Add application file

app.py

@@ -0,0 +1,55 @@
+# app.py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+import torch
+
+# load model and tokenizer
+model_name = "inclusionAI/Ling-lite-1.5"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",  
+    trust_remote_code=True
+).eval()
+
+# define chat function
+def chat(user_input, max_new_tokens=512):
+    # chat history
+    messages = [
+        {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
+        {"role": "user", "content": user_input}
+    ]
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    
+    # encode the input prompt
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # generate response
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[-1]:], skip_special_tokens=True)
+    return response
+
+# Construct Gradio Interface
+interface = gr.Interface(
+    fn=chat,
+    inputs=[
+        gr.Textbox(lines=5, label="输入你的问题"),
+        gr.Slider(minimum=100, maximum=1024, step=50, label="生成长度")
+    ],
+    outputs=gr.Textbox(label="模型回复"),
+    title="Ling-lite-1.5 MoE 模型 Demo",
+    description="基于 [inclusionAI/Ling-lite-1.5](https://huggingface.co/inclusionAI/Ling-lite-1.5)  的对话式文本生成演示。",
+    examples=[
+        ["介绍大型语言模型的基本概念", 512],
+        ["如何解决数学问题中的长上下文依赖？", 768]
+    ]
+)
+
+# launch Gradion Service
+interface.launch()