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license: apache-2.0
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inference: false
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license: apache-2.0
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---
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# Model Card
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<p align="center">
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<img src="./icon.png" alt="Logo" width="350">
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</p>
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📖 [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, Qwen1.5-1.8B 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-v1.0-3B 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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Bunny-v1_0-2B-zh employs [Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) as the language model and [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) as the vision encoder.
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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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Here we show a code snippet to show you how to use the model with transformers:
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```python
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import torch
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import transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from PIL import Image
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import warnings
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# disable some warnings
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transformers.logging.set_verbosity_error()
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transformers.logging.disable_progress_bar()
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warnings.filterwarnings('ignore')
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# set device
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torch.set_default_device('cpu') # or 'cuda'
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# create model
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model = AutoModelForCausalLM.from_pretrained(
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'BAAI/Bunny-v1_0-2B-zh',
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torch_dtype=torch.float16,
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device_map='auto',
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trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained(
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'BAAI/Bunny-v1_0-2B-zh',
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trust_remote_code=True)
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# text prompt
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prompt = 'Why is the image funny?'
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text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
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text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
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input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
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# image, sample images can be found in images folder
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image = Image.open('example_2.png')
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image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)
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# generate
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output_ids = model.generate(
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input_ids,
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images=image_tensor,
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max_new_tokens=100,
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use_cache=True)[0]
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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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```shell
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pip install torch transformers accelerate pillow
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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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