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23 件
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スライド 9 枚13:09EN
Matthew Berman· 2026年8月14日AI News: Ultrafast Inference, Claude Watermarks and New ModelsMatthew Berman · Forward FutureMatthew Berman reviews an ultrafast coding preview, text watermarking, Grok Bot, open-weight models and Computer History.InferenceOpen-weight modelsコーディングエージェント+2
スライド 5 枚9:47EN
Matthew Berman· 2026年4月3日Gemma 4: Smaller Open-Weight Models for Local AIMatthew Berman · Forward FutureMatthew Berman reviews Google’s model sizes, multimodal features and reported benchmarks, with an eye toward local agents.Open-weight modelsLocal inferenceFoundation Models+2
スライド 5 枚7:49EN
Matthew Berman· 2025年12月2日DeepSeek V3.2: Reasoning, Sparse Attention and Agent TrainingMatthew Berman · Forward FutureMatthew Berman examines DeepSeek’s December 2025 model release, its benchmark results and the techniques used to improve long-context efficiency and tool use.Foundation ModelsOpen-weight modelsReasoning & test-time compute+2
スライド1:01EN
Matthew Berman· 2025年8月5日OpenAI’s gpt-oss Release and a Coding BenchmarkMatthew Berman · Forward FutureMatthew Berman introduces two open-weight models and reviews a Codeforces comparison from the launch.Open-weight modelsOpenAIBenchmarks+1
スライド 3 枚5:27EN
Matthew Berman· 2025年1月29日Ways to Try DeepSeek R1: Hosted and Local Distilled ModelsMatthew Berman · Forward FutureMatthew Berman demonstrates DeepSeek chat, Groq’s hosted distill and a local model in LM Studio.Local inferenceOpen-weight modelsQuantization
0:28EN
Matthew Berman· 2024年11月6日Comparing Three Local Coding ModelsMatthew Berman · Forward FutureMatthew Berman introduces a sponsored comparison of DeepSeek Coder V2, Yi-Coder and Qwen Coder running on a workstation.Local inferenceコーディングエージェントOpen-weight models
スライド 2 枚9:15EN
Matthew Berman· 2024年9月25日Llama 3.2 Adds Vision and Smaller On-Device ModelsMatthew Berman · Forward FutureIn a Meta-partnered 2024 video, Matthew Berman reviews the model family, its tooling and a brief hosted 1B demonstration.Open-weight modelsMultimodal AILocal inference+1
スライド 4 枚11:14EN
Matthew Berman· 2024年9月18日Pixtral 12B: Testing a 2024 Vision-Language ModelMatthew Berman · Forward FutureMatthew Berman runs informal image, text and interface-generation tests of Mistral’s Pixtral on a sponsor-provided cloud GPU.Multimodal AI画像と動画Open-weight models+1
スライド 3 枚13:40EN
Matthew Berman· 2024年7月29日Testing Llama 3.1 8B: Benchmark Gains and Mixed Practical ResultsMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and other prompts on the small model and finds several failures despite stronger reported benchmarks.Open-weight models評価(Evals)モデルと学習+1
スライド 6 枚11:20EN
Matthew Berman· 2024年7月1日Matthew Berman Adapts Mixture of Agents to Groq InferenceMatthew Berman · Forward FutureA 2024 tutorial changes an open-source multi-model ensemble to use Groq, then tests its interactive output and fixes a streaming error.Multi-agent systemsAgent orchestrationInference+1
スライド 4 枚9:53EN
Matthew Berman· 2024年5月28日Testing Microsoft Phi-3 Medium Locally: Mixed Coding and Reasoning ResultsMatthew Berman · Forward FutureMatthew Berman runs a quantized Phi-3 Medium on a MacBook and explores coding, arithmetic and reasoning prompts.Open-weight modelsLocal inferenceBenchmarks+1
スライド 2 枚12:50EN
Matthew Berman· 2024年5月22日Testing Smaug: Hosted 70B Versus a Smaller Local ModelMatthew Berman · Forward FutureMatthew Berman compares two Smaug configurations on Snake, math and reasoning puzzles.Open-weight models評価(Evals)モデルと学習
スライド 3 枚14:23EN
Matthew Berman· 2024年3月31日Testing DBRX: Coding Strengths and Reasoning GapsMatthew Berman · Forward FutureMatthew Berman examines Databricks’ open-weight model and puts it through coding, arithmetic and reasoning prompts.Open-weight modelsモデルと学習評価(Evals)+1
スライド 4 枚14:11EN
Matthew Berman· 2024年3月23日LM Studio in 2024: Local Models, Parallel Inference and a Local APIMatthew Berman · Forward FutureMatthew Berman demonstrates model downloads, hardware settings, JSON output and serving multiple local models.Open-weight modelsInference
スライド 5 枚9:25EN
Matthew Berman· 2024年1月31日Matthew Berman Tests Meta’s Code Llama 70B on a Snake GameMatthew Berman · Forward FutureA 2024 look at Meta’s coding-model release, a SQL-focused fine-tune and a hands-on test that did not work on the first attempt.Open-weight modelsSoftware engineeringBenchmarks+2
3:37EN
Matthew Berman· 2024年1月18日Mark Zuckerberg Announces Llama 3 Training and Meta’s AI PlansMatthew Berman, Mark Zuckerberg · Forward FutureIn January 2024, Matthew Berman reacts to Zuckerberg’s remarks on general intelligence, compute and AI devices.Open-weight modelsAI Historyインフラとデプロイ+1
スライド 3 枚4:42EN
Matthew Berman· 2023年10月17日Connecting AutoGen to a Local Model With LM StudioMatthew Berman · Forward FutureMatthew Berman demonstrates an early local-model setup and explains where agent behavior still needs work.エージェントOpen-weight modelsインフラとデプロイ
スライド 3 枚12:20EN
Matthew Berman· 2023年7月10日Testing 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 models評価(Evals)モデルと学習+1
スライド 3 枚8:17EN
Matthew Berman· 2023年6月22日Using OpenLLM to Serve Open Models Through an APIMatthew Berman · Forward FutureMatthew Berman demonstrates a local model server, its interactive API and a Python client in a 2023 walkthrough.Open-weight modelsInferenceインフラとデプロイ+1
スライド 7 枚4:36EN
Matthew Berman· 2023年6月20日Testing 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.モデルと学習Local inferenceSoftware engineering+2
スライド 2 枚10:40EN
Matthew Berman· 2023年6月7日Testing Nous Hermes 13B Locally: Mixed Reasoning and Unsafe ResponsesMatthew Berman · Forward FutureMatthew Berman compares a 2023 quantized model on hosted and local hardware, with uneven results across coding, logic and safety.Open-weight modelsLocal inferenceBenchmarks+1
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 5 枚8:05EN
Matthew Berman· 2023年3月29日A 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 modelsモデルと学習インフラとデプロイ