StepLaw/StepLaw-N_214M-D_19.0B-LR9.77E-04-BS2097152

Wandb Model Name: step2v2_0618_h960_ffnh9368_numh15_numl7_lr9.77E-04_bs1024_ti9536_mlr1.00E-05

This model is part of the StepLaw-N_214M-D_19.0B collection.

Model Specifications

Architecture

• Hidden size (H): 960
• Feed-forward network size (FFN): 9368
• Attention heads: 15
• Layers: 7
• Parameter count: 214M

Training Parameters

• Learning rate (lr): 9.77E-04
• Batch size (bs): 2097152
• Training iterations: 9536
• Training tokens (D): 20.0B

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 9.77E-04 and batch size 2097152 for 9536 iterations, using a total of 20.0B training tokens.

Usage Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "StepLaw/StepLaw-N_214M-D_19.0B-LR9.77E-04-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))