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Kadir Erturk
KadirErturk
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http://www.kadirerturk.com
kadirerturk
erturkkadir
AI & ML interests
sensors and machine learning
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Good folks at @nvidia and @Tsinghua_Uni have released LLAMA-MESH - A Revolutionary Approach to 3D Content Generation! This innovative framework enables the direct generation of 3D meshes from natural language prompts while maintaining strong language capabilities. Here is the Architecture & Implementation! >> Core Components Model Foundation - If you haven't guessed it yet, it's built on the LLaMA-3.1-8B-Instruct base model - Maintains original language capabilities while adding 3D generation - Context length is set to 8,000 tokens 3D Representation Strategy - Uses the OBJ file format for mesh representation - Quantizes vertex coordinates into 64 discrete bins per axis - Sorts vertices by z-y-x coordinates, from lowest to highest - Sorts faces by the lowest vertex indices for consistency Data Processing Pipeline - Filters meshes to a maximum of 500 faces for computational efficiency - Applies random rotations (0°, 90°, 180°, 270°) for data augmentation - Generates ~125k mesh variations from 31k base meshes - Uses Cap3D-generated captions for text descriptions >> Training Framework Dataset Composition - 40% Mesh Generation tasks - 20% Mesh Understanding tasks - 40% General Conversation (UltraChat dataset) - 8x training turns for generation, 4x for understanding Training Configuration - Deployed on 32 A100 GPUs (for Nvidia, this is literally in-house) - 21,000 training iterations - Global batch size: 128 - AdamW optimizer with a 1e-5 learning rate - 30-step warmup with cosine scheduling - Total training time: approximately 3 days (based on the paper) This research opens exciting possibilities for intuitive 3D content creation through natural language interaction. The future of digital design is conversational!
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Helsinki-NLP/opus-mt-tc-big-tr-en
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