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Source channel @githubtrending · Post #15600 · Apr 4

#python#apple_silicon#florence2#idefics#llava#llm#local_ai#mlx#molmo#paligemma#pixtral#vision_framework#vision_language_model#vision_transformer MLX-VLM lets you run, chat with, and fine-tune Vision Language Models (VLMs) plus audio/video models on your Mac using MLX—install easily with `pip install -U mlx-vlm`. Use CLI for quick text/image/audio generation (e.g., `mlx_vlm.generate --model ... --image photo.jpg`), Gradio UI for chats, Python scripts, or a FastAPI server with OpenAI-compatible endpoints supporting multi-images/videos. Features like TurboQuant cut KV cache memory by 76%, and LoRA/QLoRA fine-tuning works on consumer hardware. You benefit by experimenting with powerful multimodal AI locally—fast, memory-efficient, no cloud costs, perfect for Mac users tweaking models affordably. https://github.com/Blaizzy/mlx-vlm

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Google Facts™ [ ️@googlefactss🌎]

@googlefactss · Post #40776 · 03/11/2026, 11:01 PM

During World War II, engineers studied planes that returned from missions. They first thought the areas with the most bullet holes needed armor. Statistician Abraham Wald realized this was survivorship bias. Survivorship bias happens when you focus only on survivors and ignore failures.The undamaged areas on returning planes were actually the critical spots. Planes hit there did not survive. He recommended reinforcing those undamaged areas. ✈️📊🛡️ [Read more] @googlefactss #SurvivorshipBias#WWII#AbrahamWald#Planes#Statistics#History