| import gradio as gr | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline | |
| model = AutoModelForCausalLM.from_pretrained("checkpoint_500",trust_remote_code=True) | |
| tokenizer = AutoTokenizer.from_pretrained("checkpoint_500", trust_remote_code=True) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| def inference(prompt, count): | |
| pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200) | |
| result = pipe(f"### Human: {prompt}",max_new_tokens=count) | |
| out_text = result[0]['generated_text'] | |
| return out_text | |
| title = "TSAI S21 Assignment: Adaptive QLoRA training on open assist oasst1 dataset, using microsoft/phi2 model" | |
| description = "A simple Gradio interface that accepts a context and generates GPT like text " | |
| examples = [["What is a large language model?","200"], | |
| ["Explain about monopsony","200"] | |
| ] | |
| demo = gr.Interface( | |
| inference, | |
| inputs = [gr.Textbox(placeholder="Enter a prompt"), gr.Textbox(placeholder="Enter number of characters you want to generate")], | |
| outputs = [gr.Textbox(label="Chat GPT like text")], | |
| title = title, | |
| description = description, | |
| examples = examples | |
| ) | |
| demo.launch() |