update

app.py

@@ -0,0 +1,109 @@
+import os
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import gradio as gr
+import spaces
+
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+if not huggingface_token:
+    pass
+    print("no HUGGINGFACE_TOKEN if you need set secret ")
+    #raise ValueError("HUGGINGFACE_TOKEN environment variable is not set")
+
+model_id = "microsoft/Phi-3-mini-128k-instruct"
+
+device = "auto" # torch.device("cuda" if torch.cuda.is_available() else "cpu")
+dtype = torch.bfloat16
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=huggingface_token)
+
+
+import time
+time.sleep(10)
+
+
+print(model_id,device,dtype)
+histories = []
+contents = []
+    
+def call_generate_text(prompt, system_message="You are a helpful assistant."):
+    
+    print(histories)
+    print(contents)
+    
+    if prompt =="":
+        print("empty prompt return")
+        return ""
+    global initialized
+    if not initialized:
+        initialized = True
+        #return
+    try:
+        text = generate_text(prompt,system_message)
+        contents.append(text)
+        return text
+    except RuntimeError  as e:
+        print(f"An unexpected error occurred: {e}")
+
+    return ""
+
+    
+        
+initialized = False
+
+iface = gr.Interface(
+    fn=call_generate_text,
+    inputs=[
+        gr.Textbox(lines=3, label="Input Prompt"),
+        gr.Textbox(lines=2, label="System Message", value="あなたは親切なアシスタントで常に日本語で返答します。"),
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="Phi-3-mini-128k-instruct",
+    description="Phi-3-mini-128k-instruct",
+)
+print("Initialized")
+
+# keeping model seems make crash
+
+@spaces.GPU(duration=100)
+def generate_text(prompt, system_message="You are a helpful assistant."):
+    #print(prompt,system_message)
+
+    global histories
+   
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id, token=huggingface_token ,torch_dtype=dtype,device_map=device
+    )
+    #print(system_message)
+    text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer,torch_dtype=dtype,device_map=device) #pipeline has not to(device)
+
+    messages = [
+        {"role": "system", "content": system_message},
+    ]
+    
+    messages += histories 
+
+    user_message = {"role": "user", "content": prompt}
+
+    messages += [user_message]
+
+    #print(messages)
+    
+    result = text_generator(messages, max_new_tokens=256, do_sample=True, temperature=0.7)
+
+    generated_output = result[0]["generated_text"]
+    if isinstance(generated_output, list):
+        for message in reversed(generated_output):
+            if message.get("role") == "assistant":
+                content= message.get("content", "No content found.")
+                histories += [user_message,{"role": "assistant", "content": content}]
+                print(f"history = {len(histories)}")
+                return content
+            
+        return "No assistant response found."
+    else:
+        return "Unexpected output format."
+
+if __name__ == "__main__":
+    print("Main")
+    iface.launch()

requirements.txt

@@ -0,0 +1,6 @@
+numpy
+torch
+spaces
+accelerate
+bitsandbytes
+transformers