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import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and tokenizer
@st.cache_resource
def load_model():
model_name = "prithivMLmods/QwQ-LCoT-14B-Conversational"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto", # Automatically assign to CPU/GPU
torch_dtype=torch.float16, # Mixed precision for large models
)
return tokenizer, model
# Load resources
tokenizer, model = load_model()
# Streamlit app UI
st.title("QwQ-LCoT Chatbot")
st.write("A conversational AI powered by QwQ-LCoT-14B. Ask me anything!")
# User input
user_input = st.text_input("You: ", "")
if st.button("Send"):
if user_input.strip():
with st.spinner("Generating response..."):
# Tokenize input
inputs = tokenizer(user_input, return_tensors="pt")
# Generate response
outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7)
# Decode response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Display response
st.text_area("Bot:", value=response, height=150)
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