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Direct Use
1from peft import PeftModel
2
3model_id = 'google-t5/t5-base'
4
5bnb_config = BitsAndBytesConfig(
6 load_in_4bit=True,
7 load_4bit_use_double_quant=True,
8 bnb_4bit_quant_type="nf4",
9 bnb_4bit_compute_dtype=torch.bfloat16,
10)
11
12original_model = AutoModelForSeq2SeqLM.from_pretrained(model_id,quantization_config=bnb_config,device_map='auto')
13tokenizer = AutoTokenizer.from_pretrained(model_id)
14tokenizer.pad_token = tokenizer.eos_token
15
16peft_model = PeftModel.from_pretrained(original_model, "bhuvanmdev/t5-base-news-describer")
17
18generation_config = peft_model.generation_config
19generation_config.do_sample = True
20generation_config.max_new_tokens = 100 # maxium no of token in output will get
21generation_config.temperature = 0.1
22generation_config.top_p = 0.8
23generation_config.num_return_sequences = 1
24generation_config.pad_token_id = tokenizer.eos_token_id
25generation_config.eos_token_id = tokenizer.eos_token_id
26generation_config.use_cache = True
27
28prompt = f"""Title: A big accidient occurs in luxemberg.""".strip()
29encoding = tokenizer(prompt, return_tensors="pt").to(device)
30
31with torch.inference_mode():
32 outputs = peft_model.generate(
33 input_ids=encoding.input_ids,
34 attention_mask=encoding.attention_mask,
35 generation_config=generation_config,
36 )
37print(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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