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| import time | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer, GenerationConfig | |
| import gradio as gr | |
| MODEL_NAME = "jbochi/madlad400-3b-mt" | |
| print(f"Loading {MODEL_NAME} tokenizer...") | |
| tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME) | |
| print(f"Loading {MODEL_NAME} model...") | |
| model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME, device_map="auto") | |
| def inference(input_text, target_language, max_length): | |
| global model, tokenizer | |
| start_time = time.time() | |
| input_ids = tokenizer( | |
| f"<2{target_language}> {input_text}", return_tensors="pt" | |
| ).input_ids | |
| outputs = model.generate( | |
| input_ids=input_ids.to(model.device), | |
| generation_config=GenerationConfig(max_length=max_length), | |
| ) | |
| result = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| end_time = time.time() | |
| result = { | |
| 'result': result, | |
| 'inference_time': end_time - start_time, | |
| 'input_token_ids': input_ids[0].tolist(), | |
| 'output_token_ids': outputs[0].tolist(), | |
| } | |
| return result | |
| def run(): | |
| tokens = [tokenizer.decode(i) for i in range(500)] | |
| lang_codes = [token[2:-1] for token in tokens if token.startswith("<2")] | |
| inputs = [ | |
| gr.components.Textbox(lines=5, label="Input text"), | |
| gr.components.Dropdown(lang_codes, value="en", label="Target Language"), | |
| gr.components.Slider( | |
| minimum=5, | |
| maximum=128, | |
| value=50, | |
| label="Max length", | |
| ), | |
| ] | |
| examples = [ | |
| ["I'm a mad lad!", "es", 50], | |
| ["千里之行，始於足下", "en", 50], | |
| ] | |
| outputs = gr.components.JSON() | |
| title = f"{MODEL_NAME} demo" | |
| demo_status = "Demo is running on CPU" | |
| description = f"Details: https://huggingface.co/{MODEL_NAME}. {demo_status}" | |
| gr.Interface( | |
| inference, | |
| inputs, | |
| outputs, | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| ).launch() | |
| if __name__ == "__main__": | |
| run() | |