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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](http://bunny.baai.ac.cn) | 🤗 [GGUF](https://huggingface.co/BAAI/Bunny-Llama-3-8B-V-gguf) |
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This is Bunny-Llama-3-8B-V. |
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We also provide v1.1 version accepting high-resolution images up to 1152x1152. 🤗 [v1.1](https://huggingface.co/BAAI/Bunny-v1_1-Llama-3-8B-V) |
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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 Llama-3-8B, Phi-1.5, StableLM-2, Qwen1.5, MiniCPM 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-Llama-3-8B-V, which is built upon [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) and [Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). 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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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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Users especially those in Chinese mainland may want to refer to a HuggingFace [mirror site](https://hf-mirror.com). |
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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-Llama-3-8B-V', |
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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-Llama-3-8B-V', |
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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, |
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repetition_penalty=1.0 # increase this to avoid chattering |
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)[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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