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Saikat Sengupta

Saikat

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reacted to nyuuzyou's post with 🔥 about 1 month ago

🖼️ SVGFind Icons Dataset - https://huggingface.co/datasets/nyuuzyou/svgfind Collection of 3,655,810 Scalable Vector Graphics (SVG) icons featuring: - Sourced from SVGFind across diverse categories & styles - Includes metadata: unique ID, title, tags, data pack, and license information - Contains minified SVG markup for direct use or processing - Organized into splits based on license type (Creative Commons: 3,645,444 icons, Public Domain: 10,366 icons) With over 3.6 million icons, this appears to be the largest SVG dataset on Hugging Face to date. If you're aware of a larger SVG collection, please let me know and I'll update this post with a reference to the largest dataset.

reacted to merterbak's post with 🔥 about 1 month ago

Qwen 3 models released🔥 It offers 2 MoE and 6 dense models with following parameter sizes: 0.6B, 1.7B, 4B, 8B, 14B, 30B(MoE), 32B, and 235B(MoE). Models: https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f Blog: https://qwenlm.github.io/blog/qwen3/ Demo: https://huggingface.co/spaces/Qwen/Qwen3-Demo GitHub: https://github.com/QwenLM/Qwen3 ✅ Pre-trained 119 languages(36 trillion tokens) and dialects with strong translation and instruction following abilities. (Qwen2.5 was pre-trained on 18 trillion tokens.) ✅Qwen3 dense models match the performance of larger Qwen2.5 models. For example, Qwen3-1.7B/4B/8B/14B/32B perform like Qwen2.5-3B/7B/14B/32B/72B. ✅ Three stage done while pretraining: • Stage 1: General language learning and knowledge building. • Stage 2: Reasoning boost with STEM, coding, and logic skills. • Stage 3: Long context training ✅ It supports MCP in the model ✅ Strong agent skills ✅ Supports seamless between thinking mode (for hard tasks like math and coding) and non-thinking mode (for fast chatting) inside chat template. ✅ Better human alignment for creative writing, roleplay, multi-turn conversations, and following detailed instructions.

liked a model 2 months ago

mlx-community/Qwen2.5-14B-Instruct-1M-8bit

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