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John Locke

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AI & ML interests

NLP, OCR, AI

Recent Activity

reacted to singhsidhukuldeep's post with 🚀 2 days ago
Exciting breakthrough in AI: @Meta's new Byte Latent Transformer (BLT) revolutionizes language models by eliminating tokenization! The BLT architecture introduces a groundbreaking approach that processes raw bytes instead of tokens, achieving state-of-the-art performance while being more efficient and robust. Here's what makes it special: >> Key Innovations Dynamic Patching: BLT groups bytes into variable-sized patches based on entropy, allocating more compute power where the data is more complex. This results in up to 50% fewer FLOPs during inference compared to traditional token-based models. Three-Component Architecture: • Lightweight Local Encoder that converts bytes to patch representations • Powerful Global Latent Transformer that processes patches • Local Decoder that converts patches back to bytes >> Technical Advantages • Matches performance of Llama 3 at 8B parameters while being more efficient • Superior handling of non-English languages and rare character sequences • Remarkable 99.9% accuracy on spelling tasks • Better scaling properties than token-based models >> Under the Hood The system uses an entropy model to determine patch boundaries, cross-attention mechanisms for information flow, and hash n-gram embeddings for improved representation. The architecture allows simultaneous scaling of both patch and model size while maintaining fixed inference costs. This is a game-changer for multilingual AI and could reshape how we build future language models. Excited to see how this technology evolves!
liked a Space 12 days ago
sivan22/Ituria
new activity 14 days ago
Gabriel/Qwen2-VL-2B-Instruct:Model inference
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