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//! # GPT (Radford et al.)

//!

//! Implementation of the GPT2 language model ([Improving Language Understanding by Generative Pre-Training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) Radford, Narasimhan, Salimans, Sutskever 2018).

//! The base model is implemented in the `openai_gpt::OpenAiGptModel` struct. The model also includes a language model head: `openai_gpt::OpenAIGPTLMHeadModel`

//! implementing the common `generation::LMHeadModel` trait shared between the models used for generation (see `pipelines` for more information).

//!

//! # Model set-up and pre-trained weights loading

//!

//! A full working example is provided in `examples/openai_gpt`, run with `cargo run --example openai_gpt`.

//! All models expect the following resources:

//! - Configuration file expected to have a structure following the [Transformers library](https://github.com/huggingface/transformers)

//! - Model weights are expected to have a structure and parameter names following the [Transformers library](https://github.com/huggingface/transformers). A conversion using the Python utility scripts is required to convert the `.bin` weights to the `.ot` format.

//! - `GptTokenizer` using a `vocab.txt` vocabulary and `merges.txt` 2-gram merges

//! Pretrained models are available and can be downloaded using RemoteResources.

//!

//! ```no_run

//! # fn main() -> anyhow::Result<()> {

//! use rust_tokenizers::OpenAiGptTokenizer;

//! use tch::{nn, Device};

//! # use std::path::PathBuf;

//! use rust_bert::gpt2::Gpt2Config;

//! use rust_bert::openai_gpt::OpenAiGptModel;

//! use rust_bert::resources::{LocalResource, Resource};

//! use rust_bert::Config;

//!

//! let config_resource = Resource::Local(LocalResource {

//!     local_path: PathBuf::from("path/to/config.json"),

//! });

//! let vocab_resource = Resource::Local(LocalResource {

//!     local_path: PathBuf::from("path/to/vocab.txt"),

//! });

//! let merges_resource = Resource::Local(LocalResource {

//!     local_path: PathBuf::from("path/to/vocab.txt"),

//! });

//! let weights_resource = Resource::Local(LocalResource {

//!     local_path: PathBuf::from("path/to/model.ot"),

//! });

//! let config_path = config_resource.get_local_path()?;

//! let vocab_path = vocab_resource.get_local_path()?;

//! let merges_path = merges_resource.get_local_path()?;

//! let weights_path = weights_resource.get_local_path()?;

//!

//! let device = Device::cuda_if_available();

//! let mut vs = nn::VarStore::new(device);

//! let tokenizer: OpenAiGptTokenizer = OpenAiGptTokenizer::from_file(

//!     vocab_path.to_str().unwrap(),

//!     merges_path.to_str().unwrap(),

//!     true,

//! )?;

//! let config = Gpt2Config::from_file(config_path);

//! let gpt_model = OpenAiGptModel::new(&vs.root(), &config);

//! vs.load(weights_path)?;

//!

//! # Ok(())

//! # }

//! ```


mod openai_gpt_model;
mod transformer;

pub use openai_gpt_model::{
    OpenAIGPTLMHeadModel, OpenAiGptConfigResources, OpenAiGptMergesResources, OpenAiGptModel,
    OpenAiGptModelOutput, OpenAiGptModelResources, OpenAiGptVocabResources,
};