README.md

metadata

license: apache-2.0
tags:
  - StepLaw
  - causal-lm
language:
  - en
library_name: transformers
pipeline_tag: text-generation
model-index:
  - name: step2v2_0618_h576_ffnh5016_numh9_numl6_lr2.762e-03_bs32_ti122070_mlr1e-5
    results: []

Wandb Model Name: step2v2_0618_h576_ffnh5016_numh9_numl6_lr2.762e-03_bs32_ti122070_mlr1e-5

This model is part of the StepLaw-N_59M-D_7.0B collection.

Model Specifications

Architecture

• Hidden size (H): 576
• Feed-forward network size (FFN): 5016
• Attention heads: 9
• Layers: 6
• Parameter count: 59M

Training Parameters

• Learning rate (lr): 2.762e-03
• Batch size (bs): 65536
• Training iterations: 122070
• 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 2.762e-03 and batch size 65536 for 122070 iterations, using a total of 8.0B training tokens.

Usage Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "StepLaw/StepLaw-N_59M-D_7.0B-LR2.762e-03-BS65536"
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))