| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| # Load model directly | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct", trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct", | |
| device_map="auto", | |
| trust_remote_code=True) | |
| # Function to generate text based on the prompt | |
| def generate_text(prompt, max_length=100): | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| inputs = inputs.to(model.device) | |
| outputs = model.generate(**inputs, max_length=max_length) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Create Gradio interface | |
| iface = gr.Interface( | |
| fn=generate_text, | |
| inputs="text", | |
| outputs="text", | |
| title="Microsoft Phi 3.5B Instruct - Text Generation" | |
| ) | |
| iface.launch() | |