t5-base-news-describer

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Direct Use

from peft import PeftModel

model_id = 'google-t5/t5-base'

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    load_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

original_model = AutoModelForSeq2SeqLM.from_pretrained(model_id,quantization_config=bnb_config,device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token

peft_model = PeftModel.from_pretrained(original_model, "bhuvanmdev/t5-base-news-describer")

generation_config = peft_model.generation_config
generation_config.do_sample = True
generation_config.max_new_tokens = 100   # maxium no of token in output will get
generation_config.temperature = 0.1
generation_config.top_p = 0.8
generation_config.num_return_sequences = 1
generation_config.pad_token_id = tokenizer.eos_token_id
generation_config.eos_token_id = tokenizer.eos_token_id
generation_config.use_cache = True

prompt = f"""Title: A big accidient occurs in luxemberg.""".strip()
encoding = tokenizer(prompt, return_tensors="pt").to(device)

with torch.inference_mode():
    outputs = peft_model.generate(
        input_ids=encoding.input_ids,
        attention_mask=encoding.attention_mask,
        generation_config=generation_config,
    )
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

• PEFT 0.8.2