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@@ -11,7 +11,7 @@ license: apache-2.0
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  πŸ“– [Technical report](https://arxiv.org/abs/2402.11530) | 🏠 [Code](https://github.com/BAAI-DCAI/Bunny) | 🐰 [Demo](https://wisemodel.cn/space/baai/Bunny)
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- Bunny is a family of lightweight but powerful multimodal models. It offers multiple plug-and-play vision encoders, like EVA-CLIP, SigLIP and language backbones, including Phi-1.5, StableLM-2 and Phi-2. To compensate for the decrease in model size, we construct more informative training data by curated selection from a broader data source. Remarkably, our Bunny-3B model built upon SigLIP and Phi-2 outperforms the state-of-the-art MLLMs, not only in comparison with models of similar size but also against larger MLLM frameworks (7B), and even achieves performance on par with 13B models.
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  The model is pretrained on LAION-2M and finetuned on Bunny-695K.
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  More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Bunny).
@@ -21,7 +21,13 @@ More details about this model can be found in [GitHub](https://github.com/BAAI-D
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  # Quickstart
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  The merged weights can be found in [Bunny-v1_0-3B](https://huggingface.co/BAAI/Bunny-v1_0-3B).
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- To use the model with transformers, use the merged weights instead of the LoRA weights:
 
 
 
 
 
 
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  ```python
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  import torch
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  print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())
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  ```
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- Before running the snippet, you need to install the following dependencies:
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-
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- ```shell
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- pip install torch transformers accelerate pillow
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- ```
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-
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  # License
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  This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses.
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  The content of this project itself is licensed under the Apache license 2.0.
 
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  πŸ“– [Technical report](https://arxiv.org/abs/2402.11530) | 🏠 [Code](https://github.com/BAAI-DCAI/Bunny) | 🐰 [Demo](https://wisemodel.cn/space/baai/Bunny)
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+ Bunny is a family of lightweight but powerful multimodal models. It offers multiple plug-and-play vision encoders, like EVA-CLIP, SigLIP and language backbones, including Phi-1.5, StableLM-2, Qwen1.5 and Phi-2. To compensate for the decrease in model size, we construct more informative training data by curated selection from a broader data source. Remarkably, our Bunny-3B model built upon SigLIP and Phi-2 outperforms the state-of-the-art MLLMs, not only in comparison with models of similar size but also against larger MLLM frameworks (7B), and even achieves performance on par with 13B models.
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  The model is pretrained on LAION-2M and finetuned on Bunny-695K.
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  More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Bunny).
 
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  # Quickstart
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  The merged weights can be found in [Bunny-v1_0-3B](https://huggingface.co/BAAI/Bunny-v1_0-3B).
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+ To use the model with transformers, use the merged weights instead of the LoRA weights.
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+
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+ Before running the snippet, you need to install the following dependencies:
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+
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+ ```shell
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+ pip install torch transformers accelerate pillow
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+ ```
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  ```python
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  import torch
 
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  print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())
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  ```
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  # License
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  This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses.
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  The content of this project itself is licensed under the Apache license 2.0.