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