---
license: apache-2.0
tags:
- StepLaw
- causal-lm
language:
- en
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: step2v2_0618_h960_ffnh9368_numh15_numl7_lr2.21E-02_bs24_ti406901_mlr1.00E-05
  results: []
---

# Wandb Model Name: step2v2_0618_h960_ffnh9368_numh15_numl7_lr2.21E-02_bs24_ti406901_mlr1.00E-05

This model is part of the [StepLaw-N_214M-D_19.0B](https://huggingface.co/collections/StepLaw/StepLaw-N_214M-D_19.0B) collection.

## Model Specifications

### Architecture
- **Hidden size (H)**: 960
- **Feed-forward network size (FFN)**: 9368
- **Attention heads**: 15
- **Layers**: 7
- **Parameter count**: 214M

### Training Parameters
- **Learning rate (lr)**: 2.21E-02
- **Batch size (bs)**: 49152
- **Training iterations**: 406901
- **Training tokens (D)**: 20.0B

## 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 2.21E-02 and batch size 49152 for 406901 iterations, using a total of 20.0B training tokens.

## Usage Example

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "StepLaw/StepLaw-N_214M-D_19.0B-LR2.21E-02-BS49152"
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))
```