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

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📖 [Technical report](https://arxiv.org/abs/2402.11530) | 🏠 [Code](https://github.com/BAAI-DCAI/Bunny) | 🐰 [3B Demo](https://wisemodel.cn/spaces/baai/Bunny) | 🐰 [8B Demo](https://252412006bcde38bfa.gradio.live/) This is Bunny-Llama-3-8B-V. 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 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-Llama-3-8B-V, which is built upon [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) and [Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B). More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Bunny). ![comparison](comparison.png) # 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 ``` ```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 torch.set_default_device('cpu') # or 'cuda' # create model model = AutoModelForCausalLM.from_pretrained( 'BAAI/Bunny-Llama-3-8B-V', torch_dtype=torch.float16, device_map='auto', trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained( 'BAAI/Bunny-Llama-3-8B-V', 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) # 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) # generate output_ids = model.generate( input_ids, images=image_tensor, max_new_tokens=100, use_cache=True)[0] print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()) ```