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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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model_id = "deepseek-ai/deepseek-llm-7b-chat" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") |
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def generate_response(prompt, temperature,top_p,max_new_tokens,repetition_penalty): |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate( |
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**inputs, |
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do_sample=True, |
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temperature=temperature, |
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top_p=top_p, |
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max_new_tokens=max_new_tokens, |
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repetition_penalty=repetition_penalty, |
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) |
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return tokenizer.decode(outputs[0], skip_special_tokens=True) |
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demo = gr.Interface(fn=generate_response, |
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inputs=[ |
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gr.Textbox(label="Prompt", lines=6, placeholder="Ask something..."), |
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gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider(0.1, 1.0, value=0.9, step=0.1, label="top_p"), |
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gr.Slider(32.0, 2048.0, value=512.0, step=1.0, label="max_new_tokens"), |
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gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="repetition_penalty"), |
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], |
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outputs="text" |
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) |
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demo.launch() |
