metadata
license: apache-2.0
tags:
- StepLaw
- causal-lm
language:
- en
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: >-
step2v2_0618_h1280_ffnh9048_numh10_numl13_lr1.953e-03_bs256_ti19073_mlr1.00e-05
results: []
Wandb Model Name: step2v2_0618_h1280_ffnh9048_numh10_numl13_lr1.953e-03_bs256_ti19073_mlr1.00e-05
This model is part of the StepLaw-N_536M-D_9.0B collection.
Model Specifications
Architecture
- Hidden size (H): 1280
- Feed-forward network size (FFN): 9048
- Attention heads: 10
- Layers: 13
- Parameter count: 536M
Training Parameters
- Learning rate (lr): 1.953e-03
- Batch size (bs): 524288
- Training iterations: 19073
- Training tokens (D): 10.0B
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 1.953e-03 and batch size 524288 for 19073 iterations, using a total of 10.0B training tokens.
Usage Example
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "StepLaw/StepLaw-N_536M-D_9.0B-LR1.953e-03-BS524288"
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))