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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x22B-v0.1") |
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model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-v0.1", device_map="auto") |
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def generate_text(prompt, max_length=500, temperature=0.7, top_k=50, top_p=0.95, num_return_sequences=1): |
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input_ids = tokenizer.encode(prompt, return_tensors="pt") |
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output = model.generate( |
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input_ids, |
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max_length=max_length, |
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temperature=temperature, |
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top_k=top_k, |
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top_p=top_p, |
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num_return_sequences=num_return_sequences, |
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) |
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generated_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0] |
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return generated_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=5, label="Input Prompt"), |
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gr.inputs.Slider(minimum=100, maximum=1000, default=500, step=50, label="Max Length"), |
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gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.7, step=0.1, label="Temperature"), |
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gr.inputs.Slider(minimum=1, maximum=100, default=50, step=1, label="Top K"), |
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gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.95, step=0.05, label="Top P"), |
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gr.inputs.Slider(minimum=1, maximum=10, default=1, step=1, label="Num Return Sequences"), |
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], |
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outputs=gr.outputs.Textbox(label="Generated Text"), |
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title="MixTRAL 8x22B Text Generation", |
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description="Use this interface to generate text using the MixTRAL 8x22B language model.", |
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) |
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iface.launch() |