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| import gradio as gr | |
| # from huggingface_hub import InferenceClient | |
| """ | |
| 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("HuggingFaceH4/zephyr-7b-beta") | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig, set_seed | |
| # from accelerate import infer_auto_device_map as iadm | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig | |
| model_name = "deepseek-ai/deepseek-math-7b-instruct" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") | |
| model.generation_config = GenerationConfig.from_pretrained(model_name) | |
| model.generation_config.pad_token_id = model.generation_config.eos_token_id | |
| def evaluate_response(problem): | |
| # problem=b'what is angle x if angle y is 60 degree and angle z in 60 degree of a traingle' | |
| problem=problem+'\nPlease reason step by step, and put your final answer within \\boxed{}.' | |
| messages = [ | |
| {"role": "user", "content": problem} | |
| ] | |
| input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") | |
| outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) | |
| result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) | |
| # result_output, code_output = process_output(raw_output) | |
| return result | |
| # def respond( | |
| # evaluate_response, | |
| # 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( | |
| # evaluate_response, | |
| # additional_inputs=[ | |
| # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| # gr.Slider(minimum=0.1, maximum=4.0, 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)", | |
| # ), | |
| # ], | |
| # ) | |
| demo = gr.Interface( | |
| fn=evaluate_response, | |
| inputs=[gr.Textbox(label="Question")], | |
| outputs=gr.Textbox(label="Answer"), | |
| title="Question and Answer Interface", | |
| description="Enter a question." | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |