| import gradio as gr | |
| import torch | |
| from peft import PeftModel, PeftConfig | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| peft_model_id = "hackathon-somos-nlp-2023/bertin-gpt-j-6b-ner-es" | |
| config = PeftConfig.from_pretrained(peft_model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| config.base_model_name_or_path, | |
| return_dict=True, | |
| load_in_8bit=True, | |
| device_map="auto", | |
| revision="half", | |
| ) | |
| tokenizer = AutoTokenizer.from_pretrained(peft_model_id) | |
| # Load the Lora model | |
| model = PeftModel.from_pretrained(model, peft_model_id) | |
| def gen_entities(text): | |
| text = f"<SP> text: {text}\n\n entities: " | |
| batch = tokenizer(text, return_tensors="pt") | |
| with torch.cuda.amp.autocast(): | |
| output_tokens = model.generate(**batch, max_new_tokens=256, eos_token_id=50258) | |
| return tokenizer.decode(output_tokens[0], skip_special_tokens=False) | |
| iface = gr.Interface(fn=gen_entities, inputs="text", outputs="text") | |
| iface.launch() | |