transformers/docs/source/en/model_doc/gpt_neo.md

GPT-Neo

GPT-Neo 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, a diverse dataset consisting of 22 smaller high-quality datasets.

You can find all the original GPT-Neo checkpoints under the EleutherAI 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.

import torch
from transformers import pipeline

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

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B", torch_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("cuda")

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

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

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

The example below uses bitsandbytes to only quantize the weights to 4-bits.

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("cuda")
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

FlaxGPTNeoModel

autodoc FlaxGPTNeoModel - call

FlaxGPTNeoForCausalLM

autodoc FlaxGPTNeoForCausalLM - call