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--- |
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inference: false |
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language: |
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- en |
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tags: |
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- 'LLaMA ' |
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- MultiModal |
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--- |
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*This is a Hugging Face friendly Model, the original can be found https://huggingface.co/liuhaotian/llava-llama-2-13b-chat-preview* |
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<br> |
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# LLaVA 13B Model Card |
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## Model details |
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**Model type:** |
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LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. |
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It is an auto-regressive language model, based on the transformer architecture. |
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**Model date:** |
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LLaVA-LLaMA-2-13B-Chat-Preview was trained in July 2023. |
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**Paper or resources for more information:** |
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https://llava-vl.github.io/ |
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## License |
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Llama 2 is licensed under the LLAMA 2 Community License, |
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Copyright (c) Meta Platforms, Inc. All Rights Reserved. |
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**Where to send questions or comments about the model:** |
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https://github.com/haotian-liu/LLaVA/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of LLaVA is research on large multimodal models and chatbots. |
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**Primary intended users:** |
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The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. |
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## Training dataset |
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- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. |
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- 80K GPT-generated multimodal instruction-following data. |
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## Evaluation dataset |
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A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs. |
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We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset. |
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See https://llava-vl.github.io/ for more details. |
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## Usage |
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usage is as follows |
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```python |
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from transformers import LlavaProcessor, LlavaLlamaForCausalLM |
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PATH_TO_CONVERTED_WEIGHTS = "shauray/Llava-Llama-2-13B-hf" |
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model = LlavaLlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) |
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processor = LlavaProcessor.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) |
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url = "https://llava-vl.github.io/static/images/view.jpg" |
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image = Image.open(requests.get(url, stream=True).raw).convert("RGB") |
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prompt = "How would you best describe the image given?" |
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inputs = processor(text=prompt, images=image, return_tensors="pt") |
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# Generate |
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generate_ids = model.generate(**inputs, max_length=30) |
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tokenizer.batch_decode(generate_ids, skip_special_tokens=True)[0] |
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"""The photograph shows a wooden dock floating on the water, with mountains in the background. It is an idyllic scene that captures both |
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nature and human-made structures at their finest moments of beauty or tranquility depending upon one's perspective as they gaze into it""" |
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``` |
