apple/ml-fastvlm

Health v2 · maintenance only

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7411 stars · 561 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official implementation of FastVLM, a vision language model with an efficient hybrid vision encoder (FastViTHD) that reduces token count and encoding latency for high-resolution images. Includes pretrained checkpoints (0.5B-7B), inference code, and a demo iOS app for on-device use.

Use cases

• run a fast vision language model for image question answering
• reduce time-to-first-token for VLM inference on high-resolution images
• deploy a vision language model on iPhone or iPad
• benchmark efficient vision encoders against LLaVA-OneVision
• finetune a VLM with a faster vision encoder
• run multimodal image understanding on mobile devices

When to choose

• you need low-latency VLM inference, especially on Apple/mobile hardware
• you want pretrained efficient VLM checkpoints with a demo app
• TTFT and vision encoder size are your bottleneck

When to avoid

• you need text-only LLMs or non-image modalities
• you need a production-supported framework with broad ecosystem tooling
• you require training from scratch rather than LLaVA-based finetuning
• you need non-Apple platform mobile deployment support

Facets

library · maturity active

machine-learning deep-learning llm-inference image-processing sdk machine-learning deep-learning computer-vision large-language-models mobile-development python cross-platform vision-language-model vision-encoder multimodal on-device-inference cvpr-2025 llava coreml ios macos

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markdown · JSON · MCP: product_card(name="apple/ml-fastvlm")