Rohan's Bytes

Reducing LLM Inference Costs While Preserving Performance

Improving LLM Performance - Scaling vs. Retrieval vs. Fine-Tuning

Keeping LLMs Updated Without Full Retraining: Approaches & Trade-Offs

Extending LLMs to Support Multimodal Inputs

Ensuring Fairness and Minimizing Bias in LLMs

Full end-to-end pipeline for developing and deploying an LLM-powered application

Emergent abilities in LLMs

Stabilizing LLM Training: Techniques and Insights

Distributed Training of Large Language Models Across Multiple GPUs or Machines

Designing an LLM-based system for adaptability and maintainability

Deploying LLMs on Resource-Constrained Devices

Deploying LLMs in highly regulated industries like Healthcare and Finance A Comprehensive Review