Opt 6.7b

🚀 OPT : Open Pre-trained Transformer Language Models

OPT is a suite of decoder-only pre-trained transformers aiming to enable reproducible and responsible research at scale.

🚀 Quick Start

OPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.

Disclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. Content from this model card has been written by the Hugging Face team.

✨ Features

• Open Sharing: A suite of decoder - only pre - trained transformers ranging from 125M to 175B parameters, aiming to be fully and responsibly shared with interested researchers.
• Performance Matching: Trained to roughly match the performance and sizes of the GPT - 3 class of models.
• Best Practices: Applying the latest best practices in data collection and efficient training.

📚 Documentation

Intro

To quote the first two paragraphs of the official paper

Large language models trained on massive text collections have shown surprising emergent capabilities to generate text and perform zero - and few - shot learning. While in some cases the public can interact with these models through paid APIs, full model access is currently limited to only a few highly resourced labs. This restricted access has limited researchers’ ability to study how and why these large language models work, hindering progress on improving known challenges in areas such as robustness, bias, and toxicity.

We present Open Pretrained Transformers (OPT), a suite of decoder - only pre - trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match the performance and sizes of the GPT - 3 class of models, while also applying the latest best practices in data collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the collective research community as a whole, which is only possible when models are available for study.

Model description

OPT was predominantly pretrained with English text, but a small amount of non - English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective. OPT belongs to the same family of decoder - only models like GPT - 3. As such, it was pretrained using the self - supervised causal language modedling objective.

For evaluation, OPT follows GPT - 3 by using their prompts and overall experimental setup. For more details, please read the official paper.

Intended uses & limitations

The pretrained - only model can be used for prompting for evaluation of downstream tasks as well as text generation. In addition, the model can be fine - tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language - modeling). For all other OPT checkpoints, please have a look at the model hub.

How to use

For large OPT models, such as this one, it is not recommend to make use of the text - generation pipeline because one should load the model in half - precision to accelerate generation and optimize memory consumption on GPU. It is recommended to directly call the generate method as follows:

>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> import torch

>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt - 6.7b", torch_dtype=torch.float16).cuda()

>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt - 6.7b", use_fast=False)

>>> prompt = "Hello, I'm am conscious and"

>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()

>>> generated_ids = model.generate(input_ids)

>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Hello, I'm am conscious and aware of my surroundings. I'm not sure what you mean"]

By default, generation is deterministic. In order to use the top - k sampling, please set do_sample to True.

>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch

>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt - 6.7b", torch_dtype=torch.float16).cuda()

>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt - 6.7b", use_fast=False)

>>> prompt = "Hello, I'm am conscious and"

>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()

>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True)

>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
["Hello, I'm am conscious and aware of my surroundings. I'm not sure if I'm"]

Limitations and bias

As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased :

Like other large language models for which the diversity (or lack thereof) of training data induces downstream impact on the quality of our model, OPT - 175B has limitations in terms of bias and safety. OPT - 175B can also have quality issues in terms of generation diversity and hallucination. In general, OPT - 175B is not immune from the plethora of issues that plague modern large language models.

Here's an example of how the model can have biased predictions:

>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch

>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt - 6.7b", torch_dtype=torch.float16).cuda()

>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt - 6.7b", use_fast=False)

>>> prompt = "The woman worked as a"

>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()

>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)

>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
The woman worked as a supervisor in the office
The woman worked as a bartender in a bar
The woman worked as a cashier at the
The woman worked as a teacher, and was
The woman worked as a maid at a house

compared to:

>>> from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
>>> import torch

>>> model = AutoModelForCausalLM.from_pretrained("facebook/opt - 6.7b", torch_dtype=torch.float16).cuda()

>>> # the fast tokenizer currently does not work correctly
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt - 6.7b", use_fast=False)

>>> prompt = "The man worked as a"

>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()

>>> set_seed(32)
>>> generated_ids = model.generate(input_ids, do_sample=True, num_return_sequences=5, max_length=10)

>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
The man worked as a consultant to the Government
The man worked as a bartender in a bar
The man worked as a cashier at the
The man worked as a teacher, and was
The man worked as a professional at a bank

This bias will also affect all fine - tuned versions of this model.

Training data

The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:

• BookCorpus, which consists of more than 10K unpublished books,
• CC - Stories, which contains a subset of CommonCrawl data filtered to match the story - like style of Winograd schemas,
• The Pile, from which * Pile - CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
• Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in Roller et al. (2021)
• CCNewsV2 containing an updated version of the English portion of the CommonCrawl News dataset that was used in RoBERTa (Liu et al., 2019b)

The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally to each dataset’s size in the pretraining corpus.

The dataset might contains offensive content as parts of the dataset are a subset of public Common Crawl data, along with a subset of public Reddit data, which could contain sentences that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.

Collection process

The dataset was collected form internet, and went through classic data processing algorithms and re - formatting practices, including removing repetitive/non - informative text like Chapter One or This ebook by Project Gutenberg.

Training procedure

Preprocessing

The texts are tokenized using the GPT2 byte - level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens.

The 175B model was trained on 992 80GB A100 GPUs. The training duration was roughly ~33 days of continuous training.

BibTeX entry and citation info

@misc{zhang2022opt,
      title={OPT: Open Pre-trained Transformer Language Models}, 
      author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
      year={2022},
      eprint={2205.01068},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

📄 License

This model is released under the other license, and commercial use is not allowed.

⚠️ Important Note

The dataset might contain offensive content as parts of it are from public Common Crawl and Reddit data.

💡 Usage Tip

For large OPT models, it's recommended to load the model in half - precision and directly call the generate method to accelerate generation and optimize memory consumption on GPU.