Upload 3 files

README.md

@@ -1,13 +1,12 @@
 ---
-title: Hy MT2
-emoji: 👁
-colorFrom: gray
-colorTo: pink
+title: Hy-MT2
+emoji: 🌍
+colorFrom: green
+colorTo: indigo
 sdk: gradio
 sdk_version: 6.14.0
 python_version: '3.12'
 app_file: app.py
 pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+short_description: Hy-MT2 multilingual translation demo
+---

app.py

@@ -0,0 +1,122 @@
+import gc
+import gradio as gr
+import spaces
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+current_model_id = None
+tokenizer = None
+model = None
+
+LANGUAGES = [
+    "Arabic", "Bengali", "Burmese", "Cantonese", "Chinese", "Czech", 
+    "Dutch", "English", "Filipino", "French", "German", "Gujarati", 
+    "Hebrew", "Hindi", "Indonesian", "Italian", "Japanese", "Kazakh", 
+    "Khmer", "Korean", "Malay", "Marathi", "Mongolian", "Persian", 
+    "Polish", "Portuguese", "Russian", "Spanish", "Tamil", "Telugu", 
+    "Thai", "Tibetan", "Traditional Chinese", "Turkish", "Ukrainian", 
+    "Urdu", "Uyghur", "Vietnamese"
+]
+
+MODELS = [
+    "tencent/Hy-MT2-30B-A3B",
+    "tencent/Hy-MT2-7B",
+    "tencent/Hy-MT2-1.8B"
+]
+
+def load_model(model_id):
+    global current_model_id, tokenizer, model
+    
+    if current_model_id == model_id:
+        return
+        
+    print(f"Switching model from {current_model_id} to {model_id}...")
+    
+    if model is not None:
+        del model
+        del tokenizer
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            
+    print(f"Loading tokenizer for {model_id}...")
+    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+    
+    print(f"Loading model {model_id}...")
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True,
+    )
+    model.eval()
+    current_model_id = model_id
+    print("Model loaded successfully.")
+
+@spaces.GPU
+def translate(source_text, target_lang, selected_model):
+    global current_model_id, tokenizer, model
+    
+    if not source_text.strip():
+        return ""
+        
+    try:
+        load_model(selected_model)
+        
+        prompt = f"Translate the following text into {target_lang}. Note that you should only output the translated result without any additional explanation:\n\n{source_text}"
+        messages = [{"role": "user", "content": prompt}]
+        
+        inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+        
+        if "30B" in selected_model:
+            gen_kwargs = {
+                "temperature": 0.7,
+                "top_p": 1.0,
+                "repetition_penalty": 1.0,
+            }
+        else:
+            gen_kwargs = {
+                "temperature": 0.7,
+                "top_p": 0.6,
+                "top_k": 20,
+                "repetition_penalty": 1.05,
+            }
+            
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=4096,
+                **gen_kwargs
+            )
+            
+        response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
+        return response
+        
+    except Exception as e:
+        return f"Error during generation: {str(e)}\n\n(Note: Zero GPU environments may timeout or run out of memory when loading large models dynamically.)"
+
+with gr.Blocks(title="Hy-MT2 Translator") as demo:
+    gr.Markdown("# Hy-MT2 Translator")
+    gr.Markdown("https://huggingface.co/collections/tencent/hy-mt2")
+    
+    with gr.Row():
+        with gr.Column():
+            source_text = gr.Textbox(label="Source Text", lines=8, placeholder="Enter text to translate...")
+            
+            with gr.Row():
+                target_lang = gr.Dropdown(choices=LANGUAGES, value="English", label="Target Language")
+                model_selector = gr.Dropdown(choices=MODELS, value="tencent/Hy-MT2-1.8B", label="Model")
+                
+            translate_btn = gr.Button("Translate", variant="primary")
+            
+        with gr.Column():
+            output_text = gr.Textbox(label="Translated Text", lines=12, interactive=False)
+            
+    translate_btn.click(
+        fn=translate,
+        inputs=[source_text, target_lang, model_selector],
+        outputs=output_text
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+spaces
+torch
+transformers>=5.6.0
+accelerate
+tqdm