| import gradio as gr | |
| import torch | |
| from my_gpt import my_gpt | |
| from tokenizer.tokenizer import BPE | |
| ##Load model | |
| model = my_gpt.load_pretrained("model/model_1000_.bin") | |
| # model.to(torch.device("cpu")) | |
| # model.save_pretrained("model/model_1000_cpu.bin") | |
| # exit() | |
| tokenizer = BPE() | |
| def generate(input_text): | |
| tokens = tokenizer.encode(input_text) | |
| gen_ids = model.generate(torch.tensor([tokens])) | |
| output = tokenizer.decode(gen_ids[0].tolist()) | |
| return output | |
| iface = gr.Interface(fn=generate, | |
| inputs="text", | |
| outputs="text", | |
| title="NoobGPT - 1000 steps", | |
| description="""This 13M param model is trained for 1000steps only and has seen only 1M tokens. It is not | |
| able to generate perfect sentences/words but has acquired a rudimentary understanding of the English language""") | |
| iface.launch() |