This is super nice, thanks for sharing. Using gh issue => pr => deployment flow is good, but it would be awesome to have an optional local dev flow so the iterations can go even faster.
yes, now I found the time mainly caused by:
1. openAI API call, which could not be optimized for now
2. the time to build and deploy, which can be optimized by a preset setup
"LoftQ aims to solve the problem of the discrepancy between the quantized and full-precision model in the context of quantization and LoRA fine-tuning for Large Language Models (LLMs). By simultaneously quantizing an LLM and finding a proper low-rank initialization for LoRA fine-tuning, LoftQ significantly enhances generalization in downstream tasks."
"Based on the abstract, LoftQ aims to solve the performance gap observed when applying both quantization and LoRA fine-tuning to a pre-trained Large Language Model (LLM).
Here's a breakdown of the problem and LoftQ's approach:
Problem:
Quantization: Reduces the precision of model weights to save memory and computation, but can lower accuracy.
LoRA fine-tuning: Improves accuracy on specific tasks by adding a low-rank adapter, but can struggle with quantized models.
Combined approach: Applying both quantization and LoRA fine-tuning often leads to a performance gap compared to full fine-tuning.
LoftQ's solution:
Simultaneous quantization and LoRA initialization: LoftQ proposes a novel framework that quantizes the LLM while also finding a suitable low-rank initialization for LoRA. This helps bridge the gap between the quantized and full-precision model.
Improved generalization: This approach improves the model's ability to generalize well on downstream tasks, especially in challenging memory-constrained settings.
Evaluation and results:
LoftQ is tested on various NLP tasks like question answering and summarization.
It outperforms existing quantization methods, particularly in low-precision scenarios like 2-bit and 2/4-bit mixed precision.
Overall, LoftQ tackles the challenge of combining quantization and LoRA fine-tuning for LLMs, leading to better performance and efficiency, especially in resource-limited environments."
Nice work, thanks for making it! A few nice-to-haves:
1. it didn’t mention in readme that ollama has to be started manually in terminal. But I figured that out.
2. have a short video showing how it works in coding work, especially for people who never used gh copilot.
