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import gradio as gr |
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import torch |
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import spaces |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("ping98k/typhoon-7b-rag-instruct-th") |
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model = AutoModelForCausalLM.from_pretrained("ping98k/typhoon-7b-rag-instruct-th") |
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@spaces.GPU(duration=120) |
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def response(instruction, history, inputText): |
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inp = f"""### Instruction: |
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{instruction} |
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### Input: |
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=======START OF DOCUMENT======= |
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{inputText} |
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=======END OF DOCUMENT======= |
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### Response:""" |
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input_ids = tokenizer(inp, return_tensors='pt').to("cuda") |
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beam_output = model.generate(**input_ids) |
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outputText = tokenizer.decode(beam_output[0], skip_special_token=True) |
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return outputText |
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gr.ChatInterface( |
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response, |
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additional_inputs=[ |
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gr.Textbox("You are helpful AI.", label="Input Text"), |
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], |
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).launch() |
