# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("nguyenvulebinh/deltalm-base", dtype="auto")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
from modeling_deltalm import DeltalmForConditionalGeneration # modeling_deltalm: https://huggingface.co/nguyenvulebinh/deltalm-base/blob/main/modeling_deltalm.py
from configuration_deltalm import DeltalmConfig # configuration_deltalm: https://huggingface.co/nguyenvulebinh/deltalm-base/blob/main/configuration_deltalm.py
from transformers AutoTokenizer
src_text = "i'm steve and<mask> 25 years old"
encoded_hi = tokenizer(src_text, return_tensors="pt")
generated_output = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.bos_token_id, max_length=20, num_beams=1, return_dict_in_generate=True, return_dict=True, output_hidden_states=True)
text_output = tokenizer.batch_decode(generated_output.sequences, skip_special_tokens=True)
print(text_output)
- Downloads last month
- 599
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nguyenvulebinh/deltalm-base")