This model predicts the performance of neural network configurations using scaling laws. It is trained on the Marin and StepLaw datasets to forecast performance metrics based on model configurations.

Model Description

NCPL-intermediate (Neural Configuration to Performance Scaling Law - Intermediate) is a specialized forecasting model that:

Takes pretraining configurations as input
Predicts intermediate performance metrics using learned scaling law patterns
Combines text embeddings from a base transformer with numeric value processing through a dedicated MLP
Supports multiple scaling law formulations (Marin, StepLaw)

Architecture

The model consists of:

Base Model: Qwen/Qwen3-1.7B
- Provides contextual embeddings for text tokens
Numeric MLP:
- Processes numeric values (performance metrics, configuration parameters)
- Projects numeric inputs to the same hidden dimension as text embeddings
- Architecture: Linear(1 → 2hidden_size) → ReLU → Linear(2hidden_size → hidden_size)
Prediction Head:
- Linear layer mapping from hidden_size to scalar predictions
- Outputs performance forecasts for each token position

Training Data

The model was trained on:

Datasets: Marin and StepLaw scaling law datasets
Training configuration:
- Stage 1: 10 epochs with learning rate 5e-5 (frozen base model)
- Stage 2: 400 epochs with learning rate 1e-5 (full fine-tuning)
- Batch size: 480 (across 8 GPUs)
- Weight decay: 0.01
- Loss: MSE (Mean Squared Error)

Usage

The ScalingLawForecaster class can be found in the GitHub repository.

python

import torch
from transformers import AutoTokenizer
# Get ScalingLawForecaster from: https://github.com/zhqwqwq/Configuration-to-Performance-Scaling-Law
from model import ScalingLawForecaster

# Load model
model = ScalingLawForecaster(
    base_model_name="Qwen/Qwen3-1.7B",
    init_from_pretrained=True,
    force_fp32=True
)

# Load checkpoint
checkpoint = torch.load("pytorch_model.bin")
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-1.7B")

# Prepare inputs
# input_ids: tokenized text sequence
# is_number_mask: boolean mask indicating which tokens are numeric
# number_values_filled: actual numeric values (0 for non-numeric tokens)

with torch.no_grad():
    predictions = model(
        input_ids=input_ids,
        is_number_mask=is_number_mask,
        number_values_filled=number_values_filled,
        attention_mask=attention_mask
    )

Input Format

The model expects three key inputs:

input_ids (torch.LongTensor): Tokenized sequence with special numeric tokens
is_number_mask (torch.BoolTensor): Boolean mask marking numeric token positions
number_values_filled (torch.FloatTensor): Actual numeric values at marked positions

Intended Use

This model is designed for:

Scaling law research: Understanding how neural network performance scales with configuration
Performance forecasting: Predicting model performance before full training
Configuration optimization: Finding optimal hyperparameters based on scaling patterns
Resource planning: Estimating computational requirements for different model sizes

Limitations

Trained specifically on Marin and StepLaw datasets; generalization to other settings likely require at least finetuning
Requires properly formatted inputs with numeric tokens replaced and masked

Citation

If you use this model in your research, please cite:

bibtex

@article{ncpl2026,
  title = {Neural Configuration to Performance Scaling Law},
  author = {Huaqing Zhang and Kaiyue Wen and Tengyu Ma},
  journal = {arXiv preprint arXiv:2602.10300},
  year = {2026},
  url = {https://www.arxiv.org/abs/2602.10300}
}

OptimizerStudy/NCPL-intermediate

NCPL-intermediate: Neural Configuration to Performance Scaling Law