enginex-mlu370-any2any/transformers/docs/source/en/model_doc/gpt_neo.md

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*This model was released on 2021-03-21 and added to Hugging Face Transformers on 2021-03-30.*

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## GPT-Neo

[GPT-Neo](https://zenodo.org/records/5297715) is an open-source alternative to GPT-2 and GPT-3 models, built with Mesh TensorFlow for TPUs. GPT-Neo uses local attention in every other layer for more efficiency. It is trained on the [Pile](https://huggingface.co/datasets/EleutherAI/pile), a diverse dataset consisting of 22 smaller high-quality datasets. The original github repository can be found [here](https://github.com/EleutherAI/gpt-neo/tree/v1.1)

You can find all the original GPT-Neo checkpoints under the [EleutherAI](https://huggingface.co/EleutherAI?search_models=gpt-neo) organization.

> [!TIP]
> Click on the GPT-Neo models in the right sidebar for more examples of how to apply GPT Neo to different language tasks.

The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`], and from the command line.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="EleutherAI/gpt-neo-1.3B", dtype=torch.float16, device=0)
pipeline("Hello, I'm a language model")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B", dtype=torch.float16, device_map="auto", attn_implementation="flash_attention_2")
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")

input_ids = tokenizer("Hello, I'm a language model", return_tensors="pt").to(model.device)

output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
<hfoption id="transformers CLI">

```bash
echo -e "Hello, I'm a language model" | transformers run --task text-generation --model EleutherAI/gpt-neo-1.3B --device 0
```

</hfoption>
</hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bits.

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True
)

model = AutoModelForCausalLM.from_pretrained(
    "EleutherAI/gpt-neo-2.7B",
    quantization_config=quantization_config,
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
inputs = tokenizer("Hello, I'm a language model", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Notes

- Pad inputs on the right because GPT-Neo uses absolute position embeddings.

## GPTNeoConfig

[[autodoc]] GPTNeoConfig

## GPTNeoModel

[[autodoc]] GPTNeoModel
    - forward

## GPTNeoForCausalLM

[[autodoc]] GPTNeoForCausalLM
    - forward

## GPTNeoForQuestionAnswering

[[autodoc]] GPTNeoForQuestionAnswering
    - forward

## GPTNeoForSequenceClassification

[[autodoc]] GPTNeoForSequenceClassification
    - forward

## GPTNeoForTokenClassification

[[autodoc]] GPTNeoForTokenClassification
    - forward