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import gradio as gr |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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def runLLM (): |
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model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-small", device_map="auto", torch_dtype=torch.float16) |
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tokenizer = AutoTokenizer.from_pretrained("cyberagent/open-calm-small") |
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inputs = tokenizer("AIによって私達の暮らしは、", return_tensors="pt").to(model.device) |
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with torch.no_grad(): |
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tokens = model.generate( |
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**inputs, |
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max_new_tokens=64, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.9, |
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repetition_penalty=1.05, |
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pad_token_id=tokenizer.pad_token_id, |
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) |
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output = tokenizer.decode(tokens[0], skip_special_tokens=True) |
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return output |
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def display_message(): |
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msg = runLLM() |
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return msg |
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iface = gr.Interface(fn=display_message, inputs=None, outputs="text") |
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iface.launch() |
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