Using the model

Using `model.generate()`

To use the model, first call the checkpoints and initialize the model

python

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("smji/dialogpt2-instruct-following")
model = AutoModelForCausalLM.from_pretrained("smji/dialogpt2-instruct-following")

And then move onto generating the text

python

def generate_text(prompt):
    inputs = tokenizer.encode(prompt, return_tensors='pt').to(device)
    outputs = model.generate(inputs, max_length=512, pad_token_id=tokenizer.eos_token_id)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return generated_text[:generated_text.rfind('.')+1]

generate_text("How can I bake a cake?")

Using the pipeline

Or, you can also use the pipeline

python

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="smji/dialogpt2-instruct-following")

pipe("How can I bake a cake?", max_length=512)

Done by S M Jishanul Islam

smji/dialogpt2-instruct-following

DialoGPT2 Instruction Following

Using the model

Using model.generate()

Using the pipeline

smji/
dialogpt2-instruct-following

Using `model.generate()`