LLM-RAG QLoRA Inference Optimization
What it is
An end-to-end pipeline that combines Retrieval-Augmented Generation (RAG) with QLoRA 4-bit quantization to handle long-context generation. It implements FlashAttention-2 and KV Caching to optimize memory usage and latency when processing large medical datasets.
The problem it solves
Out-of-Memory (OOM) errors and high latency when processing large context windows in Large Language Models.
What you could use it for
- Generating clinical summaries from large medical documents (e.g., 30,000 tokens) on limited GPU hardware.
- Benchmarking the impact of KV Cache and FlashAttention-2 on inference speed and VRAM usage.
- Simulating RAG systems with massive context windows in a constrained memory environment.
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.