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# GPT Neo

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64, 
">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
</div>

## Overview

The GPTNeo model was released in the [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) repository by Sid
Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. It is a GPT2 like causal language model trained on the
[Pile](https://pile.eleuther.ai/) dataset.

The architecture is similar to GPT2 except that GPT Neo uses local attention in every other layer with a window size of
256 tokens.

This model was contributed by [valhalla](https://huggingface.co/valhalla).

## Usage example

The `generate()` method can be used to generate text using GPT Neo model.

```python
>>> from transformers import GPTNeoForCausalLM, GPT2Tokenizer

>>> model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
>>> tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")

>>> prompt = (
...     "In a shocking finding, scientists discovered a herd of unicorns living in a remote, "
...     "previously unexplored valley, in the Andes Mountains. Even more surprising to the "
...     "researchers was the fact that the unicorns spoke perfect English."
... )

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

>>> gen_tokens = model.generate(
...     input_ids,
...     do_sample=True,
...     temperature=0.9,
...     max_length=100,
... )
>>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
```

## Combining GPT-Neo and Flash Attention 2

First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature, and make sure your hardware is compatible with Flash-Attention 2. More details are available [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#flashattention-2) concerning the installation.

Make sure as well to load your model in half-precision (e.g. `torch.float16`).

To load and run a model using Flash Attention 2, refer to the snippet below:

```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto

>>> model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")

>>> prompt = "def hello_world():"

>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)

>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"def hello_world():\n    >>> run_script("hello.py")\n    >>> exit(0)\n<|endoftext|>"
```

### Expected speedups

Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using `EleutherAI/gpt-neo-2.7B` checkpoint and the Flash Attention 2 version of the model.
Note that for GPT-Neo it is not possible to train / run on very long context as the max [position embeddings](https://huggingface.co/EleutherAI/gpt-neo-2.7B/blob/main/config.json#L58 ) is limited to 2048 - but this is applicable to all gpt-neo models and not specific to FA-2

<div style="text-align: center">
<img src="https://user-images.githubusercontent.com/49240599/272241893-b1c66b75-3a48-4265-bc47-688448568b3d.png">
</div>


## Resources

- [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling)

## GPTNeoConfig

[[autodoc]] GPTNeoConfig


<frameworkcontent>
<pt>

## GPTNeoModel

[[autodoc]] GPTNeoModel
    - forward

## GPTNeoForCausalLM

[[autodoc]] GPTNeoForCausalLM
    - forward

## GPTNeoForQuestionAnswering

[[autodoc]] GPTNeoForQuestionAnswering
    - forward

## GPTNeoForSequenceClassification

[[autodoc]] GPTNeoForSequenceClassification
    - forward

## GPTNeoForTokenClassification

[[autodoc]] GPTNeoForTokenClassification
    - forward

</pt>
<jax>

## FlaxGPTNeoModel

[[autodoc]] FlaxGPTNeoModel
    - __call__

## FlaxGPTNeoForCausalLM

[[autodoc]] FlaxGPTNeoForCausalLM
    - __call__

</jax>
</frameworkcontent>


