initial commit

app.py

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+import time
+
+from transformers import T5ForConditionalGeneration, T5Tokenizer, GenerationConfig
+import gradio as gr
+
+MODEL_NAME = "jbochi/madlad400-10b-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="shn", label="Target Language"),
+        gr.components.Slider(
+            minimum=5,
+            maximum=128,
+            value=50,
+            label="Max length",
+        ),
+    ]
+    examples = [
+        ["I'm walking on the beach", "shn", 50],
+        ["မႂ်ႇသုင်ၶႃႈ", "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()

requirements.txt

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+gradio
+transformers
+sentencepiece
+safetensors
+accelerate