Create README.md

README.md

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+---
+license: llama3
+language:
+- it
+base_model:
+- meta-llama/Meta-Llama-3-8B
+- openai/clip-vit-large-patch14-336
+pipeline_tag: text-generation
+---
+
+# Model Card for LLaVA-NDiNO_pt_short_long
+
+## Model description
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**LLaVA-NDiNO** is a family of *Large Vision Language Models (LVLMs)* that have been trained for the Italian language. 
+
+The model was trained by instruction-tuning [LLaVA-NDiNO_pt_short](https://huggingface.co/swap-uniba/LLaVA-NDiNO_pt_short) on an Italian machine-translated version of [LLaVA Conversation 58k](https://huggingface.co/datasets/jxu124/llava_conversation_58k).
+
+If you are interested in more details regarding the training procedure, you can find the code we used at the following link:
+- **Repository:** https://github.com/swapUniba/LLaVA-NDiNO
+
+- **Developed by:** Elio Musacchio, Lucia Siciliani, Pierpaolo Basile, Giovanni Semeraro
+- **Funded by:** PNRR project FAIR - Future AI Research
+- **Compute infrastructure:** [Leonardo](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer
+- **Model type:** LLaMA 3 + CLIP
+- **Language(s) (NLP):** Italian
+- **License:** Llama 3 Community License 
+- **Finetuned from model:** [swap-uniba/LLaVA-NDiNO_pt_short](https://huggingface.co/swap-uniba/LLaVA-NDiNO_pt_short)
+
+
+## Example Usage
+
+```python
+import torch
+import requests
+
+from PIL import Image
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, set_seed
+
+model_name = "swap-uniba/LLaVA-NDiNO_pt_short_long"
+
+processor = LlavaNextProcessor.from_pretrained(model_name)
+model = LlavaNextForConditionalGeneration.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto") 
+
+url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+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 %}"
+
+conversation = [
+    {
+        "role": "user",
+        "content": "<image>\nCosa c'è di strano in questa immagine?"
+    },
+]
+
+prompt = processor.apply_chat_template(conversation, chat_template, add_generation_prompt=True)
+inputs = processor(prompt, image, return_tensors="pt")
+
+set_seed(42)
+output = model.generate(**inputs, max_new_tokens=4096)
+
+print(processor.decode(output[0][inputs.input_ids.shape[1]:]))
+```
+
+## Citation
+
+```
+@inproceedings{musacchioLLaVANDiNO,
+  title={LLaVA-NDiNO: Empowering LLMs with Multimodality for the Italian Language},
+  author={Musacchio, Elio and Siciliani, Lucia and Basile, Pierpaolo and Semeraro, Giovanni},
+  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)},
+  year={2024}
+}
+```