79 lines
3.1 KiB
Markdown
79 lines
3.1 KiB
Markdown
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---
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license: llama3
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datasets:
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- swap-uniba/the_cauldron_ita
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language:
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- it
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base_model:
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- meta-llama/Meta-Llama-3-8B
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- openai/clip-vit-large-patch14-336
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pipeline_tag: text-generation
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---
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# Model Card for LLaVA-NDiNO_pt_short_it
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## Model description
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<!-- Provide a quick summary of what the model is/does. -->
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**LLaVA-NDiNO** is a family of *Large Vision Language Models (LVLMs)* that have been trained for the Italian language.
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The model was trained by instruction-tuning [**LLaVA-NDiNO_pt**](https://huggingface.co/m-elio/LLaVA-NDiNO_pt) on an Italian machine-translated version of [The Cauldron](HuggingFaceM4/the_cauldron).
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If you are interested in more details regarding the training procedure, you can find the code we used at the following link:
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- **Repository:** https://github.com/swapUniba/LLaVA-NDiNO
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- **Developed by:** Elio Musacchio, Lucia Siciliani, Pierpaolo Basile, Giovanni Semeraro
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- **Funded by:** PNRR project FAIR - Future AI Research
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- **Compute infrastructure:** [Leonardo](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer
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- **Model type:** LLaMA 3 + CLIP
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- **Language(s) (NLP):** Italian
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- **License:** Llama 3 Community License
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- **Finetuned from model:** [swap-uniba/LLaVA-NDiNO_pt](https://huggingface.co/swap-uniba/LLaVA-NDiNO_pt)
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## Example Usage
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```python
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import torch
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import requests
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from PIL import Image
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from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, set_seed
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model_name = "swap-uniba/LLaVA-NDiNO_pt_short_it"
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processor = LlavaNextProcessor.from_pretrained(model_name)
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model = LlavaNextForConditionalGeneration.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
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url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
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conversation = [
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{
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"role": "user",
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"content": "<image>\nCosa c'è di strano in questa immagine?"
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},
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]
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prompt = processor.apply_chat_template(conversation, chat_template, add_generation_prompt=True)
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inputs = processor(prompt, image, return_tensors="pt")
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set_seed(42)
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output = model.generate(**inputs, max_new_tokens=4096)
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print(processor.decode(output[0][inputs.input_ids.shape[1]:]))
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```
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## Citation
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```
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@inproceedings{musacchioLLaVANDiNO,
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title={LLaVA-NDiNO: Empowering LLMs with Multimodality for the Italian Language},
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author={Musacchio, Elio and Siciliani, Lucia and Basile, Pierpaolo and Semeraro, Giovanni},
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booktitle={Proceedings of the Eighth Workshop on Natural Language for Artificial Intelligence (NL4AI 2024) co-located with 23th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2024)},
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year={2024}
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}
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```
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