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Fine-tuningFreeOpen Source
DEEPSPEED
Distributed training and optimization for large models
Apache-2.0
ABOUT
Training and fine-tuning large models with billions of parameters is prohibitively memory-intensive. Standard approaches either require dozens of high-end GPUs or simply fail on consumer hardware. DeepSpeed solves this with ZeRO memory optimization that partitions model states across GPUs, offloads to CPU or NVMe, and enables 3D parallelism (data, pipeline, tensor) — reducing GPU memory requirements by up to 8x while maintaining training throughput and model quality so teams can fine-tune LLMs on far fewer GPUs than previously possible.
INSTALL
pip install deepspeedINTEGRATION GUIDE
1. Fine-tune large language models (Llama, GPT) on limited GPU hardware using ZeRO stage 2 or 3 optimization
2. Train Mixture-of-Experts (MoE) models at scale with memory-efficient distributed parallelism
3. Run distributed fine-tuning through HuggingFace Transformers with the --deepspeed flag for seamless integration
4. Offload optimizer states and parameters to CPU or NVMe to fit models larger than available GPU memory
TAGS
fine-tuningdistributed-trainingzerollmdeep-learningpytorchmixed-precisionmodel-parallelism