repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · yesterday
Blaizzy/mlx-vlm
MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 55%Dynamo: Dynamic Skill-Tool Evolution for Vision-Language Agents →
- PossiblePossibly related (embedding) · 54%VioletVision-3B →
- PossiblePossibly related (embedding) · 48%AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models →
- PossiblePossibly related (embedding) · 46%Search-based Testing of Vision Language Models for In-Car Scene Understanding →
- PossiblePossibly related (embedding) · 46%Show Me Examples: Inferring Visual Concepts from Image Sets →
- PossiblePossibly related (embedding) · 47%SteelBench: Evaluating Vision-Language Models in Real-World Industrial Environments →
- PossiblePossibly related (embedding) · 46%you can just watch a language model think now. i built a way to visualize the words AI doesn’t say →
Implements
paperDynamo: Dynamic Skill-Tool Evolution for Vision-Language AgentspaperAnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language ModelspaperSearch-based Testing of Vision Language Models for In-Car Scene UnderstandingpaperShow Me Examples: Inferring Visual Concepts from Image Sets
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Covers (incoming)
Related across the graph
paperDynamo: Dynamic Skill-Tool Evolution for Vision-Language Agentsnewsyou can just watch a language model think now. i built a way to visualize the words AI doesn’t saymodelVioletVision-3BpaperSteelBench: Evaluating Vision-Language Models in Real-World Industrial EnvironmentspaperShow Me Examples: Inferring Visual Concepts from Image SetspaperAnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language ModelspaperSearch-based Testing of Vision Language Models for In-Car Scene Understanding