Optimizing Large Language Model Training Using FP4 Quantization
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Eloi Eynard
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Optimizing Large Language Model Training Using FP4 Quantization
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Open Preference Dataset for Text-to-Image Generation by the 🤗 Community
Open Image Preferences is an Apache 2.0 licensed dataset for text-to-image generation. This dataset contains 10K text-to-image preference pairs across common image generation categories, while using different model families and varying prompt complexities.
https://huggingface.co/blog/image-preferences
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Open Preference Dataset for Text-to-Image Generation by the 🤗 Community
Open Image Preferences is an Apache 2.0 licensed dataset for text-to-image generation. This dataset contains 10K text-to-image preference pairs across common image generation categories, while using different model families and varying prompt complexities.
https://huggingface.co/blog/image-preferences
Open Image Preferences is an Apache 2.0 licensed dataset for text-to-image generation. This dataset contains 10K text-to-image preference pairs across common image generation categories, while using different model families and varying prompt complexities.
https://huggingface.co/blog/image-preferences
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🌐 Introducing Websim.ai User Projects Dataset -
nyuuzyou/websim
Dataset highlights:
- 137,452 user projects from Websim.ai, a service for creating small sites using Large Language Models (LLMs)
- Primarily in English, with potential for multilingual content in generated websites
- Each entry includes: project metadata, user information, and generated HTML content
- Contains detailed information about project revisions, site generation, and user interactions
- Data covers a wide range of user-generated website projects created through AI assistance
- Dedicated to the public domain under Creative Commons Zero (CC0) license
The dataset can be used for analyzing AI-assisted web development trends, studying user behavior in LLM-powered creative tools, and exploring the capabilities of language models in web design.
Dataset highlights:
- 137,452 user projects from Websim.ai, a service for creating small sites using Large Language Models (LLMs)
- Primarily in English, with potential for multilingual content in generated websites
- Each entry includes: project metadata, user information, and generated HTML content
- Contains detailed information about project revisions, site generation, and user interactions
- Data covers a wide range of user-generated website projects created through AI assistance
- Dedicated to the public domain under Creative Commons Zero (CC0) license
The dataset can be used for analyzing AI-assisted web development trends, studying user behavior in LLM-powered creative tools, and exploring the capabilities of language models in web design.
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Looking for a logo idea 👀 ?
I made a new cool space enzostvs/Logo.Ai to help you design a great logo in seconds!
Here are some examples of what you can do, feel free to share yours too! 🚀
I made a new cool space enzostvs/Logo.Ai to help you design a great logo in seconds!
Here are some examples of what you can do, feel free to share yours too! 🚀
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Introducing GUICourse! 🎉
By leveraging extensive OCR pretraining with grounding ability, we unlock the potential of parsing-free methods for GUIAgent.
📄 Paper: ( GUICourse: From General Vision Language Models to Versatile GUI Agents (2406.11317))
🌐 Github Repo: (https://github.com/yiye3/GUICourse)
📖 Dataset: ( yiye2023/GUIAct) / ( yiye2023/GUIChat) / ( yiye2023/GUIEnv)
🎯 Model: ( RhapsodyAI/minicpm-guidance) / ( RhapsodyAI/qwen_vl_guidance)
By leveraging extensive OCR pretraining with grounding ability, we unlock the potential of parsing-free methods for GUIAgent.
📄 Paper: ( GUICourse: From General Vision Language Models to Versatile GUI Agents (2406.11317))
🌐 Github Repo: (https://github.com/yiye3/GUICourse)
📖 Dataset: ( yiye2023/GUIAct) / ( yiye2023/GUIChat) / ( yiye2023/GUIEnv)
🎯 Model: ( RhapsodyAI/minicpm-guidance) / ( RhapsodyAI/qwen_vl_guidance)
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At Google I/O 2024, we're collaborating with the Google Visual Blocks team (https://visualblocks.withgoogle.com) to release custom Hugging Face nodes. Visual Blocks for ML is a browser-based tool that allows users to create machine learning pipelines using a visual interface. We're launching nodes with Transformers.js, running models on the browser, as well as server-side nodes running Transformers pipeline tasks and LLMs using our hosted inference. With
@Xenova
@JasonMayes
You can learn more about it here https://huggingface.co/blog/radames/hugging-face-google-visual-blocks
Source-code for the custom nodes:
https://github.com/huggingface/visual-blocks-custom-components
You can learn more about it here https://huggingface.co/blog/radames/hugging-face-google-visual-blocks
Source-code for the custom nodes:
https://github.com/huggingface/visual-blocks-custom-components