Ask HN: Can you crowdfund the compute for GPT?

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • petals

    🌸 Run LLMs at home, BitTorrent-style. Fine-tuning and inference up to 10x faster than offloading

  • Yes: https://github.com/bigscience-workshop/petals

  • ai_and_memory_wall

    AI and Memory Wall

  • Depends on which version you're running. According to https://github.com/amirgholami/ai_and_memory_wall

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • metaseq

    Repo for external large-scale work

  • https://github.com/facebookresearch/metaseq/blob/main/projec... [PDF]

    It's a 114 page document detailing the months of full-time work for multiple engineers that went into training a 175B language model at meta AI.

  • GLM-130B

    GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)

  • https://github.com/THUDM/GLM-130B might be a useful place to look

  • hivemind

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

  • Probably the biggest recent result: https://arxiv.org/abs/2209.04836

    See also: https://github.com/learning-at-home/hivemind

    and more to OP's incentive structure: https://docs.bittensor.com/

    Latter two intend to beat latency with Mixture-of-Expert models (MOEs). If the results of the former hold, it shows that with a simple algorithmic transformation you can merge two independently trained models in weight-space and have performance functionally equivalent to a model trained monolithically.

  • SaaSHub

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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