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16-year-old trains 780B parameter LLM, Ganymede 1.1, with MoE architecture

A 16-year-old developer named Afonso has detailed the year-long process of training his own 780 billion parameter language model, Ganymede 1.1. He emphasized that using a mixture-of-experts (MoE) architecture was crucial for managing compute limitations, and that data cleaning proved more time-consuming than training itself. Afonso also highlighted the importance of building safety features into the model from the outset and noted that distributed training across multiple data centers was essential for accessing the necessary GPU resources. The Ganymede 1.1 model, which features a 1 million token context window and supports tool calling, is currently available for free use via chat and an OpenAI-compatible API. AI

IMPACT Demonstrates the feasibility of individual developers training large-scale models, potentially lowering barriers to entry for advanced AI development.

RANK_REASON The item describes the development and release of a large language model by an individual, detailing the technical process and learnings, which falls under research and development in AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

16-year-old trains 780B parameter LLM, Ganymede 1.1, with MoE architecture

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The item describes the development and release of a large language model by an individual, detailing the technical process and learnings, which falls under research and development in AI. [lever_c_…
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Afonso Faro ·

    I'm 16 and spent a year training my own 780B language model. Here's what I learned

    <p>Hi, I'm Afonso. I'm 16, and for about the past year I've been building Ganymede, my own large language model, mostly on my own under a small lab I started called Merkium.</p> <p>Most write-ups about training LLMs come from big labs with huge teams. I wanted to share what it ac…