Good folks at Meta has just unveiled Llama 3.2, pushing the boundaries of language models and computer vision.
Even more interesting is how they trained this cutting-edge model:
1๏ธโฃ Architecture:
Llama 3.2 uses an optimized transformer architecture with auto-regressive capabilities. The largest models (11B and 90B) now support multimodal inputs, integrating both text and images.
2๏ธโฃ Training Pipeline:
โข Started with pretrained Llama 3.1 text models
โข Added image adapters and encoders
โข Pretrained on large-scale noisy (image, text) pair data
โข Fine-tuned on high-quality in-domain and knowledge-enhanced (image, text) pairs
3๏ธโฃ Vision Integration:
โข Trained adapter weights to integrate a pre-trained image encoder
โข Used cross-attention layers to feed image representations into the language model
โข Preserved text-only capabilities by not updating language model parameters during adapter training
4๏ธโฃ Post-Training Alignment:
โข Multiple rounds of supervised fine-tuning (SFT)
โข Rejection sampling (RS)
โข Direct preference optimization (DPO)
โข Synthetic data generation using Llama 3.1 for Q&A augmentation
โข Reward model ranking for high-quality fine-tuning data
5๏ธโฃ Lightweight Models:
โข Used pruning and distillation techniques for 1B and 3B models
โข Structured pruning from Llama 3.1 8B model
โข Knowledge distillation using Llama 3.1 8B and 70B as teachers
6๏ธโฃ Context Length:
All models support an impressive 128K token context length.
7๏ธโฃ Safety Measures:
Incorporated safety mitigation data to balance helpfulness and safety.
The result? A suite of models ranging from edge-friendly 1B parameters to powerful 90B parameter versions, capable of sophisticated reasoning across text and images. Llama 3.2 is set to revolutionize AI applications from mobile devices to enterprise-scale solutions.
What are your thoughts on these advancements? How do you see Llama 3.2 impacting your industry? Let's discuss in the comments!