--- license: mit datasets: - Cynaptics/persona-chat - rizalHidayat/bot-dialog language: - en metrics: - accuracy base_model: - distilbert/distilgpt2 pipeline_tag: text-generation library_name: transformers tags: - llm - text-generation-inference --- # ✨DarkNeuron-AI/darkneuron-chat-v1.1 **DarkNeuron-Chat v1.1** is a chatbot designed for basic, friendly conversations. It provides clear and concise responses and is suitable for general use. --- ## 👍Model Overview - **Model type:** GPT-based causal language model - **Purpose:** Basic conversational chatbot - **Training data:** Fine-tuned on [Persona-Chat](https://huggingface.co/datasets/Cynaptics/persona-chat) and [Bot-Dialog](https://huggingface.co/datasets/rizalHidayat/bot-dialog) datasets - **Intended audience:** General users, students, hobbyists, and researchers interested in chatbot interactions --- ## 🌟Installation Install the latest version of Transformers: ```bash !pip install --upgrade transformers torch ``` --- ## 👽Example Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline import torch, gc # Load tokenizer and model model_name = "DarkNeuron-AI/darkneuron-chat-v1.1" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # Use GPU if available device = 0 if torch.cuda.is_available() else -1 # Create chatbot pipeline chatbot = pipeline( "text-generation", model=model, tokenizer=tokenizer, device=device, return_full_text=False ) # Optional: Free GPU memory gc.collect() torch.cuda.empty_cache() # Interactive chat loop print("Chatbot ready! Type 'exit' or 'quit' to stop.\n") while True: user_input = input("User: ") if user_input.lower() in ["exit", "quit"]: print("Chat ended.") break prompt = f"User: {user_input}\nBot:" response = chatbot( prompt, max_length=100, do_sample=True, temperature=0.7, top_p=0.9, num_return_sequences=1 ) print(response[0]["generated_text"]) ``` # Developed With ❤️ By DarkNeuronAI