Programming Tutorials

  You have successfully downloaded the Llama 3 8B weights. You set up your PyTorch environment, configured a basic LoRA adapter, and launched the training script on your RTX 3090 or Google Colab T4. Then, before the first epoch even starts, you hit the wall:  RuntimeError: CUDA out of memory. Tried to allocate... This is the single most common barrier for engineers moving from using LLMs to fine-tuning them. It is frustrating because the math  seems  like it should work. If Llama 3 8B is roughly 15GB in half-precision (FP16), why does it crash a 24GB or even 40GB card? The answer lies in the hidden memory overhead of the training process itself. This guide provides a root cause analysis of VRAM consumption and a production-grade code solution using QLoRA (Quantized LoRA) to fit Llama 3 training pipelines onto consumer hardware. The Root Cause: Where Did the VRAM Go? To fix the OOM (Out Of Memory) error, you must understand what consumes GPU memory. It is not just the...

  Few things break a development flow faster than a   401 Unauthorized   error when you know your credentials are correct. If you are attempting to load Meta’s Llama 3 (or Llama 3.2) using the   transformers   library and receiving a "Repository Not Found" or 401 error, you are likely encountering a specific friction point regarding   gated model access . This is not a generic connectivity issue. It is a handshake failure between your local environment's authentication headers and the specific access requirements of the Meta Llama repositories on the Hugging Face Hub. Here is the root cause analysis and the definitive, production-grade solution to get your inference pipeline running. The Root Cause: Gated Repositories and API Obfuscation To resolve this, we must understand why the error message is often misleading. When you request  meta-llama/Meta-Llama-3-8B , the Hugging Face Hub API checks two things: Authentication:  Is the request accompanied...