06f1fd69a6
4.1 KiB
This model was contributed to Hugging Face Transformers on 2021-03-30.
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. The original github repository can be found here
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.
from transformers import pipeline
pipeline = pipeline(task="text-generation", model="EleutherAI/gpt-neo-1.3B", device=0)
pipeline("Hello, I'm a language model")
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B", 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))
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.
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