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sparklerz/README.md

Sarat Kannan

I build scalable LLM training/inference systems across distributed multi-GPU (PyTorch DDP/FSDP, DeepSpeed ZeRO, Ray, MosaicML LLM Foundry) and decentralised swarms (Hivemind, Petals). Writing experiments up so others can reproduce them.

Selected work

Writing

Medium: https://medium.com/@kannansarat9

Contact

DMs open: https://x.com/saratkannan

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  1. multigpu-llm-finetuningmultigpu-llm-finetuningPublic

    This repository showcases hands-on projects leveraging distributed multi-GPU training to fine-tune large language models (LLMs).

    Python

  2. hivemind-qwen2-0.5bhivemind-qwen2-0.5bPublic

    Internet-scale data-parallel fine-tuning of Qwen2-0.5B-Instruct using Hivemind + TorchTune. Initial peer on public IP; second peers on free GPUs (e.g., Kaggle).

  3. petals-llama2-70bpetals-llama2-70bPublic

    Decentralised inference + Prompt tuning of LLaMA-2-70B with Petals (swarm model-parallelism). Meta-repo with summary and links to the two write-ups.

  4. hivemind-modified-for-torchtunehivemind-modified-for-torchtunePublic

    Forked from learning-at-home/hivemind

    Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.

    Python 1

  5. torchtunetorchtunePublic

    Forked from meta-pytorch/torchtune

    PyTorch native finetuning library

    Python 1