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README.md
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---
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**The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.**
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## Setting Up
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```python
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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from peft import PeftModel, PeftConfig
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# Define the repository ID
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repo_id = "Miguelpef/bart-base-lora-3DPrompt"
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# Load the PEFT configuration from the Hub
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peft_config = PeftConfig.from_pretrained(repo_id)
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# Load the base model from the Hub
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model = AutoModelForSeq2SeqLM.from_pretrained(peft_config.base_model_name_or_path)
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# Load the tokenizer from the Hub
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tokenizer = AutoTokenizer.from_pretrained(repo_id)
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# Wrap the base model with PEFT
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model = PeftModel.from_pretrained(model, repo_id)
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# Now you can use the model for inference as before
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def generar_prompt_desde_objeto(objeto):
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prompt = objeto
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inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
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outputs = model.generate(**inputs, max_length=100)
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prompt_generado = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return prompt_generado
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mi_objeto = "Mesa grande marrón" #Change this object
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prompt_generado = generar_prompt_desde_objeto(mi_objeto)
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print({prompt_generado})
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```
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