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4df5e6a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | # app.py
from transformers import pipeline
import gradio as gr
# ======================
# Configuration
# ======================
MODEL_ID = "microsoft/Phi-3-mini-128k-instruct"
# ======================
# Load Model with pipeline
# ======================
print(f"🚀 Loading model: {MODEL_ID}")
pipe = pipeline(
"text-generation",
model=MODEL_ID,
trust_remote_code=False,
torch_dtype="auto", # Auto-select float16 on GPU
device_map="auto", # Use GPU if available
return_full_text=False, # Only return assistant's reply
pad_token_id=198, # Phi-3: common pad_token_id (for <|endoftext|>)
)
print("✅ Pipeline loaded!")
# ======================
# Response Function
# ======================
def respond(message, history):
if not message.strip():
return ""
# Build conversation using chat template
messages = [
{"role": "user", "content": msg["content"]}
for msg in history
]
messages.append({"role": "user", "content": message})
# Generate response
outputs = pipe(
messages,
max_new_tokens=1024,
temperature=0.7,
top_p=0.9,
do_sample=True,
stop_strings=["<|end|>", "<|endoftext|>"], # Auto-stopping
truncation=True,
max_length=128000,
)
# Extract response text
response = outputs[0]["generated_text"] if outputs else ""
return response
# ======================
# Gradio Interface
# ======================
demo = gr.ChatInterface(
fn=respond,
chatbot=gr.Chatbot(height=600, type="messages"),
textbox=gr.Textbox(placeholder="Ask me anything...", container=False, scale=7),
title="🧠 Phi-3 Mini (128K) Chat - Simple Pipeline Version",
description="A lightweight demo using `transformers.pipeline` for clean, readable code.",
examples=[
"Who are you?",
"Explain quantum computing in simple terms",
"Write a Python function to reverse a string"
],
)
# ======================
# Launch
# ======================
if __name__ == "__main__":
demo.launch()
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