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Recent developments in the field of vector embeddings have shown significant advancements in reducing memory usage while maintaining retrieval accuracy. Strategies such as binary quantization and product quantization have been implemented to achieve this goal. For instance, binary embeddings have successfully reduced memory usage by over 98% while retaining more than 90% of model performance. New quantization methods like QuIP and LoftQ have also emerged, offering improved preprocessing and outperforming existing quantization techniques for large language models.
🤖 From this week's issue: An article that introduces the concept of embedding quantization and showcases its impact on retrieval speed, memory usage, disk space, and cost. https://t.co/pS7tbDDBd6
"LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models" - outperforming existing quantization methods. 🔥 📌 This Paper proposes LoftQ (LoRA-Fine-Tuning-aware Quantization), a novel quantization framework that simultaneously quantizes an LLM and finds a proper… https://t.co/THzfWIdHxH
"QuIP: 2-Bit Quantization of Large Language Models With Guarantees" - huge promise for the GPU-poor ✨ Finds that its preprocessing improves several existing quantization algorithms and yields the first LLM quantization methods that produce viable results using only two bits per… https://t.co/J2L0Dweex1
