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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
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from peft import PeftModel |
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import torch |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model_path = "Hack337/WavGPT-1.0" |
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct") |
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-1.5B-Instruct", |
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torch_dtype="auto", device_map="auto") |
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model = PeftModel.from_pretrained(model, model_path) |
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def respond( |
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message, |
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history: list[tuple[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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): |
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messages = [{"role": "system", "content": system_message}] |
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for val in history: |
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if val[0]: |
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messages.append({"role": "user", "content": val[0]}) |
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if val[1]: |
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messages.append({"role": "assistant", "content": val[1]}) |
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messages.append({"role": "user", "content": message}) |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=max_tokens, |
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pad_token_id=tokenizer.eos_token_id, |
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temperature=temperature, |
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top_p=top_p |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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return response |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox(value="Вы очень полезный помощник.", label="System message"), |
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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], |
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) |
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if __name__ == "__main__": |
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demo.launch() |
