--- inference: false license: apache-2.0 --- # Model Card

Logo

📖 [Technical report](https://arxiv.org/abs/2402.11530) | 🏠 [Code](https://github.com/BAAI-DCAI/Bunny) | 🐰 [Demo](http://bunny.baai.ac.cn) This is Bunny-v1.1-4B. 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. 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). | | 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 | | ------------------ | :--------------: | :--------------: |:--------------: | :----------------: | :--: | :-----------------: | :---------------: | :--: | :--------------: | :--: | | 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 | # Quickstart Here we show a code snippet to show you how to use the model with transformers. Before running the snippet, you need to install the following dependencies: ```shell pip install torch transformers accelerate pillow ``` If the CUDA memory is enough, it would be faster to execute this snippet by setting `CUDA_VISIBLE_DEVICES=0`. Users especially those in Chinese mainland may want to refer to a HuggingFace [mirror site](https://hf-mirror.com). ```python import torch import transformers from transformers import AutoModelForCausalLM, AutoTokenizer from PIL import Image import warnings # disable some warnings transformers.logging.set_verbosity_error() transformers.logging.disable_progress_bar() warnings.filterwarnings('ignore') # set device device = 'cuda' # or cpu torch.set_default_device(device) # create model model = AutoModelForCausalLM.from_pretrained( 'BAAI/Bunny-v1_1-4B', torch_dtype=torch.float16, # float32 for cpu device_map='auto', trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained( 'BAAI/Bunny-v1_1-4B', trust_remote_code=True) # text prompt prompt = 'Why is the image funny?' 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: \n{prompt} ASSISTANT:" text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('')] input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1][1:], dtype=torch.long).unsqueeze(0).to(device) # image, sample images can be found in images folder image = Image.open('example_2.png') image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device) # generate output_ids = model.generate( input_ids, images=image_tensor, max_new_tokens=100, use_cache=True, repetition_penalty=1.0 # increase this to avoid chattering )[0] print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()) ```