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 1.95E-03 and batch size 64 for 152587 iterations, using a total of 20.0B training tokens.

Usage Example

Learning rate (lr): 1.95E-03
Batch size (bs): 64
Training iterations: 152587
Training tokens (D): 20.0B

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "StepLaw/StepLaw-N_214M-D_19.0B-LR1.95E-03-BS131072"
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))
```## Part of StepLaw Project

StepLaw is an initiative to provide thousands of models for optimal hyperparameter research.
Visit [StepLaw Project](https://step-law.github.io/) for more information.