--- license: apache-2.0 datasets: - AIDC-AI/Ovis-dataset library_name: transformers tags: - MLLM pipeline_tag: image-text-to-text language: - en --- # Ovis1.6-Gemma2-9B-GPTQ-Int4
## Introduction [GitHub](https://github.com/AIDC-AI/Ovis) | [Demo](https://huggingface.co/spaces/AIDC-AI/Ovis1.6-Gemma2-9B) | [Paper](https://arxiv.org/abs/2405.20797) We are excited to announce the open-sourcing of **Ovis-1.6**, our latest multi-modal large language model. Ovis is a novel Multimodal Large Language Model (MLLM) architecture, designed to structurally align visual and textual embeddings.
## Model Built upon Ovis1.5, **Ovis1.6** further enhances high-resolution image processing, is trained on a larger, more diverse, and higher-quality dataset, and refines the training process with DPO training following instruction-tuning. | Ovis MLLMs | ViT | LLM | Model Weights | Demo | |:------------------|:-----------:|:------------------:|:---------------------------------------------------------------:|:----------------------------------------------------------------:| | Ovis1.6-Gemma2-9B | Siglip-400M | Gemma2-9B-It | [Huggingface](https://huggingface.co/AIDC-AI/Ovis1.6-Gemma2-9B) | [Space](https://huggingface.co/spaces/AIDC-AI/Ovis1.6-Gemma2-9B) | ## Quantized Model: GPTQ-Int4 We quantized Ovis1.6 with AutoGPTQ. Follow these steps to run it. ### Installation 1. Run the following commands to get a basic environment. Be sure to run with CUDA 12.1. ```bash conda create -n python=3.10 conda activate pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121 pip install numpy==1.24.3 transformers==4.44.2 pillow==10.3.0 gekko pandas ``` 2. Build AutoGPTQ: We customized AutoGPTQ to support Ovis model quantization. You need to build from source to install the customized version. ```bash git clone https://github.com/kq-chen/AutoGPTQ.git cd AutoGPTQ pip install -vvv --no-build-isolation -e . ``` Check [this](https://github.com/AutoGPTQ/AutoGPTQ/issues/194) first if you are building inside a Docker container. ### Usage Below is a code snippet to run Ovis1.6-Gemma2-9B-GPTQ-Int4 with multimodal inputs. For additional usage instructions, including inference wrapper and Gradio UI, please refer to [Ovis GitHub](https://github.com/AIDC-AI/Ovis?tab=readme-ov-file#inference). ```python import torch from PIL import Image from transformers import GenerationConfig from auto_gptq.modeling import OvisGPTQForCausalLM # load model load_device = "cuda:0" # customize load device model = OvisGPTQForCausalLM.from_pretrained( "TryingHard/Ovis1.6-Gemma2-9B-GPTQ-Int4", device=load_device, multimodal_max_length=8192, trust_remote_code=True ) model.model.generation_config = GenerationConfig.from_pretrained("TryingHard/Ovis1.6-Gemma2-9B-GPTQ-Int4") text_tokenizer = model.get_text_tokenizer() visual_tokenizer = model.get_visual_tokenizer() # enter image path and prompt image_path = input("Enter image path: ") image = Image.open(image_path) text = input("Enter prompt: ") query = f'\n{text}' # format conversation prompt, input_ids, pixel_values = model.preprocess_inputs(query, [image]) attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id) input_ids = input_ids.unsqueeze(0).to(device=model.device) attention_mask = attention_mask.unsqueeze(0).to(device=model.device) pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)] # generate output with torch.inference_mode(): gen_kwargs = dict( max_new_tokens=1024, do_sample=False, top_p=None, top_k=None, temperature=None, repetition_penalty=None, eos_token_id=model.generation_config.eos_token_id, pad_token_id=text_tokenizer.pad_token_id, use_cache=True ) output_ids = model.generate(input_ids, pixel_values=pixel_values, attention_mask=attention_mask, **gen_kwargs)[0] output = text_tokenizer.decode(output_ids, skip_special_tokens=True) print(f'Output:\n{output}') ```
Batch inference ```python batch_inputs = [ ('example_image1.jpeg', 'Describe the content of this image.'), ('example_image2.jpeg', 'What is the equation in the image?') ] batch_input_ids = [] batch_attention_mask = [] batch_pixel_values = [] for image_path, text in batch_inputs: image = Image.open(image_path) query = f'\n{text}' prompt, input_ids, pixel_values = model.preprocess_inputs(query, [image]) attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id) input_ids = input_ids.unsqueeze(0).to(device=model.device) attention_mask = attention_mask.unsqueeze(0).to(device=model.device) pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)] batch_input_ids.append(input_ids.squeeze()) batch_attention_mask.append(attention_mask.squeeze()) batch_pixel_values.append(pixel_values) pad_batch_input_ids = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_input_ids],batch_first=True, padding_value=0.0).flip(dims=[1]) pad_batch_input_ids = pad_batch_input_ids[:,-model.config.multimodal_max_length:] pad_batch_attention_mask = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in batch_attention_mask],batch_first=True, padding_value=False).flip(dims=[1]) pad_batch_attention_mask = pad_batch_attention_mask[:,-model.config.multimodal_max_length:] pad_batch_pixel_values = [item for sublist in batch_pixel_values for item in sublist] # generate output with torch.inference_mode(): gen_kwargs = dict( max_new_tokens=1024, do_sample=False, top_p=None, top_k=None, temperature=None, repetition_penalty=None, eos_token_id=model.generation_config.eos_token_id, pad_token_id=text_tokenizer.pad_token_id, use_cache=True ) output_ids = model.generate(pad_batch_input_ids, pixel_values=pad_batch_pixel_values, attention_mask=pad_batch_attention_mask, **gen_kwargs) for i in range(len(batch_input_ids)): output = text_tokenizer.decode(output_ids[i], skip_special_tokens=True) print(f'Output_{i}:\n{output}') ```
## Performance Here we report the performance of Ovis1.6-Gemma2-9B-GPTQ-Int4. The results are obtained with VLMEvalkit. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/645cb4b4a03f3ebb0bde20e0/pSKiBhCy1S6Fb1QODY_ZZ.png) ## Citation If you find Ovis useful, please cite the paper ``` @article{lu2024ovis, title={Ovis: Structural Embedding Alignment for Multimodal Large Language Model}, author={Shiyin Lu and Yang Li and Qing-Guo Chen and Zhao Xu and Weihua Luo and Kaifu Zhang and Han-Jia Ye}, year={2024}, journal={arXiv:2405.20797} } ``` ## License This project is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt) (SPDX-License-Identifier: Apache-2.0). ## Disclaimer We used compliance-checking algorithms during the training process, to ensure the compliance of the trained model to the best of our ability. Due to the complexity of the data and the diversity of language model usage scenarios, we cannot guarantee that the model is completely free of copyright issues or improper content. If you believe anything infringes on your rights or generates improper content, please contact us, and we will promptly address the matter.