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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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import torch |
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from peft import LoraConfig, PeftModel |
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base_model_name = "microsoft/phi-2" |
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new_model = "./checkpoint_360" |
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model = AutoModelForCausalLM.from_pretrained( "microsoft/phi-2", trust_remote_code=True) |
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model.config.use_cache = False |
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model.load_adapter(new_model) |
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tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True) |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = "right" |
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def QLoRA_Chatgpt(prompt): |
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print(prompt) |
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pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200) |
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result = pipe(f"<s>[INST] {prompt} [/INST]") |
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return(result[0]['generated_text']) |
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description = 'An AI assistant that works on the Microsoft Phi 2 model, which has been finetuned on the Open Assistant dataset using the QLora method, operates effectively. ' |
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title = 'AI Chat bot finetuned on Microsoft Phi 2 model using QLORA' |
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iface = gr.Interface(fn=QLoRA_Chatgpt, inputs=gr.Textbox("how can help you today", label='prompt'), outputs=gr.Textbox(label='Generated-output',scale = 2), title = title, |
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description = description) |
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iface.launch(share=True) |
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