Instructions to use MaoXun/llava-lora-7-20-10-5-vicuna-7b-v1.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MaoXun/llava-lora-7-20-10-5-vicuna-7b-v1.3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lmsys/vicuna-7b-v1.3") model = PeftModel.from_pretrained(base_model, "MaoXun/llava-lora-7-20-10-5-vicuna-7b-v1.3") - Notebooks
- Google Colab
- Kaggle
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- liuhaotian/llava-pretrain-vicuna-7b-v1.3
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---
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# Brief
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This is the LoRA Model of LLaVA 7B v1.3 trained on [Synergy-General-MultimodalPairs](https://huggingface.co/datasets/MaoXun/Synergy-General-MultimodalPairs).
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# Dataset
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## Link
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[Github](https://github.com/mao-code/Synergy-General-MultimodalPairs) | [Paper](https://link.springer.com/chapter/10.1007/978-981-97-6125-8_12)
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## Introduction
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This is a visual-text pair dataset synergistically generated by a text-to-image model and multimodal large language model.
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The name of the file means (n_th generation)\_(numbers of batch)\_(numbers of initial description of each batch)\_(numbers of refined cycles of each initial description)
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base_model:
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- lmsys/vicuna-7b-v1.3
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---
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# Brief
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This is the LoRA Model of LLaVA 7B v1.3 trained on [Synergy-General-MultimodalPairs](https://huggingface.co/datasets/MaoXun/Synergy-General-MultimodalPairs).
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The dataset is to enhance the ability of describing images in detail for vision language models (VLM).
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Below is the introduction of this dataset.
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# Dataset
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### Link
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[Github](https://github.com/mao-code/Synergy-General-MultimodalPairs) | [Paper](https://link.springer.com/chapter/10.1007/978-981-97-6125-8_12)
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### Introduction
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This is a visual-text pair dataset synergistically generated by a text-to-image model and multimodal large language model.
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The name of the file means (n_th generation)\_(numbers of batch)\_(numbers of initial description of each batch)\_(numbers of refined cycles of each initial description)
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