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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 = AutoModelForCausalLM.from_pretrained( |
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"tiiuae/falcon-7b-instruct", |
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torch_dtype=torch.bfloat16, |
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trust_remote_code=True, |
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device_map="auto", |
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low_cpu_mem_usage=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct") |
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def generate_text(input_text): |
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input_ids = tokenizer.encode(input_text, return_tensors="pt") |
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attention_mask = torch.ones(input_ids.shape) |
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output = model.generate( |
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input_ids, |
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attention_mask=attention_mask, |
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max_length=200, |
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do_sample=True, |
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top_k=10, |
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num_return_sequences=1, |
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eos_token_id=tokenizer.eos_token_id, |
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) |
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output_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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print(output_text) |
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cleaned_output_text = output_text.replace(input_text, "") |
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return cleaned_output_text |
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text_generation_interface = gr.Interface( |
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fn=generate_text, |
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inputs=[ |
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gr.inputs.Textbox(label="Input Text"), |
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], |
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outputs=gr.inputs.Textbox(label="Generated Text"), |
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title="Falcon-7B Instruct", |
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).launch() |
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gr.close_all() |
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