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--- |
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license: llama3 |
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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_long |
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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/swap-uniba/LLaVA-NDiNO_pt) on an Italian machine-translated version of [LLaVA Conversation 58k](https://huggingface.co/datasets/jxu124/llava_conversation_58k). |
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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_long" |
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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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``` |
