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Welcome, Gradio 5
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onekq's
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5 days ago
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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
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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IP-Adapter for PonyXL
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#31 opened 11 months ago
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snusnumrik
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singhsidhukuldeep's
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4 months ago
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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?
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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🔥🎭🌟 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
📄 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
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onekq's
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Here is my latest study on OpenAI🍓o1🍓.
A Case Study of Web App Coding with OpenAI Reasoning Models (2409.13773)
I wrote an easy-to-read blogpost to explain finding.
https://huggingface.co/blog/onekq/daily-software-engineering-work-reasoning-models
INSTRUCTION FOLLOWING is the key.
100% instruction following + Reasoning = new SOTA
But if the model misses or misunderstands one instruction, it can perform far worse than non-reasoning models.
A Case Study of Web App Coding with OpenAI Reasoning Models (2409.13773)
I wrote an easy-to-read blogpost to explain finding.
https://huggingface.co/blog/onekq/daily-software-engineering-work-reasoning-models
INSTRUCTION FOLLOWING is the key.
100% instruction following + Reasoning = new SOTA
But if the model misses or misunderstands one instruction, it can perform far worse than non-reasoning models.
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🥳 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 𝗔𝗴𝗲𝗻𝘁𝘀 𝗻𝗼𝘄 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀!
Multi-agent systems have been introduced in Microsoft's framework Autogen. It simply means having several agents working together to solve your task instead of only one : this paradigm empirically yields better performance on most benchmarks. The reason for this better performance is conceptually simple: for many tasks, rather than using a do-it-all system, you would prefer to specialize units on sub-tasks. Here, having agents with separate tool sets and memories allows to achieve efficient specialization.
You can now easily build hierarchical multi-agent systems with transformers.agents (not released yet, use the dev version)
To do so, encapsulate the agent in a ManagedAgent object. This object needs arguments agent, name, and a description, which will then be embedded in the manager agent's system prompt to let it know how to call this managed agent, as we also do for tools.
Cf the example in the image! We'll keep building on this paradigm in the upcoming weeks 🚀
Read more in the doc 👉 https://github.com/huggingface/transformers/blob/main/docs/source/en/agents_advanced.md
Checkout an advanced multi-agent system that tops the GAIA leaderboard 👉 https://github.com/aymeric-roucher/GAIA/blob/main/gaia_multiagent.py
Multi-agent systems have been introduced in Microsoft's framework Autogen. It simply means having several agents working together to solve your task instead of only one : this paradigm empirically yields better performance on most benchmarks. The reason for this better performance is conceptually simple: for many tasks, rather than using a do-it-all system, you would prefer to specialize units on sub-tasks. Here, having agents with separate tool sets and memories allows to achieve efficient specialization.
You can now easily build hierarchical multi-agent systems with transformers.agents (not released yet, use the dev version)
To do so, encapsulate the agent in a ManagedAgent object. This object needs arguments agent, name, and a description, which will then be embedded in the manager agent's system prompt to let it know how to call this managed agent, as we also do for tools.
Cf the example in the image! We'll keep building on this paradigm in the upcoming weeks 🚀
Read more in the doc 👉 https://github.com/huggingface/transformers/blob/main/docs/source/en/agents_advanced.md
Checkout an advanced multi-agent system that tops the GAIA leaderboard 👉 https://github.com/aymeric-roucher/GAIA/blob/main/gaia_multiagent.py
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xianbao's
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5 months ago
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1907
With the open-weight release of CogVideoX-5B from THUDM, i.e. GLM team, the Video Generation Model (how about calling it VGM) field has officially became the next booming "LLM"
What does the landscape look like? What are other video generation models? This collection below is all your need.
xianbao/video-generation-models-66c350163c74f60f5c412af6
The above video is generated by @a-r-r-o-w with CogVideoX-5B, taken from a nice lookout for the field!
What does the landscape look like? What are other video generation models? This collection below is all your need.
xianbao/video-generation-models-66c350163c74f60f5c412af6
The above video is generated by @a-r-r-o-w with CogVideoX-5B, taken from a nice lookout for the field!
This model slaps hard!
2
#1 opened 5 months ago
by
Winnougan