Upload chat_light.py with huggingface_hub

chat_light.py

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+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load a small, fast model
+model_name = "distilgpt2"  # smaller than full GPT-2, uses less memory
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+conversation = "You are a kind AI assistant. Stay on topic.\n"
+print("\nType your messages below. Type 'quit' to exit.\n")
+
+while True:
+    try:
+        user_input = input("You: ")
+    except KeyboardInterrupt:
+        print("\nEnding chat. Bye!")
+        break
+
+    if user_input.lower() in ["quit", "exit"]:
+        print("Ending chat. Bye!")
+        break
+
+    conversation += f"User: {user_input}\nAI:"
+
+    # Tokenize efficiently and limit input size to avoid memory issues
+    inputs = tokenizer(conversation, return_tensors="pt", truncation=True, max_length=512)
+
+    # Generate a short response to save memory and speed
+    output = model.generate(
+        **inputs,
+        max_new_tokens=40,       # shorter responses = faster
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    # Decode and extract AI response
+    ai_response = tokenizer.decode(output[0], skip_special_tokens=True).split("AI:")[-1].strip()
+    print(f"Baby AI: {ai_response}")
+
+    # Add AI response to conversation for context
+    conversation += f"{ai_response}\n"