Create app.py

app.py

@@ -0,0 +1,67 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+import torch
+from threading import Thread
+
+MODEL_ID = "HODACHI/EZO-Common-9B-gemma-2-it"
+DTYPE = torch.bfloat16
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="cuda",
+    torch_dtype=DTYPE,
+)
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    max_tokens,
+    temperature,
+    top_p,
+):
+    chat = []
+    for user, assistant in history:
+        chat.append({"role": "user", "content": user})
+        chat.append({"role": "assistant", "content": assistant})
+    chat.append({"role": "user", "content": message})
+
+    prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+    
+    generation_kwargs = dict(
+        input_ids=inputs,
+        max_new_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        do_sample=True,
+        streamer=streamer,
+    )
+    
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    response = ""
+    for new_text in streamer:
+        response += new_text
+        yield response
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Slider(minimum=1, maximum=2048, value=150, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()