import torch from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import gradio as gr torch.random.manual_seed(0) model = AutoModelForCausalLM.from_pretrained( "microsoft/Phi-3-mini-128k-instruct", device_map="auto", torch_dtype="auto", trust_remote_code=True, low_cpu_mem_usage=True, ) tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct") messages = [ {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, ] pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, ) generation_args = { "max_new_tokens": 256, "return_full_text": False, "temperature": 0.2, "do_sample": True, } def phi3_fun(message,chat_history): messages=[ {"role": "user", "content": message}, ] output = pipe(messages, **generation_args) respond = output[0]['generated_text'] return respond title = "Phi-3 " examples = [ 'How are You?', "talk about your self", ] gr.ChatInterface( fn=phi3_fun, title =title, examples = examples ).launch(debug=True) # demo = gr.Interface(fn=phi3_fun, inputs="text", outputs="text",title =title, # examples = examples) # demo.launch() # output = pipe(messages, **generation_args) # print(output[0]['generated_text'])