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Both of your scripts receive a single PrismContext (ctx). It supplies the metadata and limits your model and loop need, and it controls everything the miner must not: the dataset, the seed, the scoring, and the held-out evaluation. Source: docs/submissions.md:56-74; src/prism_challenge/evaluator/interface.py.

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PrismContext fields and methods

Field / methodMeaningSource
vocab_size, max_seq_lenToken-id geometry for the modelsrc/prism_challenge/evaluator/interface.py:23-24, :49-50
max_paramsHard parameter cap (150M)src/prism_challenge/evaluator/interface.py:26, :53-54
seedThe forced seed (challenge-controlled; you cannot change it)docs/submissions.md:64
data_dirRead-only path to the locked FineWeb-Edu train splitdocs/submissions.md:65
artifacts_dirThe only writable path (rank-0 writes)docs/submissions.md:66
device, world_size, rank, local_rankDistributed launch geometrydocs/submissions.md:67
token_budget, step_budgetCompute budget for the rundocs/submissions.md:68
build_model()Helper that builds the model from architecture.pydocs/submissions.md:69
reference_tokenizer(name)Loads a pre-staged offline tokenizer ("gpt2" or "llama"); never touches the networksrc/prism_challenge/evaluator/interface.py:56-66
Source: docs/submissions.md:60-71.

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What you control — and what you do not

You provide model code and a training loop, not your own data. PRISM supplies and controls the dataset. The miner does not control:
  • the dataset content or splits;
  • the seed and initialization (forced by the harness);
  • the scoring;
  • the held-out evaluation.
Source: docs/submissions.md:72-73; docs/miner/README.md:72-74.

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Reading the locked data

Read raw text from ctx.data_dir and tokenize it with your own tokenizer or a pre-staged reference. The val/test splits are secret and never exposed to your script — only the challenge scorer reads them. The eval container runs with network=none, HF_HUB_OFFLINE=1, and HF_DATASETS_OFFLINE=1, so there is no network during training. Do not try to download data, tokenizers, or weights at runtime. Fail closed if the locked data is missing rather than fabricating data. Source: docs/miner/README.md:76-81; docs/submissions.md:91-94.

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Reference tokenizers

ctx.reference_tokenizer(name) loads a pre-staged tokenizer entirely offline. Two references are available: "gpt2" (via a tiktoken cache) and "llama" (via a sentencepiece .model). Using a reference tokenizer never touches the network. 
def train(ctx):
    tok = ctx.reference_tokenizer("gpt2")
    ...
Source: src/prism_challenge/evaluator/interface.py:56-66; docs/miner/README.md:70.
Because the score normalizes by raw UTF-8 bytes, the metric is tokenizer-agnostic — you can bring any tokenizer and still be compared like for like. See Scoring.

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Distributed geometry

ctx.world_size, ctx.rank, ctx.local_rank, and ctx.device describe the launch shape. The harness launches torchrun --standalone --nnodes=1 --nproc-per-node=<gpu_count> and exposes WORLD_SIZE, RANK, and LOCAL_RANK. Your loop must also work correctly at world_size=1, because the official scored run uses one physical GPU. Source: docs/submissions.md:96-110; docs/scaling.md:22-35. See Constraints for the sandbox, the parameter cap, and the multi-GPU bounds.