---
pipeline_tag: image-text-to-text
---

The following is the code to run Pangea-7B using huggingface generate:
```
# Assuming that you have text_input and image_path
from transformers import LlavaNextForConditionalGeneration, AutoProcessor
import torch
from PIL import Image

image_input = Image.open(image_path)

model = LlavaNextForConditionalGeneration.from_pretrained(
            "neulab/Pangea-7B-hf", 
            torch_dtype=torch.float16
        ).to(0)
processor = AutoProcessor.from_pretrained("neulab/Pangea-7B-hf")
model.resize_token_embeddings(len(processor.tokenizer))

text_input = f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<image>\n{text_input}<|im_end|>\n<|im_start|>assistant\n"
model_inputs = processor(images=image_input, text=text_input, return_tensors='pt').to("cuda", torch.float16)
output = model.generate(**model_inputs, max_new_tokens=1024, min_new_tokens=32, temperature=1.0, top_p=0.9, do_sample=True)
output = output[0]
result = processor.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=False)

print(result)
```