Examples

examples/tiny-1m/
  prism.yaml         # declares the architecture + training entrypoints and the tokenizer
  architecture.py    # exposes build_model(ctx); defines the model only
  training.py        # exposes train(ctx); the miner-owned loop

architecture:
  entrypoint: architecture.py
training:
  entrypoint: training.py
tokenizer: gpt2

import torch

class TinyModel(torch.nn.Module):
    def __init__(self, vocab_size):
        super().__init__()
        self.embedding = torch.nn.Embedding(vocab_size, 8)
        self.linear = torch.nn.Linear(8, vocab_size)

    def forward(self, tokens):
        return self.linear(self.embedding(tokens))

def build_model(ctx):
    return TinyModel(ctx.vocab_size)

from architecture import build_model

def train(ctx):
    model = build_model(ctx)
    # construct the optimizer/schedule, read ctx.data_dir, tokenize, run the loop,
    # handle multi-GPU, and write only under ctx.artifacts_dir.
    ...

POST /v1/submissions
Content-Type: application/json

{
  "filename": "project.zip",
  "code": "<base64 zip payload>",
  "metadata": {}
}