StepLaw-N_429M-D_7.0B-LR4.883e-04-BS720896

Wandb Model Name: step2v2_0618_h1280_ffnh9472_numh10_numl10_lr4.883e-04_bs352_ti11097_mlr1.00e-05

This model is part of the StepLaw-N_429M-D_7.0B collection.

Model Specifications

Architecture

• Hidden size (H): 1280
• Feed-forward network size (FFN): 9472
• Attention heads: 10
• Layers: 10
• Parameter count: 429M

Training Parameters

• Learning rate (lr): 4.883e-04
• Batch size (bs): 720896
• Training iterations: 11097
• Training tokens (D): 8.0B

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 4.883e-04 and batch size 720896 for 11097 iterations, using a total of 8.0B training tokens.

Usage Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "StepLaw/StepLaw-N_429M-D_7.0B-LR4.883e-04-BS720896"
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))