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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = "aburnazy/opt-350m-hy-wiki-alpaca" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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def generate_text(prompt, temperature, top_k, top_p, max_length): |
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inputs = tokenizer.encode(prompt, return_tensors="pt") |
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outputs = model.generate(inputs, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, do_sample=True) |
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text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return text |
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iface = gr.Interface( |
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fn=generate_text, |
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inputs=[ |
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gr.inputs.Textbox(lines=2, default=f"""### Instruction: |
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Ո՞րն է Հայաստանի մայրաքաղաքը |
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### Response: |
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"""), |
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gr.inputs.Slider(minimum=0, maximum=1, step=0.01, default=0.1, label='Temperature'), |
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gr.inputs.Slider(minimum=0, maximum=100, step=1, default=20, label='Top K'), |
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gr.inputs.Slider(minimum=0, maximum=1, step=0.01, default=0.1, label='Top P'), |
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gr.inputs.Slider(minimum=10, maximum=1024, step=1, default=512, label='Max Length'), |
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], |
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outputs="text" |
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) |
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iface.launch() |
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