Module gpt_neo

GPT-Neo

Implementation of the GPT-Neo language model (The Pile: An 800GB Dataset of Diverse Text for Language Modeling 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, 2020). The base model is implemented in the gpt_neo_model::GptNeoModel struct. A causal language modeling head is implemented in gpt_neo_model::GptNeoForCausalLM

Model set-up and pre-trained weights loading

A full working example is provided in examples/generation_gpt_neo, run with cargo run --example generation_gpt_neo. All models expect the following resources:

• Configuration file expected to have a structure following the Transformers library
• Model weights are expected to have a structure and parameter names following the Transformers library. A conversion using the Python utility scripts is required to convert the .bin weights to the .ot format.
• GPT2Tokenizer using a vocab.json vocabulary and a merges.txt merges file

The following pre-trained checkpoints are readily available:

• 125M parameters model (GptNeoModelResources::GPT_NEO_125M)
• 1.3B parameters model (GptNeoModelResources::GPT_NEO_1_3B)
• 2.7B parameters model (GptNeoModelResources::GPT_NEO_2_7B)

use rust_bert::gpt_neo::{
    GptNeoConfigResources, GptNeoMergesResources, GptNeoModelResources, GptNeoVocabResources,
};
use rust_bert::pipelines::common::ModelType;
use rust_bert::pipelines::text_generation::{TextGenerationConfig, TextGenerationModel};
use rust_bert::resources::RemoteResource;
use tch::Device;

fn main() -> anyhow::Result<()> {
    use rust_bert::pipelines::common::ModelResource;
    let config_resource = Box::new(RemoteResource::from_pretrained(
        GptNeoConfigResources::GPT_NEO_1_3B,
    ));
    let vocab_resource = Box::new(RemoteResource::from_pretrained(
        GptNeoVocabResources::GPT_NEO_1_3B,
    ));
    let merges_resource = Box::new(RemoteResource::from_pretrained(
        GptNeoMergesResources::GPT_NEO_1_3B,
    ));
    let model_resource = Box::new(RemoteResource::from_pretrained(
        GptNeoModelResources::GPT_NEO_1_3B,
    ));

    let text_generation_config = TextGenerationConfig {
        model_type: ModelType::GPTNeo,
        model_resource: ModelResource::Torch(model_resource),
        config_resource,
        vocab_resource,
        merges_resource: Some(merges_resource),
        num_beams: 4,
        no_repeat_ngram_size: 3,
        device: Device::cuda_if_available(),
        ..Default::default()
    };
    let model = TextGenerationModel::new(text_generation_config)?;

    let input_context_1 = "It was a very nice and sunny";
    let input_context_2 = "It was a gloom winter night, and";
    let output = model.generate(&[input_context_1, input_context_2], None)?;

    for sentence in output {
        println!("{}", sentence);
    }

    Ok(())
}

Structs

GptNeoConfig

GPT-Neo model configuration

GptNeoConfigResources

GPT-Neo Pretrained model config files

GptNeoForCausalLM

GPT-Neo Model for causal language modeling

GptNeoGenerator

Language generation model based on the GPT-Neo architecture

GptNeoMergesResources

GPT-Neo Pretrained model merges files

GptNeoModel

GPT-Neo Base model

GptNeoModelResources

GPT-Neo Pretrained model weight files

GptNeoVocabResources

GPT-Neo Pretrained model vocab files

LayerState

Cache for GPT-Neo attention layers