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Uthman Bilal

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reacted to AdinaY's post with 🚀 about 20 hours ago
Xwen 🔥 a series of open models based on Qwen2.5 models, developed by a brilliant research team of PhD students from the Chinese community. https://huggingface.co/collections/shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e ✨ 7B/72B ✨ Apache 2.0 ✨ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto
upvoted a collection 2 days ago
200+ Roleplay, Creative Writing, Uncensored, NSFW models.
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Load 4bit models 4x faster
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reacted to AdinaY's post with 🚀 about 20 hours ago
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Xwen 🔥 a series of open models based on Qwen2.5 models, developed by a brilliant research team of PhD students from the Chinese community.
shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e
✨ 7B/72B
✨ Apache 2.0
✨ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto
upvoted 2 collections 2 days ago
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New activity in Alissonerdx/YuE-s1-7B-anneal-en-icl-nf4 4 days ago

How run this in Windows?

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#1 opened 4 days ago by
Winnougan
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So 🐋DeepSeek🐋 hits the mainstream media. But it has been a star in our little cult for at least 6 months. Its meteoric success is not overnight, but two years in the making.

To learn their history, just look at their 🤗 repo https://huggingface.co/deepseek-ai

* End of 2023, they launched the first model (pretrained by themselves) following Llama 2 architecture
* June 2024, v2 (MoE architecture) surpassed Gemini 1.5, but behind Mistral
* September, v2.5 surpassed GPT 4o mini
* December, v3 surpassed GPT 4o
* Now R1 surpassed o1

Most importantly, if you think DeepSeek success is singular and unrivaled, that's WRONG. The following models are also near or equal the o1 bar.

* Minimax-01
* Kimi k1.5
* Doubao 1.5 pro
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upvoted a collection 15 days ago
New activity in h94/IP-Adapter 2 months ago

IP-Adapter for PonyXL

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#31 opened 11 months ago by
snusnumrik
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Researchers have developed a novel approach called Logic-of-Thought (LoT) that significantly enhances the logical reasoning capabilities of large language models (LLMs).

Here are the steps on how Logic-of-Thought (LoT) is implemented:

-- 1. Logic Extraction

1. Use Large Language Models (LLMs) to identify sentences containing conditional reasoning relationships from the input context.
2. Generate a collection of sentences with logical relationships.
3. Use LLMs to extract the set of propositional symbols and logical expressions from the collection.
4. Identify propositions with similar meanings and represent them using identical propositional symbols.
5. Analyze the logical relationships between propositions based on their natural language descriptions.
6. Add negation (¬) for propositions that express opposite meanings.
7. Use implication (→) to connect propositional symbols when a conditional relationship exists.

-- 2. Logic Extension

1. Apply logical reasoning laws to the collection of logical expressions from the Logic Extraction phase.
2. Use a Python program to implement logical deduction and expand the expressions.
3. Apply logical laws such as Double Negation, Contraposition, and Transitivity to derive new logical expressions.

-- 3. Logic Translation

1. Use LLMs to translate the newly generated logical expressions into natural language descriptions.
2. Combine the natural language descriptions of propositional symbols according to the extended logical expressions.
3. Incorporate the translated logical information as a new part of the original input prompt.

-- 4. Integration with Existing Prompting Methods

1. Combine the LoT-generated logical information with the original prompt.
2. Use this enhanced prompt with existing prompting methods like Chain-of-Thought (CoT), Self-Consistency (SC), or Tree-of-Thoughts (ToT).
3. Feed the augmented prompt to the LLM to generate the final answer.

What do you think about LoT?
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liked 2 models 4 months ago
reacted to DmitryRyumin's post with 😎 4 months ago
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🔥🎭🌟 New Research Alert - ECCV 2024 (Avatars Collection)! 🌟🎭🔥
📄 Title: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos 🔝

📝 Description: MeshAvatar is a novel pipeline that generates high-quality triangular human avatars from multi-view videos, enabling realistic editing and rendering through a mesh-based approach with physics-based decomposition.

👥 Authors: Yushuo Chen, Zerong Zheng, Zhe Li, Chao Xu, and Yebin Liu

📅 Conference: ECCV, 29 Sep – 4 Oct, 2024 | Milano, Italy 🇮🇹

📄 Paper: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos (2407.08414)

🌐 Github Page: https://shad0wta9.github.io/meshavatar-page
📁 Repository: https://github.com/shad0wta9/meshavatar

📺 Video: https://www.youtube.com/watch?v=Kpbpujkh2iI

🚀 CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers

🚀 WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers

🚀 ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers

📚 More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin

🚀 Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36

🔍 Keywords: #MeshAvatar #3DAvatars #MultiViewVideo #PhysicsBasedRendering #TriangularMesh #AvatarCreation #3DModeling #NeuralRendering #Relighting #AvatarEditing #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #ECCV2024