Multi Scale Model Family With Parameter Efficiency Benchmarking

via “example model solutions with multi-size performance reference”

8.5K grade school math problems — multi-step reasoning, verifiable solutions, reasoning benchmark.

Unique: Pre-computed solutions from multiple model sizes in a single standardized file enable direct comparison of how model scale affects reasoning quality without requiring researchers to re-run inference on large models, reducing computational overhead for benchmarking studies

vs others: More convenient than running inference on reference models yourself (no compute cost) but less flexible than dynamic baselines that could be updated as new models emerge

via “training efficiency benchmarking and comparison across scales”

* ⭐ 04/2022: [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (SayCan)](https://arxiv.org/abs/2204.01691)

Unique: Systematically benchmarks training efficiency across a wide range of model sizes (70M to 540B) and token counts, revealing that compute-optimal allocation (N ≈ D) achieves ~20% better efficiency than undertrained or overtrained alternatives. Provides empirical efficiency curves rather than theoretical predictions.

vs others: More comprehensive efficiency analysis than prior work by testing both parameter and token scaling; reveals that equal scaling is optimal, contradicting prior assumptions of undertrained models being more efficient

via “scaling law analysis and parameter efficiency evaluation”

Gopher by DeepMind is a 280 billion parameter language model.

via “multi-scale model family with parameter-efficiency benchmarking”

* 📰 03/2023: [GPT-4](https://openai.com/research/gpt-4)

Unique: Provides four independently-trained model scales with published benchmark comparisons showing that 13B outperforms GPT-3 (175B), enabling empirical parameter-efficiency analysis without distillation or pruning — a rare transparency in the foundation model space.

vs others: Unlike GPT-3 (single 175B model) or Chinchilla (limited scale variants), LLaMA's multi-scale family enables cost-optimized deployment with published evidence that smaller variants match larger competitors, reducing inference costs by 10-100x for equivalent performance.

via “multi-size-model-selection”

via “scalable-model-selection”