Update model.py
Browse files
model.py
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from threading import Thread
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from typing import Iterator
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import torch
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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model="/kaggle/input/deberta-v3-large-hf-weights"
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if torch.cuda.is_available():
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config = AutoConfig.from_pretrained(model_id)
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config.pretraining_tp = 1
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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config=config,
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torch_dtype=torch.float16,
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load_in_4bit=True,
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device_map='auto'
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)
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else:
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model = None
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def preprocess(sample):
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first_sentences = [sample["prompt"]] * 5
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second_sentences = [sample[option] for option in "ABCDE"]
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tokenized_sentences = tokenizer(first_sentences, second_sentences, truncation=True, padding=True, return_tensors="pt")
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sample["input_ids"] = tokenized_sentences["input_ids"]
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sample["attention_mask"] = tokenized_sentences["attention_mask"]
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return sample
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def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int:
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prompt = get_prompt(message, chat_history, system_prompt)
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input_ids = tokenizer([prompt], return_tensors='np', add_special_tokens=False)['input_ids']
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return input_ids.shape[-1]
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def run(message: str,
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chat_history: list[tuple[str, str]],
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system_prompt: str,
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max_new_tokens: int = 1024,
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temperature: float = 0.8,
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top_p: float = 0.95,
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top_k: int = 50) -> Iterator[str]:
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prompt = get_prompt(message, chat_history, system_prompt)
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inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
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streamer = TextIteratorStreamer(tokenizer,
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timeout=10.,
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skip_prompt=True,
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skip_special_tokens=True)
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generate_kwargs = dict(
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inputs,
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streamer=streamer,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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top_p=top_p,
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top_k=top_k,
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temperature=temperature,
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num_beams=1,
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)
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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t.start()
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outputs = []
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for text in streamer:
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outputs.append(text)
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yield ''.join(outputs)
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