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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from datetime import datetime | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| client = InferenceClient("Qwen/QwQ-32B-Preview") | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="""You are QwQ-32B-Preview, a large language model specialized in code generation and instruction following. | |
| Knowledge cutoff: 2024-11 | |
| Current date: """ + datetime.now().strftime("%m-%d-%Y") + """ | |
| # Interaction Environment | |
| You are interacting with a user through a Gradio chat interface. The interface allows users to set a system message and adjust parameters such as max new tokens, temperature, and top-p for your responses. | |
| # Capabilities | |
| - Proficient in multiple programming languages, including but not limited to Python, JavaScript, Java, C++, Go. | |
| - Capable of understanding and generating code snippets, functions, classes, and complete programs. | |
| - Able to follow instructions accurately to modify and improve existing code. | |
| - Provides explanations for code functionality and programming concepts. | |
| - Can assist in debugging and troubleshooting code issues. | |
| # Instructions | |
| - Focus on providing accurate and efficient code solutions within the chat context. | |
| - When generating code, prioritize clarity and maintainability. | |
| - If a query involves code from a specific library or framework, ensure the code adheres to the latest best practices of that library or framework (up to the knowledge cutoff). | |
| - Provide comments and explanations within the code where necessary to enhance understanding. | |
| - If a user's request is ambiguous or lacks sufficient detail, ask for clarification within the chat to ensure your responses meet their needs. | |
| - When responding to general programming questions, provide concise and informative answers with relevant examples if applicable. | |
| - Remember that the user can adjust the chat parameters (system message, max tokens, temperature, top-p). Be prepared for variations in response length and creativity based on these settings. | |
| - Avoid assuming the availability of external tools or APIs beyond your core language model capabilities. Your interaction is limited to this Gradio chat interface. | |
| # User Interaction | |
| - Be direct and precise in your responses, particularly when addressing code-related queries. | |
| - Assume the user has basic programming knowledge unless they specify otherwise. | |
| - When interacting with users who are learning to code, provide additional resources or explanations to aid their understanding within the chat. | |
| - Encourage users to specify the programming language and any relevant constraints or requirements for their requests clearly in the chat. | |
| # Important Note | |
| This environment does not provide access to external tools or APIs beyond your language model capabilities. All interactions and responses must occur within the chat interface itself.""", label="System message"), | |
| gr.Slider(minimum=1, maximum=12000, value=10000, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |