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| import transformers | |
| import torch | |
| import tokenizers | |
| import gradio as gr | |
| def get_model(model_name, model_path='pytorch_model.bin'): | |
| tokenizer = transformers.GPT2Tokenizer.from_pretrained(model_name) | |
| model = transformers.OPTForCausalLM.from_pretrained(model_name) | |
| model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))) | |
| model.eval() | |
| return model, tokenizer | |
| def predict(text, model, tokenizer, n_beams=5, temperature=2.5, top_p=0.8, length_of_generated=300): | |
| text += '\n' | |
| input_ids = tokenizer.encode(text, return_tensors="pt") | |
| length_of_prompt = len(input_ids[0]) | |
| with torch.no_grad(): | |
| out = model.generate(input_ids, | |
| do_sample=True, | |
| num_beams=n_beams, | |
| temperature=temperature, | |
| top_p=top_p, | |
| max_length=length_of_prompt + length_of_generated, | |
| eos_token_id=tokenizer.eos_token_id | |
| ) | |
| return list(map(tokenizer.decode, out))[0] | |
| model, tokenizer = get_model('big-kek/NeuroSkeptic', 'OPT13b-skeptic.bin') | |
| example = 'Who is Bill Gates really?' | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.components.Textbox(label="what is your interest?",value = example), | |
| ], | |
| outputs=[ | |
| gr.components.Textbox(label="oh! my ...",interactive = False), | |
| ], | |
| ) | |
| demo.launch() | |