---
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_lr9.766e-04_bs32_ti61035_mlr1e-5
  results: []
---

# Wandb Model Name: step2v2_0618_h1280_ffnh9048_numh10_numl13_lr9.766e-04_bs32_ti61035_mlr1e-5

This model is part of the [StepLaw-N_536M-D_3.0B](https://huggingface.co/collections/StepLaw/StepLaw-N_536M-D_3.0B) 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)**: 9.766e-04
- **Batch size (bs)**: 65536
- **Training iterations**: 61035
- **Training tokens (D)**: 4.0B

## 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 9.766e-04 and batch size 65536 for 61035 iterations, using a total of 4.0B training tokens.

## Usage Example

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "StepLaw/StepLaw-N_536M-D_3.0B-LR9.766e-04-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))
```