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The solver.py refactoring renamed most solver classes from *Solver to *Model (e.g. DistributedSolver → DistributedModel). The docs still referenced the old names, causing the Quarto site build to fail with: ImportError: cannot import name 'DistributedSolver' from 'mlsysim' - Fix executable code cells in tutorials/distributed.qmd - Update non-executable code examples across 10 doc files - Rename 19 API reference files from *Solver.qmd to *Model.qmd - SensitivitySolver and SynthesisSolver retain their names (correct) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
44 lines
1.8 KiB
Plaintext
44 lines
1.8 KiB
Plaintext
# core.solver.ScalingModel { #mlsysim.core.solver.ScalingModel }
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```python
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core.solver.ScalingModel()
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```
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Analyzes the 'Scaling Physics' of model training (Chinchilla Laws).
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This solver determines the optimal model size (P) and dataset size (D)
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given a compute budget (C), following the compute-optimal training
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regime where D ≈ 20P.
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Literature Source:
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1. Hoffmann et al. (2022), "Training Compute-Optimal Large Language Models."
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2. Kaplan et al. (2020), "Scaling Laws for Neural Language Models."
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3. McCandlish et al. (2018), "An Empirical Model of Large-Batch Training."
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## Methods
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| Name | Description |
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| --- | --- |
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| [solve](#mlsysim.core.solver.ScalingModel.solve) | Solves for compute-optimal model and dataset parameters. |
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### solve { #mlsysim.core.solver.ScalingModel.solve }
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```python
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core.solver.ScalingModel.solve(compute_budget, target_model_size=None)
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```
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Solves for compute-optimal model and dataset parameters.
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#### Parameters {.doc-section .doc-section-parameters}
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| Name | Type | Description | Default |
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|-------------------|----------|---------------------------------------------------------------------------|------------|
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| compute_budget | Quantity | Total training budget (e.g., in TFLOPs or H100-GPU-days). | _required_ |
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| target_model_size | Quantity | If provided, calculates the required tokens for this specific model size. | `None` |
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#### Returns {.doc-section .doc-section-returns}
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| Name | Type | Description |
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|--------|------------------|-------------------------------------------------------------------|
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| | Dict\[str, Any\] | Optimal parameters, token count, and training duration estimates. |
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