# GPT-Neo 2.7B (By EleutherAI)
## Model Description
GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model.
## Training procedure
This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.
## Intended Use and Limitations
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
>>> generator("EleutherAI has", do_sample=True, min_length=50)
[{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}]
```
### Limitations and Biases
GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. 
## Eval results
All evaluations were done using our [evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness). Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our [Discord](https://discord.gg/vtRgjbM).
### Linguistic Reasoning
| Model and Size   | Pile BPB   | Pile PPL   | Wikitext PPL  | Lambada PPL | Lambada Acc | Winogrande | Hellaswag   |
| ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- |
| GPT-Neo 1.3B     | 0.7527     | 6.159      | 13.10         |  7.498      | 57.23%      | 55.01%     | 38.66%  |
| GPT-2 1.5B       | 1.0468     | -----      | 17.48         |  10.634     | 51.21%      | 59.40%     | 40.03%      |
| **GPT-Neo 2.7B** | **0.7165** | **5.646**  | **11.39**     |  **5.626**  | **62.22%**  | **56.50%** | **42.73%**  |
| GPT-3 Ada        | 0.9631     | -----      | -----         |  9.954      | 51.60%      | 52.90%     | 35.93%      |
### Physical and Scientific Reasoning
| Model and Size   | MathQA     | PubMedQA   | Piqa        |
| ---------------- | ---------- | ---------- | ----------- |
| GPT-Neo 1.3B     | 24.05%     | 54.40%     | 71.11%  |
| GPT-2 1.5B       | 23.64%     | 58.33%     | 70.78%      |
| **GPT-Neo 2.7B** | **24.72%** | **57.54%** | **72.14%**  |
| GPT-3 Ada        | 24.29%     | 52.80%     | 68.88%      |
### Down-Stream Applications
TBD
### BibTeX entry and citation info
To cite this model, use
```bibtex
@article{gao2020pile,
  title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
  author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others},
  journal={arXiv preprint arXiv:2101.00027},
  year={2020}
}
```
To cite the codebase that this model was trained with, use
```bibtex
@software{gpt-neo,
  author = {Black, Sid and Gao, Leo and Wang, Phil and Leahy, Connor and Biderman, Stella},
  title = {{GPT-Neo}: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow},
  url = {http://github.com/eleutherai/gpt-neo},
  version = {1.0},
  year = {2021},
}
```
{"language": ["en"], "license": "apache-2.0", "tags": ["text generation", "pytorch", "the Pile", "causal-lm"], "datasets": ["the Pile"]}
transformers pytorch gpt_neo text-generation text generation the Pile causal-lm en license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #gpt_neo #text-generation #text generation #the Pile #causal-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

GPT-Neo 2.7B (By EleutherAI)
============================


Model Description
-----------------


GPT-Neo 2.7B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 2.7B represents the number of parameters of this particular pre-trained model.


Training data
-------------


GPT-Neo 2.7B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model.


Training procedure
------------------


This model was trained for 420 billion tokens over 400,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.


Intended Use and Limitations
----------------------------


This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt.


### How to use


You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:


### Limitations and Biases


GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.


Eval results
------------


All evaluations were done using our evaluation harness. Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our Discord.


### Linguistic Reasoning



### Physical and Scientific Reasoning



### Down-Stream Applications


TBD


### BibTeX entry and citation info


To cite this model, use


To cite the codebase that this model was trained with, use



### How to use


You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ### Limitations and Biases


GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.


Eval results
------------


All evaluations were done using our evaluation harness. Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our Discord. ### Linguistic Reasoning ### Physical and Scientific Reasoning ### Down-Stream Applications


TBD ### BibTeX entry and citation info


To cite this model, use


To cite the codebase that this model was trained with, use
TAGS
#transformers #pytorch #gpt_neo #text-generation #text generation #the Pile #causal-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### How to use


You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ### Limitations and Biases


GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.


Eval results
------------


All evaluations were done using our evaluation harness. Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our Discord. ### Linguistic Reasoning ### Physical and Scientific Reasoning ### Down-Stream Applications


TBD ### BibTeX entry and citation info


To cite this model, use


To cite the codebase that this model was trained with, use
TAGS
#transformers #pytorch #gpt_neo #text-generation #text generation #the Pile #causal-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### How to use


You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:### Limitations and Biases


GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.


Eval results
------------


All evaluations were done using our evaluation harness. Some results for GPT-2 and GPT-3 are inconsistent with the values reported in the respective papers. We are currently looking into why, and would greatly appreciate feedback and further testing of our eval harness. If you would like to contribute evaluations you have done, please reach out on our Discord.### Linguistic Reasoning### Physical and Scientific Reasoning### Down-Stream Applications


TBD### BibTeX entry and citation info


To cite this model, use


To cite the codebase that this model was trained with, use