AntleRot (Kev Daragon)

🚀 Introducing 𝐅𝐢𝐫𝐬𝐭 𝐇𝐮𝐠𝐠𝐢𝐧𝐠 𝐅𝐚𝐜𝐞 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐦𝐢𝐧𝐆𝐑𝐔 𝐌𝐨𝐝𝐞𝐥𝐬 from the paper 𝐖𝐞𝐫𝐞 𝐑𝐍𝐍𝐬 𝐀𝐥𝐥 𝐖𝐞 𝐍𝐞𝐞𝐝𝐞𝐝?

🖥 I have integrated 𝐧𝐞𝐱𝐭-𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐑𝐍𝐍𝐬, specifically minGRU, which offer faster performance compared to Transformer architectures, into HuggingFace. This allows users to leverage the lighter and more efficient minGRU models with the "𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬" 𝐥𝐢𝐛𝐫𝐚𝐫𝐲 for both usage and training.

💻 I integrated two main tasks: 𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 and 𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐂𝐚𝐮𝐬𝐚𝐥𝐋𝐌.

𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧:
You can use this class for 𝐒𝐞𝐪𝐮𝐞𝐧𝐜𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 tasks. I also trained a Sentiment Analysis model with stanfordnlp/imdb dataset.

𝐌𝐢𝐧𝐆𝐑𝐔𝐅𝐨𝐫𝐂𝐚𝐮𝐬𝐚𝐥𝐋𝐌:
You can use this class for 𝐂𝐚𝐮𝐬𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 tasks such as GPT, Llama. I also trained an example model with roneneldan/TinyStories dataset. You can fine-tune and use it!

🔗 𝐋𝐢𝐧𝐤𝐬:
Models: suayptalha/mingru-676fe8d90760d01b7955d7ab
GitHub: https://github.com/suayptalha/minGRU-hf
LinkedIn Post: https://www.linkedin.com/posts/suayp-talha-kocabay_mingru-a-suayptalha-collection-activity-7278755484172439552-wNY1

📰 𝐂𝐫𝐞𝐝𝐢𝐭𝐬:
Paper Link: https://arxiv.org/abs/2410.01201

I am thankful to Leo Feng, Frederick Tung, Mohamed Osama Ahmed, Yoshua Bengio and Hossein Hajimirsadeghi for their papers.

reacted to Jaward's post with ❤️ 9 days ago

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nanoBLT: Simplified lightweight implementation of a character-level Byte Latent Transformer model (under 500 lines of code). The model is 2x4x2 (n_layers_encoder, n_layers_latent, n_layers_decoder) layer deep trained on ~1M bytes of tiny Shakespeare with a patch size of 4.

Code: https://github.com/Jaykef/ai-algorithms/blob/main/byte_latent_transformer.ipynb

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🎨 KLING AI Dataset - nyuuzyou/klingai

A collection of 12,782 AI-generated media items featuring:
- High-quality image and video generations at various resolutions
- Complete metadata including user IDs, prompts, and generation parameters
- Content generated using text-to-image, text-to-video, and image-to-video modalities
- Full generation settings and technical parameters

reacted to csabakecskemeti's post with 🔥 9 days ago

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I've built a small utility to split safetensors file by file.
The issue/need came up when I've tried to convert the new Deepseek V3 model from FP8 to BF16.
The only Ada architecture GPU I have is an RTX 4080 and the 16GB vram was just wasn't enough for the conversion.

BTW: I'll upload the bf16 version here:
DevQuasar/deepseek-ai.DeepSeek-V3-Base-bf16
(it will take a while - days with my upload speed)
If anyone has access the resources to test it I'd appreciate a feedback if it's working or not.

The tool, is available from here:
https://github.com/csabakecskemeti/ai_utils/blob/main/safetensor_splitter.py
It's splitting every file to n pieces by the layers if possible, and create a new "model.safetensors.index.json" file.
I've tested it with Llama 3.1 8B and multiple split sizes, and validated by using inference pipeline.
use --help for usage
Please note current version expects the model is already multiple file and have a "model.safetensors.index.json" layer-safetensor mapping file.

reacted to hexgrad's post with 🔥 9 days ago

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🇬🇧 Four British voices have joined hexgrad/Kokoro-82M (Apache TTS model): bf_emma, bf_isabella, bm_george, bm_lewis

reacted to YannisTevissen's post with 🤗 9 days ago

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Starting this collection to gather models, spaces, dataset or even papers related to disability. Feel free to ping me if you see something relevant to add

YannisTevissen/ai-for-disability-67684a1a9966a2e699f6b114

liked a model 9 days ago

deepseek-ai/DeepSeek-V3

Updated 9 days ago • 74.1k • 1.41k

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🌟 PawMatchAI: Making Breed Selection More Intuitive! 🐕
Excited to share the latest update to this AI-powered companion for finding your perfect furry friend! The breed recommendation system just got a visual upgrade to help you make better decisions.

✨ What's New?
Enhanced breed recognition accuracy through strategic model improvements:
- Upgraded to a fine-tuned ConvNeXt architecture for superior feature extraction
- Implemented progressive layer unfreezing during training
- Optimized data augmentation pipeline for better generalization
- Achieved 8% improvement in breed classification accuracy

🎯 Key Features:
- Smart breed recognition powered by AI
- Visual matching scores with intuitive color indicators
- Detailed breed comparisons with interactive tooltips
- Lifestyle-based recommendations tailored to your needs

💭 Project Vision
Combining my passion for AI and pets, this project represents another step toward my goal of creating meaningful AI applications. Each update aims to make the breed selection process more accessible while improving the underlying technology.

👉 Try it now: DawnC/PawMatchAI

Your likes ❤️ on this space fuel this project's growth!

#AI #MachineLearning #DeepLearning #Pytorch #ComputerVision
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