for a GB300 AI Workstation at HomeMatthew Berman · Forward FutureAfter NVIDIA visits with a Dell Pro Max GB300 system, Berman describes his plans for local models, fine-tuning and future AI agents.Local inferenceインフラとデプロイファインチューニング+1
スライド 3 枚11:56EN
Matthew Berman· 2024年11月3日IBM Granite 3.0 and InstructLab: Small Models for Enterprise AIMatthew Berman · Forward FutureMatthew Berman reviews IBM's 2024 model family and its approach to adding domain knowledge. IBM partnered on the video and invited him to its event.モデルと学習エンタープライズ AIファインチューニング+2
スライド 5 枚12:03EN
Matthew Berman· 2024年9月6日Reflection 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 Models評価(Evals)+2
スライド0:44EN
Matthew Berman· 2024年8月29日NVIDIA Adds Multi-LoRA Serving to Its RTX AI ToolkitMatthew Berman · Forward FutureIn an NVIDIA-partnered video, Matthew Berman describes using multiple adapters with one base model and a vendor-reported throughput gain.ファインチューニングInferenceインフラとデプロイ
スライド 6 枚14:30EN
Matthew Berman· 2024年7月3日Retrieval-Augmented Generation: External Knowledge, Embeddings and SearchMatthew Berman · Forward FutureMatthew Berman introduces RAG using customer-service, company-document and automotive-manual examples, then explains vector retrieval and agents.検索拡張生成(RAG)EmbeddingsSearch & retrieval+1
スライド 2 枚6:28EN
Matthew Berman· 2023年8月29日A 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.ファインチューニングSynthetic dataモデルと学習
スライド 5 枚13:19EN
Matthew Berman· 2023年8月17日Testing WizardLM 70B: Setup, Coding and Reasoning ResultsMatthew Berman · Forward FutureMatthew Berman runs a quantized Llama 2-based model through an informal collection of coding, writing, arithmetic and logic prompts.Foundation ModelsファインチューニングLocal inference+2
スライド 3 枚5:34EN
Matthew Berman· 2023年7月26日FreeWilly2: A 2023 Prompt Test with Uncertain FormattingMatthew Berman · Forward FutureMatthew Berman retries an instruction-tuned Llama 2 model after a poor first run, finding mixed results and a likely template problem.Open-weight modelsBenchmarksファインチューニング+1
スライド 2 枚8:49EN
Matthew Berman· 2023年7月19日Llama 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.オープンソースとコミュニティFoundation ModelsAI Alignment+1
スライド 5 枚59:36EN
Matthew Berman· 2023年6月27日Pascal Pfeiffer on Retrieval, Open Models, Kaggle and Fine-Tuning with H2O LLM StudioMatthew Berman, Pascal Pfeiffer · Forward FutureIn a 2023 interview, H2O.ai’s Pascal Pfeiffer explains document retrieval and parameter-efficient fine-tuning, reflects on Kaggle competitions and demonstrates LLM Studio. He and Matthew Berman also consider coding assistants and unverified rumors about model architecture.ファインチューニング検索拡張生成(RAG)Open-weight models+2
スライド 5 枚19:38EN
Matthew Berman· 2023年6月9日Orca: 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.DistillationファインチューニングSynthetic data+2
スライド 4 枚11:42EN
Matthew Berman· 2023年5月31日Guanaco 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.ファインチューニングQuantizationOpen-weight models+1
スライド 4 枚14:29EN
Matthew Berman· 2023年5月8日A 2023 Google Memo on Open Models, LoRA and AI’s Competitive MoatMatthew Berman · Forward FutureMatthew Berman reviews a leaked employee essay arguing that rapid community iteration could challenge proprietary AI labs, and traces the early LLaMA ecosystem.Open-weight modelsAI Historyファインチューニング+1
1:13:52EN
Matthew Berman· 2023年4月15日Andriy 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
スライド 3 枚6:50EN
Matthew Berman· 2023年3月19日Running 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 inferenceモデルと学習ファインチューニング+1
スライド 4 枚8:19EN
Matthew Berman· 2023年2月15日Leonardo.Ai in 2023: Generating Images, Remixing Models and Editing a CanvasMatthew Berman · Forward FutureMatthew Berman walks through Leonardo.Ai’s early image-generation interface, from style models and settings to community remixes, custom training and the beta canvas. Features and pricing have changed since this February 2023 demonstration.Generative mediaPrompting & in-context learningプロダクトと UX+1