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app.py

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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)
+

requirements.txt

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+transformers
+torch
+streamlit
+