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Yatharth Sharma

YaTharThShaRma999

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reacted to ariG23498's post with 🚀 2 days ago

Timm ❤️ Transformers Wtih the latest version of transformers you can now use any timm model with the familiar transformers API. Blog Post: https://huggingface.co/blog/timm-transformers Repository with examples: https://github.com/ariG23498/timm-wrapper-examples Collection: https://huggingface.co/collections/ariG23498/timmwrapper-6777b85f1e8d085d3f1374a1

reacted to Tonic's post with 🔥 2 days ago

🙋🏻‍♂️ Hey there folks , Facebook AI just released JASCO models that make music stems . you can try it out here : https://huggingface.co/spaces/Tonic/audiocraft hope you like it

upvoted a paper 2 days ago

RepVideo: Rethinking Cross-Layer Representation for Video Generation

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reacted to ariG23498's post with 🚀 2 days ago

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Timm ❤️ Transformers

Wtih the latest version of transformers you can now use any timm model with the familiar transformers API.

Blog Post: https://huggingface.co/blog/timm-transformers
Repository with examples: https://github.com/ariG23498/timm-wrapper-examples
Collection: ariG23498/timmwrapper-6777b85f1e8d085d3f1374a1

reacted to Tonic's post with 🔥 2 days ago

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1184

🙋🏻‍♂️ Hey there folks ,

Facebook AI just released JASCO models that make music stems .

you can try it out here : Tonic/audiocraft

hope you like it

upvoted a paper 2 days ago

RepVideo: Rethinking Cross-Layer Representation for Video Generation

Paper • 2501.08994 • Published 3 days ago • 13

liked a Space 3 days ago

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MiniMaxVL01

reacted to AdinaY's post with 🔥 4 days ago

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2891

MiniMax, the company behind Hailuo_AI, has joined the open source community by releasing both models and demos of MiniMax-Text-01 & MiniMax-VL-01🔥
- Model
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01
- Demo
MiniMaxAI/MiniMax-VL-01
MiniMaxAI/MiniMax-Text-01

✨ MiniMax-text-01:
- 456B with 45.9B activated per token
- Combines Lightning Attention, Softmax Attention, and MoE for optimal performance
- Training context up to 1M tokens, inference handles 4M tokens

✨ MiniMax-VL-01:
- ViT-MLP-LLM framework ( non-transformer👀)
- Handles image inputs from 336×336 to 2016×2016
- 694M image-caption pairs + 512B tokens processed across 4 stages