LLM Training & Fine-tuning Technologies
Technical deep dive into the core technologies powering modern large language model development and optimization.
PyTorch
PyTorch is a dynamic computational graph framework that serves as the foundation for modern deep learning development. It implements automatic differentiation through a tape-based autograd system, enabling dynamic neural network architectures. Key features include eager execution, CUDA support with automatic memory management, and distributed training primitives through the torch.distributed package. PyTorch's C++ frontend (LibTorch) enables high-performance production deployment, while its Python API provides flexibility for research and development.
Transformers
The Transformers library, developed by Hugging Face, provides state-of-the-art implementations of transformer architectures. It includes the PreTrainedModel and PreTrainedTokenizer abstractions, enabling consistent interfaces across different model architectures (BERT, GPT, T5, etc.). The library implements efficient attention mechanisms including sliding window attention, sparse attention patterns, and optimized key-value caching for inference. It supports model parallelism through device maps, gradient checkpointing for memory efficiency, and integrates with accelerate for distributed training.
PEFT (Parameter-Efficient Fine-Tuning)
PEFT encompasses techniques for adapting large language models while minimizing memory and computational requirements. Key methods include LoRA (Low-Rank Adaptation) which adds trainable rank decomposition matrices to frozen model weights, reducing memory by 95%+ compared to full fine-tuning. Prefix Tuning prepends trainable continuous prompts to inputs, while Prompt Tuning optimizes soft prompts in continuous embedding space. QLoRA enables fine-tuning using 4-bit quantization with double quantization and paged optimizers, allowing training of 65B+ parameter models on consumer GPUs.
bitsandbytes
bitsandbytes provides hardware-accelerated 4-bit and 8-bit quantization for LLMs. It implements LLM.int8() quantization which uses mixed-precision techniques, maintaining FP16 precision for outlier features while quantizing the majority to INT8. The library provides optimized CUDA kernels for quantized operations, including specialized optimizers like Lion8bit and PagedAdamW8bit that operate directly on quantized weights. It supports stable quantization through calibration and includes modules for automated mixed-precision training with quantized backpropagation.