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```python
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
dataset = load_dataset("CarperAI/openai_summarize_tldr")
val_prompts = [sample["prompt"] for sample in dataset["valid"]]
kwargs = {
"max_new_tokens": 50,
"do_sample": True,
"top_k": 0,
"top_p": 0.95,
"temperature": 0.5
}
model = AutoModelForCausalLM.from_pretrained("pvduy/ppo_pythia6B_sample")
model.eval()
tokenizer = AutoTokenizer.from_pretrained("pvduy/ppo_pythia6B_sample")
tokenizer.pad_token_id = tokenizer.eos_token_id
count = 0
for prompt in val_prompts:
output_tk = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(output_tk.input_ids, attention_mask=output_tk.attention_mask, **kwargs)
print("Prompt:", prompt)
print("Output:", tokenizer.decode(outputs[0], skip_special_tokens=True).split("TL;DR:")[1].strip())
print("=================================")
count += 1
if count == 10:
break
``` |