commit 35a6f0103489ce997a2096b6d74befedfa49e323 Author: ModelHub XC Date: Thu Jun 4 19:16:18 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: marin-community/delphi-1e21-3.4Bparams-46.3Btokens Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..27754b4 --- /dev/null +++ b/README.md @@ -0,0 +1,134 @@ +--- +license: apache-2.0 +library_name: transformers +pipeline_tag: text-generation +language: +- en +tags: +- marin +- delphi +- scaling-laws +- pretrained +- research-only +datasets: +- nvidia/Nemotron-CC +- bigcode/starcoderdata +- EleutherAI/proof-pile-2 +--- + +# delphi-1e21-3.4Bparams-46.3Btokens + +A 3.4B-parameter base model from the **Delphi** scaling suite. Trained at 1 × 10²¹ FLOPs on 46.3B tokens with the Delphi recipe. + +## About Delphi + +Delphi is the Marin team's first open scaling suite, inspired by +[Pythia](https://www.eleuther.ai/papers-blog/pythia-a-suite-for-analyzing-large-language-modelsacross-training-and-scaling). +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"](https://openathena.ai/blog/delphi) +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`](https://huggingface.co/marin-community) on the Hub. + +This is a research artifact, not a production model. + +## Model details + +| | | +|---|---| +| Architecture | Qwen 3 (pre-norm decoder, RMSNorm, RoPE, QK-norm with learned scaling, SwiGLU MLPs) | +| Parameters | 3,383,110,656 | +| Hidden size | 2560 | +| Layers | 26 | +| Attention heads | 20 | +| KV heads | 20 (no GQA) | +| Head dim | 128 | +| FFN intermediate | 10240 (MLP ratio 4) | +| Vocab size | 128,256 (Llama 3 tokenizer) | +| Max sequence length | 4096 | +| Position encoding | RoPE (θ = 500000, Llama 3-style scaling) | +| Bias terms | None | +| Tied embeddings | No | + +## Training + +| | | +|---|---| +| Compute | 1 × 10²¹ FLOPs | +| Tokens | 46,256,881,664 | +| Steps | 22,056 | +| Sequence length | 4096 | +| Optimizer | AdamH (Adam with Hyperball) | +| Recipe | Delphi (Complete(d)P-style scaling with `(T₀/T)^0.3` token-horizon LR correction) | +| LR schedule | WSD: 10% linear warmup, 20% linear decay, 0 floor | +| Precision | f32 master params, bf16 compute | +| Parallelism | FSDP | +| Data mixture | Nemotron-CC + StarCoderData + ProofPile 2 | +| Tokenizer | Llama 3 (vocab 128,256) | + +[AdamH](https://whenwen.github.io/wd_blog/public/hyperball-part-1.html), 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`](https://github.com/marin-community/marin/blob/main/experiments/scaling_law_sweeps/completed_adamh.py). + +## Companion releases + +- All Delphi model checkpoints: [`marin-community`](https://huggingface.co/marin-community) on the Hub. +- Plot data behind every figure in the blog post: + [`marin-community/delphi-blog-data`](https://huggingface.co/datasets/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`](https://github.com/marin-community/marin/blob/main/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`](https://huggingface.co/marin-community). + +## Citation + +```bibtex +@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} +} +``` diff --git a/config.json b/config.json new file mode 100644 index 0000000..c2a77da --- /dev/null +++ b/config.json @@ -0,0 +1,123 @@ +{ + "vocab_size": 128256, + "max_position_embeddings": 4096, + "hidden_size": 2560, + "intermediate_size": 10240, + "num_hidden_layers": 26, + "num_attention_heads": 20, + "use_sliding_window": false, + "sliding_window": null, + "max_window_layers": 28, + "num_key_value_heads": 20, + "head_dim": 128, + "hidden_act": "silu", + "initializer_range": 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