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BITSANDBYTES
Accessible LLMs via k-bit quantization for PyTorch
MIT
ABOUT
Training and running large language models requires massive GPU memory that is out of reach for most developers and organizations. bitsandbytes solves this by providing k-bit quantization techniques — 8-bit optimizers for training, LLM.int8() for inference, and 4-bit QLoRA for fine-tuning — that dramatically reduce memory usage while maintaining model quality. This makes it feasible to fine-tune and run large models on consumer GPUs, democratizing access to state-of-the-art AI.
INSTALL
pip install bitsandbytesINTEGRATION GUIDE
1. Fine-tune a 7B+ parameter LLM on a single consumer GPU using QLoRA 4-bit quantization
2. Run inference on large models with half the memory using LLM.int8() without quality degradation
3. Train large models with 8-bit optimizers that save up to 75% memory while matching 32-bit performance
4. Enable LLM workloads on resource-constrained environments like laptops or edge devices
5. Integrate memory-efficient training into any PyTorch pipeline with a simple library import
TAGS
quantizationfine-tuningpytorchqloramemory-optimizationllmgpu