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--- |
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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://d61b68ac93656b614f.gradio.live/) |
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This is Bunny-v1.1-4B. |
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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-3-mini, Llama-3-8B, 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. |
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We provide Bunny-v1.1-4B, which is built upon [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) and [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) with [S \\(^{2}\\)-Wrapper](https://github.com/bfshi/scaling_on_scales), supporting 1152x1152 resolution. More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Bunny). |
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| | MME \\(^{\text{P}}\\) | MME \\(^{\text{C}}\\) | MMB \\(^{\text{T/D}}\\) | MMB-CN \\(^{\text{T/D}}\\) |SEED(-IMG) | MMMU \\(^{\text{V/T}}\\) | VQA \\(^{\text{v2}}\\) | GQA | SQA \\(^{\text{I}}\\) | POPE | |
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| ------------------ | :--------------: | :--------------: |:--------------: | :----------------: | :--: | :-----------------: | :---------------: | :--: | :--------------: | :--: | |
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| Bunny-v1.1-4B | 1503.9 | 362.9 | 74.1/74.1 |66.3/64.8 | 64.6(71.7) | 40.2/38.8 | 81.7 | 63.4 | 76.3 | 87.0 | |
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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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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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If the CUDA memory is enough, it would be faster to execute this snippet by setting `CUDA_VISIBLE_DEVICES=0`. |
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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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device = 'cuda' # or cpu |
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torch.set_default_device(device) |
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# create model |
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model = AutoModelForCausalLM.from_pretrained( |
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'BAAI/Bunny-v1_1-4B', |
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torch_dtype=torch.float16, # float32 for cpu |
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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_1-4B', |
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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][1:], dtype=torch.long).unsqueeze(0).to(device) |
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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, device=device) |
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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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