README.md

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

---
# Megatron-GPT 1.3B

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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)


## 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]. It has Tensor Parallelism (TP) of 1, Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU.

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

### Step 1: Install NeMo and dependencies

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.

### Step 2: Launch eval server 

**Note.** The model has been trained with Tensor Parallelism (TP) of 1 and Pipeline Parallelism (PP) of 1 and should fit on a single NVIDIA GPU.

```
git clone https://github.com/NVIDIA/NeMo.git 
cd NeMo/examples/nlp/language_modeling
git checkout v1.11.0
python megatron_gpt_eval.py gpt_model_file=nemo_gpt5B_fp16.nemo server=True tensor_model_parallel_size=1 trainer.devices=1
```

### Step 3: Send prompts to you model!
```python
import json
import requests

port_num = 5555
headers = {"Content-Type": "application/json"}

def request_data(data):
    resp = requests.put('http://localhost:{}/generate'.format(port_num),
                        data=json.dumps(data),
                        headers=headers)
    sentences = resp.json()['sentences']
    return sentences


data = {
    "sentences": ["Tell me an interesting fact about space travel."]*1,
    "tokens_to_generate": 50,
    "temperature": 1.0,
    "add_BOS": True,
    "top_k": 0,
    "top_p": 0.9,
    "greedy": False,
    "all_probs": False,
    "repetition_penalty": 1.2,
    "min_tokens_to_generate": 2,
}

sentences = request_data(data)
print(sentences)
```


## Training Data

The model was trained on ["The Piles" dataset prepared by Eleuther.AI](https://pile.eleuther.ai/). [4]

## Evaluation results

*Zero-shot performance.* Evaluated using [LM Evaluation Test Suite from AI21](https://github.com/AI21Labs/lm-evaluation)

| ARC-Challenge	| ARC-Easy | RACE-middle | RACE-high | Winogrande | RTE | BoolQA | HellaSwag | PiQA |
| ------------- | -------- | ----------- | --------- | ---------- | --- | ------ | --------- | ---- |
| 0.3012        | 0.4596  | 0.459       | 0.3797    | 0.5343     | 0.5451 | 0.5979 | 0.4443 | 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)

[4] [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027)

## 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.