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

	---
	# NeMo Megatron-GPT 5B

	<style>
	img {
	display: inline;
	}
	</style>

	\|[![Model architecture](https://img.shields.io/badge/Model%20Arch-Transformer%20Decoder-green)](#model-architecture)\|[![Model size](https://img.shields.io/badge/Params-5B-green)](#model-architecture)\|[![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets)


	## Model Description

	Megatron-GPT 5B is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 5B refers to the total trainable parameter count (5 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

	### 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 example below launches a model variant with Tensor Parallelism (TP) of 2 and Pipeline Parallelism (PP) of 1 on two GPUs.


	```
	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_tp2.nemo server=True tensor_model_parallel_size=2 trainer.devices=2
	```

	### Step 3: Send prompts to your 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 \|
	\| ------------- \| -------- \| ----------- \| --------- \| ---------- \| --- \| ------ \| --------- \| ---- \|
	\| 0.3976 \| 0.5566 \| 0.5007 \| 0.4171 \| 0.6133 \| 0.5812 \| 0.6356 \| 0.6298 \| 0.7492 \|

	## Limitations

	The model was trained on the data originally crawled from the Internet. This data contains toxic language and societal biases. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.

	## 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.