Compress vision-language and unimodal AI models by structured pruning
[ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers - sdc17/UPop
Compress vision-language and unimodal AI models by structured pruning launched on July 31, 2023 with 7 votes, #1,751 of 4,347 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 3 years on.
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Ideogram 4.0 – open-weight 9.3B text-to-image modelJun 2026 · github.com · ▲46It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/ We will be happy to answer any questions :)


onprem unstructured data extraction with 4 lines of code2025 · github.com · ▲8The traditional pipeline for unstructured data extraction typically follows these steps: 1. Image → OCR Model (e.g., Google Vision) → Layout Model (e.g. Surya) → LLM → Final Answer However, this can be streamlined using a Vision-Language Model (VLM): 2. Image → VLM → Final Answer Recently VLMs have improved a lot for OCR and document understanding tasks, specifically the Qwen-2.5-VL series. We can run the Qwen-2.5-VL-7B-AWQ model locally with just 16GB VRAM, and perform end-to-end information extraction (fields and table extraction) without any external models. Hallucination with VLMs One…

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Hey everyone, I here is a small open-source project I've been working on lately. I'd love to hear your thoughts and improvement ideas :) GitHub: [github.com/Vincenius/workout-lol](https://github.com/Vincenius/workout-lol)
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