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201 件
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スライド 2 枚0:42EN
Matthew Berman· 2024年1月31日LLaVA 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 AIモデルと学習Local inference+1
スライド 5 枚14:04EN
Matthew Berman· 2023年12月15日Mistral 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.モデルと学習BenchmarksAI cost & performance+2
スライド 5 枚19:37EN
Matthew Berman· 2023年12月13日How Rephrased Test Data Inflated a 13B Model’s Benchmark ScoresMatthew Berman · Forward FutureMatthew Berman examines a 2023 experiment showing how reworded benchmark questions can evade simple contamination checks and inflate model scores.Benchmarks評価(Evals)Data quality & curation+2
スライド 8 枚20:49EN
Matthew Berman· 2023年12月12日Mixtral 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.モデルと学習Open-weight modelsInference+2
スライド 10 枚26:52EN
Matthew Berman· 2023年12月7日Google’s Gemini Launch: Multimodal Demos, Benchmarks and Research ApplicationsMatthew Berman · Forward FutureMatthew Berman reviews Google’s December 2023 Gemini report and promotional demonstrations, from visual reasoning to scientific literature screening.GeminiMultimodal AIBenchmarks+2
スライド 4 枚7:48EN
Matthew Berman· 2023年11月26日Testing Dolphin 2.2 Yi 34B on Coding and Reasoning TasksMatthew Berman · Forward FutureMatthew Berman runs a quantized Yi fine-tune through informal coding, writing and logic prompts in November 2023, with uneven results.Open-weight modelsFoundation ModelsBenchmarks+2
スライド 3 枚10:37EN
Matthew Berman· 2023年11月17日Testing DeepSeek Coder on Forms, Bugs and a Snake GameMatthew Berman · Forward FutureMatthew Berman puts the 2023 coding model through a small hands-on rubric, from JSON and unit tests to a playable game.Software engineering開発ワークフローとテストOpen-weight models+1
スライド 5 枚11:05EN
Matthew Berman· 2023年11月7日Testing 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 modelsBenchmarksモデルと学習+1
スライド 5 枚13:11EN
Matthew Berman· 2023年10月5日Testing 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
スライド 6 枚12:49EN
Matthew Berman· 2023年9月14日Falcon 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 modelsBenchmarksモデルと学習+1
スライド 5 枚10:20EN
Matthew Berman· 2023年8月30日Phind Code Llama 34B vs GPT-4: A 2023 Python Coding TestMatthew Berman · Forward FutureMatthew Berman compares a quantized Phind fine-tune with GPT-4 on games, short functions and refactoring, with mixed results.Open-weight modelsコーディングエージェント評価(Evals)+2
スライド2:25EN
Matthew Berman· 2023年8月1日Gorilla: Generating API Calls with Language ModelsMatthew Berman · Forward FutureMatthew Berman introduces a 2023 research project on using retrieved documentation to help language models generate tool calls.AI agentsMCP とツールBenchmarks
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 7 枚9:40EN
Matthew Berman· 2023年7月20日Testing 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 modelsBenchmarksモデルと学習+2
スライド 3 枚9:46EN
Matthew Berman· 2023年7月17日Orca Mini V2 vs. OpenOrca: A 2023 Informal Model ComparisonMatthew Berman · Forward FutureMatthew Berman tests two Orca-inspired models on coding, writing and reasoning, with results shaped by prompt setup and subjective scoring.BenchmarksOpen-weight modelsReasoning & test-time compute+2
スライド 6 枚18:08EN
Matthew Berman· 2023年6月29日Six 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 modelsモデルと学習+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 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 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
1:25:35EN
Cognitive Revolution "How AI Changes Everything"· 2023年5月21日ポッドキャストTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical Schoolホスト 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.モデルと学習AI in healthcareファインチューニング+3
スライド 4 枚10:16EN
Matthew Berman· 2023年5月21日DarkBERT: Adapting a Language Model to Dark-Web TextMatthew Berman · Forward FutureMatthew Berman reviews research on a RoBERTa-based model and its cybersecurity evaluations.AI securityPretraining & midtrainingBenchmarks+1
スライド 4 枚11:07EN
Matthew Berman· 2023年5月16日Testing 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 modelsBenchmarksモデルと学習+1
58:24EN
Cognitive Revolution "How AI Changes Everything"· 2023年5月11日ポッドキャストAmong 100 Random Doctors, Zak Kohane Would Be Surprised If One Beat GPT-4ホスト Nathan LabenzHarvard's biomedical informatics chair on a rare newborn case, why drug-style trials will not certify a general medical model, and why the AI should still work for the patient.AI in healthcare評価(Evals)Human-AI Collaboration+3
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