Create app.py

app.py

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+import threading
+import torch
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TextIteratorStreamer,
+)
+import gradio as gr
+
+# Load model and tokenizer
+model_id = "microsoft/bitnet-b1.58-2B-4T"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+def respond(
+    message: str,
+    history: list[tuple[str, str]],
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
+):
+    """
+    Generate a chat response using streaming with TextIteratorStreamer.
+
+    Args:
+        message: User's current message.
+        history: List of (user, assistant) tuples from previous turns.
+        system_message: Initial system prompt guiding the assistant.
+        max_tokens: Maximum number of tokens to generate.
+        temperature: Sampling temperature.
+        top_p: Nucleus sampling probability.
+
+    Yields:
+        The growing response text as new tokens are generated.
+    """
+    # Assemble messages
+    messages = [{"role": "system", "content": system_message}]
+    for user_msg, bot_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if bot_msg:
+            messages.append({"role": "assistant", "content": bot_msg})
+    messages.append({"role": "user", "content": message})
+
+    # Prepare prompt and tokenize
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    # Set up streamer for real-time output
+    streamer = TextIteratorStreamer(
+        tokenizer, skip_prompt=True, skip_special_tokens=True
+    )
+    generate_kwargs = dict(
+        **inputs,
+        streamer=streamer,
+        max_new_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        do_sample=True,
+    )
+    # Start generation in a separate thread
+    thread = threading.Thread(target=model.generate, kwargs=generate_kwargs)
+    thread.start()
+
+    # Stream tokens back to user
+    response = ""
+    for new_text in streamer:
+        response += new_text
+        yield response
+
+# Initialize Gradio chat interface
+
+demo = gr.ChatInterface(
+    fn=respond,
+    title="Bitnet-b1.58-2B-4T Chatbot",
+    description="This chat application is powered by Microsoft BitNet-B1 and designed for natural conversations.",
+    examples=[
+        # Each example: [message, system_message, max_new_tokens, temperature, top_p]
+        [
+            "Hello! How are you?",
+            "You are a helpful AI assistant.",
+            512,
+            0.7,
+            0.95,
+        ],
+        [
+            "Can you code a snake game in Python?",
+            "You are a helpful AI assistant.",
+            512,
+            0.7,
+            0.95,
+        ],
+    ],
+    additional_inputs=[
+        gr.Textbox(
+            value="You are a helpful AI assistant.",
+            label="System message"
+        ),
+        gr.Slider(
+            minimum=1,
+            maximum=2048,
+            value=512,
+            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()