Summary of Apiq: Finetuning Of 2-bit Quantized Large Language Model, by Baohao Liao et al.
ApiQ: Finetuning of 2-Bit Quantized Large Language Model
by Baohao Liao, Christian Herold, Shahram Khadivi, Christof Monz
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in interest for memory-efficient fine-tuning of large language models (LLMs) has been driven by the increasing size of these models and the constraints imposed by GPU memory limitations. The current approaches to memory-efficient fine-tuning, such as QLoRA, exhibit inconsistent performance across diverse bit-width quantizations and multifaceted tasks. This inconsistency is largely due to the detrimental impact of the quantization process on preserved knowledge, leading to catastrophic forgetting and undermining the utilization of pre-trained models for fine-tuning purposes. To address this issue, we introduce a novel quantization framework, ApiQ, designed to restore the lost information from quantization by concurrently initializing LoRA components and quantizing the weights of LLMs. This approach ensures the maintenance of the original LLM’s activation precision while mitigating error propagation from shallower into deeper layers. Our experiments demonstrate that ApiQ consistently achieves superior fine-tuning results across various bit-widths, thereby minimizing activation error during quantization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting bigger and need to be trained more efficiently. To do this, researchers have been working on ways to make the training process use less memory, but so far it’s been hit or miss. The problem is that when you compress the model to make it smaller, some of the information gets lost, which can cause problems during fine-tuning. We’ve developed a new way to compress models, called ApiQ, that helps keep more of the original information and makes training more reliable. |
Keywords
* Artificial intelligence * Fine tuning * Lora * Precision * Quantization
