deepseek-ai/DeepSeek-V3.2-Speciale
Text Generation
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Tim Becker
wsbagnsv1
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deepseek-ai/DeepSeek-V3.2-Speciale
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wsbagnsv1/LLaDA2.0-mini-preview-GGUF
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reacted to
AdinaY's
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about 1 month ago
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Kimi K2 Thinking is now live on the hub 🔥
moonshotai/Kimi-K2-Thinking
✨ 1T MoE for deep reasoning & tool use
✨ Native INT4 quantization = 2× faster inference
✨ 256K context window
✨ Modified MIT license
moonshotai/Kimi-K2-Thinking
✨ 1T MoE for deep reasoning & tool use
✨ Native INT4 quantization = 2× faster inference
✨ 256K context window
✨ Modified MIT license
liked
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about 1 month ago
Holocine: interesting new wan model, paper and weights published
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#9 opened about 1 month ago
by
WernerrenreW
reacted to
DmitryRyumin's
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about 1 month ago
Post
3008
🚀👁️🌟 New Research Alert - ICCV 2025 (Oral)! 🌟👁️🚀
📄 Title: Token Activation Map to Visually Explain Multimodal LLMs 🔝
📝 Description: The Token Activation Map (TAM) is an advanced explainability method for multimodal LLMs. Using causal inference and a Rank Gaussian Filter, TAM reveals token-level interactions and eliminates redundant activations. The result is clearer, high-quality visualizations that enhance understanding of object localization, reasoning and multimodal alignment across models.
👥 Authors: Yi Li, Hualiang Wang, Xinpeng Ding, Haonan Wang, and Xiaomeng Li
📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸
📄 Paper: Token Activation Map to Visually Explain Multimodal LLMs (2506.23270)
📁 Repository: https://github.com/xmed-lab/TAM
🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
🚀 Added to the Multi-Modal Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/multi-modal-learning.md
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🔍 Keywords: #TokenActivationMap #TAM #CausalInference #VisualReasoning #Multimodal #Explainability #VisionLanguage #LLM #XAI #AI #ICCV2025 #ResearchHighlight
📄 Title: Token Activation Map to Visually Explain Multimodal LLMs 🔝
📝 Description: The Token Activation Map (TAM) is an advanced explainability method for multimodal LLMs. Using causal inference and a Rank Gaussian Filter, TAM reveals token-level interactions and eliminates redundant activations. The result is clearer, high-quality visualizations that enhance understanding of object localization, reasoning and multimodal alignment across models.
👥 Authors: Yi Li, Hualiang Wang, Xinpeng Ding, Haonan Wang, and Xiaomeng Li
📅 Conference: ICCV, 19 – 23 Oct, 2025 | Honolulu, Hawai'i, USA 🇺🇸
📄 Paper: Token Activation Map to Visually Explain Multimodal LLMs (2506.23270)
📁 Repository: https://github.com/xmed-lab/TAM
🚀 ICCV-2023-25-Papers: https://github.com/DmitryRyumin/ICCV-2023-25-Papers
🚀 Added to the Multi-Modal Learning Section: https://github.com/DmitryRyumin/ICCV-2023-25-Papers/blob/main/sections/2025/main/multi-modal-learning.md
📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
🔍 Keywords: #TokenActivationMap #TAM #CausalInference #VisualReasoning #Multimodal #Explainability #VisionLanguage #LLM #XAI #AI #ICCV2025 #ResearchHighlight
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updated
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about 2 months ago
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Config issue
2
#1 opened about 2 months ago
by
wsbagnsv1
The text encoder and VAE files are not in this repo, as it states.
1
#12 opened about 2 months ago
by
ljredux
upvoted
a
paper
about 2 months ago
no example workflow?
3
#1 opened about 2 months ago
by
HeyGoodEnough
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about 2 months ago