marin-community/delphi-9e18-998Mparams-1.5Btokens

delphi-9e18-998Mparams-1.5Btokens

A 998M-parameter base model from the Delphi scaling suite. Trained at 9 × 10¹⁸ FLOPs on 1.5B tokens with the Delphi recipe.

About Delphi

Delphi is the Marin team's first open scaling suite, inspired by Pythia. It has three parts:

• —a scaling recipe that maps compute budgets to model configurations,
• —a scaling suite of models trained from that recipe at IsoFLOP budgets from 3 × 10¹⁸ to 1 × 10²³ FLOPs, and
• —a scaling law which uses the smaller Delphi models to predict the larger ones.

A pre-registered forecast from that scaling law predicted the final loss of the largest Delphi run (1 × 10²³ FLOPs, 25 B parameters, 600 B tokens) within 0.2%, using 300× less compute than the training run itself. The same process forecasts downstream benchmarks — MMLU, HumanEval, and GSM8K — via a two-step regression combining compute and observational scaling laws.

See "Scaling Laws That Extrapolate 300× Past the Fit" for the recipe, fit, and downstream-eval projections. The full set of Delphi checkpoints — IsoFLOP grid points, held-out optima at 1e21/1e22/1e23 with multiple random seeds, and training intermediates — lives on `marin-community` on the Hub.

This is a research artifact, not a production model.

Model details

Training

AdamH, Adam with Hyperball, constrains every projection weight to stay on the Frobenius- norm sphere it was initialized on, so weight decay has nothing to regularize away and falls out of the recipe. A Complete(d)P-style transfer rule with a (T₀/T)^0.3 correction sets learning rate as token horizon grows. Reference constants: B₀ = 64, T₀ = 2.5 B tokens, η₀ = 0.00630, η₀,Adam = 0.000656, ε₀ = 1.85 × 10⁻⁸. Recipe code: `experiments/scaling_law_sweeps/completed_adamh.py`.

Companion releases

• —All Delphi model checkpoints: `marin-community` on the Hub.
• —Plot data behind every figure in the blog post: `marin-community/delphi-blog-data` (one config per figure, with wandb_url on every row).
• —Pipelines that deterministically reproduce the training mixture from public Nemotron-CC, StarCoderData, and ProofPile 2 sources: see the Marin repo.

Evaluation

This checkpoint is part of the Delphi eval suite (`experiments/exp1337_eval_suite.py`), which scores every Delphi run alongside reference open-weights baselines (Qwen 3, Llama 2/3, OLMo 2, Marin 8B). Following the blog's two-step forecast, soft metrics (per-choice log-prob for multiple-choice tasks, bits-per-byte for generative tasks) carry the signal the scaling law is fit on, and a sigmoid fit on an external model pool maps soft metric to hard metric (accuracy, pass@1, exact-match). Below ~1e21 FLOPs the hard metrics stay near chance even when the underlying probabilities are improving smoothly; that is expected and is exactly why the soft metrics exist.

Limitations

• —Trained on an English-heavy web mixture; no multilingual coverage.
• —Pretrained-only — no instruction tuning, RLHF, or safety alignment.
• —The Delphi recipe targets compute-optimal training, not inference-cost-aware overtraining; for inference-heavy deployments, an overtrained smaller model may be preferable. The blog's "off-optimal training" section quantifies the penalty.
• —This is one checkpoint in a much larger Delphi release; pick the one that matches your compute / parameter / token regime, or browse the full set at `marin-community`.

Citation

@misc{held2026delphi,
  title  = {Scaling Laws That Extrapolate 300× Past the Fit},
  author = {Held, Will and {Marin Community}},
  year   = {2026},
  url    = {https://openathena.ai/blog/delphi}
}