---
language:
- en
library_name: nemo
datasets:
- the_pile
tags:
- text generation
- pytorch
- causal-lm
license: cc-by-4.0

---

<style>
img {
 display: inline;
}
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| [![Model architecture](https://img.shields.io/badge/Model%20Arch-Transformer%20Decoder-green)](#model-architecture)
| [![Model size](https://img.shields.io/badge/Params-1.3B-green)](#model-architecture)
| [![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets)


# Megatron-GPT 1.3B

## Model Description

Megatron-GPT 1.3B is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 1.3B refers to the total trainable parameter count (1.3 Billion) [1, 2].

This model was trained with [NeMo Megatron](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html).

## Getting started

You will need to install NVIDIA Apex and NeMo. 

```
git clone https://github.com/ericharper/apex.git
cd apex
git checkout nm_v1.11.0
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./
```

```
pip install nemo_toolkit['nlp']==1.11.0
``` 

Alternatively, you can use NeMo Megatron training docker container with all dependencies pre-installed.

## Training Data

The model was trained on ["The Piles" dataset prepared by Eleuther.AI](https://pile.eleuther.ai/).

## Evaluation results

*Zero-shot performance.*

| ARC-Challenge	| ARC-Easy | RACE-middle | RACE-high | Winogrande | RTE | BoolQA | HellaSwag | PiQA |
| ------------- | -------- | ----------- | --------- | ---------- | --- | ------ | --------- | ---- |
| 0.3012        | 0.4596  | 0.459       | 0.3811    | 0.5343     | 0.5451 | 0.5979 | 0.4442 | 0.6834 | 



## References

[1] [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)

[2] [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf)

[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)

## Licence

License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.