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Update app.py
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app.py
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@@ -1,4 +1,4 @@
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import gradio as gr
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import torch
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import time
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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@@ -79,7 +79,7 @@ def generate_response(message, temperature, max_new_tokens, top_p, task):
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print(f"Prompt: {prompt}")
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start_time = time.time()
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inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(input_ids=inputs,
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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processed_response = response.split("RESPONSE :")[-1].strip()
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end_time = time.time()
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@@ -111,4 +111,4 @@ with gr.Blocks(theme='1024m/1024m-1') as demo:
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send_btn.click(fn=generate_response, inputs=[input_text, temperature, max_new_tokens, top_p, task_dropdown], outputs=output_text)
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clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[input_text, output_text])
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if __name__ == "__main__":
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demo.queue().launch()
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"""import gradio as gr
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import torch
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import time
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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print(f"Prompt: {prompt}")
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start_time = time.time()
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inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(input_ids=inputs, max_new_tokens=max_new_tokens, use_cache=True, temperature=temperature, min_p=top_p, pad_token_id=tokenizer.eos_token_id)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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processed_response = response.split("RESPONSE :")[-1].strip()
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end_time = time.time()
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send_btn.click(fn=generate_response, inputs=[input_text, temperature, max_new_tokens, top_p, task_dropdown], outputs=output_text)
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clear_btn.click(fn=lambda: ("", ""), inputs=None, outputs=[input_text, output_text])
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if __name__ == "__main__":
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demo.queue().launch()
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