StepLaw-N_268M-D_4.0B-LR7.812e-03-BS2097152

Wandb Model Name: step2v2_0618_h1024_ffnh9552_numh16_numl8_lr7.812e-03_bs1024_ti2384_mlr1.00e-05

This model is part of the StepLaw-N_268M-D_4.0B collection.

Model Specifications

Architecture

• Hidden size (H): 1024
• Feed-forward network size (FFN): 9552
• Attention heads: 16
• Layers: 8
• Parameter count: 268M

Training Parameters

• Learning rate (lr): 7.812e-03
• Batch size (bs): 2097152
• Training iterations: 2384
• Training tokens (D): 5.0B

Model Description

StepLaw models are trained with various hyperparameter settings to enable research on scaling laws and hyperparameter optimization. This specific model was trained with learning rate 7.812e-03 and batch size 2097152 for 2384 iterations, using a total of 5.0B training tokens.

Usage Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "StepLaw/StepLaw-N_268M-D_4.0B-LR7.812e-03-BS2097152"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

# Generate text
inputs = tokenizer("A long time ago in a galaxy far, far away", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))