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 1.953e-03 and batch size 32768 for 61035 iterations, using a total of 2.0B training tokens.

Usage Example

Learning rate (lr): 1.953e-03
Batch size (bs): 32768
Training iterations: 61035
Training tokens (D): 2.0B

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "StepLaw/StepLaw-N_1.0B-D_1.0B-LR1.953e-03-BS32768"
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))