File size: 4,246 Bytes
073bf85 70077f0 073bf85 70077f0 073bf85 70077f0 26104d5 70077f0 1cbbf0b 70077f0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
---
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)|[](#model-architecture)|[](#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.
|