Vision Language Model on SoloSoft

InternVL: Open-Source Vision Language Model Family Scaling to 241B Parameters

Mon, 01 Jan 0001 00:00:00 +0000

InternVL is a series of open-source vision-language foundation models developed by OpenGVLab at the Shanghai Artificial Intelligence Laboratory. The InternVL family scales vision transformers to 6 billion parameters and progressively aligns them with large language models, creating a unified architecture that achieves GPT-4o-level performance across a wide range of multimodal benchmarks. The flagship InternVL2.5-241B model represents one of the largest open-source multimodal models ever released.

The project has been recognized at CVPR 2024 and has garnered significant attention for demonstrating that open-source vision-language models can match or exceed proprietary systems when scaled appropriately. InternVL’s architecture handles tasks spanning image captioning, visual question answering, document understanding, chart analysis, and multi-image reasoning, making it a versatile foundation for multimodal AI applications.

MLX-VLM: Vision Language Model Inference and Fine-Tuning on Apple Silicon

Mon, 01 Jan 0001 00:00:00 +0000

Running Vision Language Models – AI systems that can simultaneously understand images and text – has traditionally required expensive NVIDIA GPUs with substantial VRAM. Apple Silicon users were largely left out of the multimodal AI revolution, forced to rely on cloud APIs or dual-machine setups. MLX-VLM by developer Blaizzy changes this equation entirely.

MLX-VLM is an open-source Python package that brings Vision Language Model inference and fine-tuning directly to Apple Silicon hardware using Apple’s MLX framework. By leveraging the unified memory architecture of M-series chips, it enables Mac users to run sophisticated multimodal models – including LLaVA, Qwen-VL, InternVL2, and PaliGemma2 – entirely on-device, with performance that often surprises even experienced practitioners.

VILA: NVIDIA's Open-Source Vision Language Model Family from NVlabs

Mon, 01 Jan 0001 00:00:00 +0000

Vision Language Models (VLMs) that can reason about both images and text have become one of the most active areas in AI research. VILA (Visual Language Model), developed by NVIDIA Labs (NVlabs), represents a comprehensive family of open-source VLMs designed for multi-image reasoning, video understanding, and visual chain-of-thought. The models are designed to scale from edge devices to cloud deployments, making them suitable for robotics, video analytics, and document understanding.

The VILA family, hosted at github.com/NVlabs/VILA, has evolved through several generations – from VILA 1.0 through NVILA and LongVILA – each introducing new capabilities. VILA models are built on a “scale-then-compress” philosophy that first trains on high-resolution images to maximize perception quality, then compresses the visual tokens for efficient inference. This approach achieves state-of-the-art results on video understanding benchmarks while remaining practical for deployment.