import gradio as gr import spaces import torch from huggingface_hub import InferenceClient import os """ 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") # pip install 'git+https://github.com/huggingface/transformers.git' token=os.getenv('token') print('token = ',token) from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistralai/Mistral-7B-v0.3" model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1" @spaces.GPU(duration=180) def respond( message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p, ): tokenizer = AutoTokenizer.from_pretrained(model_id, token= token) model = AutoModelForCausalLM.from_pretrained(model_id, token= token, torch_dtype=torch.bfloat16,attn_implementation="flash_attention_2",) messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda") outputs = model.generate(inputs, max_new_tokens=2000) gen_text=tokenizer.decode(outputs[0], skip_special_tokens=True) print(gen_text) yield gen_text # 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 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)", ), ], ) if __name__ == "__main__": demo.launch()