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

1️⃣ 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.

2️⃣ Training Pipeline:
• Started with pretrained Llama 3.1 text models
• Added image adapters and encoders
• Pretrained on large-scale noisy (image, text) pair data
• Fine-tuned on high-quality in-domain and knowledge-enhanced (image, text) pairs

3️⃣ Vision Integration:
• Trained adapter weights to integrate a pre-trained image encoder
• Used cross-attention layers to feed image representations into the language model
• Preserved text-only capabilities by not updating language model parameters during adapter training

4️⃣ Post-Training Alignment:
• Multiple rounds of supervised fine-tuning (SFT)
• Rejection sampling (RS)
• Direct preference optimization (DPO)
• Synthetic data generation using Llama 3.1 for Q&A augmentation
• Reward model ranking for high-quality fine-tuning data

5️⃣ Lightweight Models:
• Used pruning and distillation techniques for 1B and 3B models
• Structured pruning from Llama 3.1 8B model
• Knowledge distillation using Llama 3.1 8B and 70B as teachers

6️⃣ Context Length:
All models support an impressive 128K token context length.

7️⃣ 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!

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