Train massive neural networks on your gaming PC with LoQT's memory tricks
LoQT introduces a memory-efficient training method that combines low-rank adapters with quantization, enabling training of 7B parameter models on a single 24GB GPU without model sharding or offloading. This breakthrough makes LLM training accessible on consumer hardware[3].
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https://arxiv.org/abs/2405.16528
🔍 Original Problem:
Training large neural networks demands extensive computational resources, making it impractical on consumer hardware without complex techniques like model sharding or gradient offloading[3].
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🛠️ Solution in this Paper:
→ LoQT initializes two low-rank factors for each weight matrix - one from gradient projections and another to minimize quantization error.
→ Only one matrix receives active optimization, significantly reducing gradient and optimizer state size compared to full training.
→ The product of these matrices merges periodically into the full-rank matrix with exponentially increasing gaps[3].
→ Non-updated matrices stay quantized for optimal memory usage.
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💡 Key Insights:
→ Large accumulated updates enable successful quantized model training, as small changes wouldn't register in quantized states[3].
→ Gradient-based tensor factorization provides effective initialization for low-rank adapters.
→ Exponentially increasing merge intervals align with model convergence patterns.
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📊 Results:
→ Successfully trains 7B parameter models on 24GB consumer GPUs
→ Enables 13B parameter model training with per-layer updates on same hardware
→ Suitable for both pretraining and fine-tuning tasks[3]
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