license: mit
datasets:
- laion/laion2B-en
- laion/laion-coco
- laion/laion2B-multi
- kakaobrain/coyo-700m
- conceptual_captions
- wanng/wukong100m
pipeline_tag: visual-question-answering
Model Card for InternVL-Chat-V1-1
[๐ Blog] [๐ InternVL 1.0 Paper] [๐ InternVL 1.5 Report] [๐จ๏ธ Chat Demo]
[๐ค HF Demo] [๐ Quick Start] [๐ Community-hosted API] [๐ ไธญๆ่งฃ่ฏป]
We released InternVL-Chat-V1-1, featuring a structure similar to LLaVA, including a ViT, an MLP projector, and an LLM. In this version, we explored increasing the resolution to 448x448, enhancing OCR capabilities, and improving support for Chinese conversations.
Model Details
Model Type: multimodal large language model (MLLM)
Model Stats:
- Architecture: InternViT-6B-448px + MLP + LLaMA2-13B (Our internal SFT versions)
- Image size: 448 x 448 (256 tokens)
- Params: 19B
Training Strategy:
- Pretraining Stage
- Learnable Component: InternViT-6B + LLaMA2-13B
- Data: Trained on 72M samples, including COYO, LAION, CC12M, CC3M, SBU, Wukong, GRIT, Objects365, OpenImages, and OCR-related datasets.
- Note: In this stage, we load the pretrained weights of the original InternViT-6B-224px and interpolate its position embedding to the size corresponding to 448 x 448 pixels. Moreover, in order to reduce the number of visual tokens, we use a pixel shuffle operation to reduce 1024 tokens to 256 tokens.
- Supervised Finetuning Stage
- Learnable Component: MLP + LLaMA2-13B
- Data: A comprehensive collection of open-source datasets, along with their Chinese translation versions, totaling approximately 6M samples.
- Pretraining Stage
Released Models
Vision Foundation model
| Model | Date | Download | Note |
|---|---|---|---|
| InternViT-6B-448px-V1-5 | 2024.04.20 | ๐ค HF link | support dynamic resolution, super strong OCR (๐ฅnew) |
| InternViT-6B-448px-V1-2 | 2024.02.11 | ๐ค HF link | 448 resolution |
| InternViT-6B-448px-V1-0 | 2024.01.30 | ๐ค HF link | 448 resolution |
| InternViT-6B-224px | 2023.12.22 | ๐ค HF link | vision foundation model |
| InternVL-14B-224px | 2023.12.22 | ๐ค HF link | vision-language foundation model |
Multimodal Large Language Model (MLLM)
| Model | Date | Download | Note |
|---|---|---|---|
| InternVL-Chat-V1-5 | 2024.04.18 | ๐ค HF link | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (๐ฅnew) |
| InternVL-Chat-V1-2-Plus | 2024.02.21 | ๐ค HF link | more SFT data and stronger |
| InternVL-Chat-V1-2 | 2024.02.11 | ๐ค HF link | scaling up LLM to 34B |
| InternVL-Chat-V1-1 | 2024.01.24 | ๐ค HF link | support Chinese and stronger OCR |
Model Usage
We provide an example code to run InternVL-Chat-V1-1 using transformers.
You also can use our online demo for a quick experience of this model.
import torch
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor
from transformers import AutoTokenizer
path = "OpenGVLab/InternVL-Chat-V1-1"
# If your GPU has more than 40G memory, you can put the entire model on a single GPU.
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True).eval().cuda()
# Otherwise, you need to set device_map='auto' to use multiple GPUs for inference.
# model = AutoModel.from_pretrained(
# path,
# torch_dtype=torch.bfloat16,
# low_cpu_mem_usage=True,
# trust_remote_code=True,
# device_map='auto').eval()
tokenizer = AutoTokenizer.from_pretrained(path)
image = Image.open('./examples/image2.jpg').convert('RGB')
image = image.resize((448, 448))
image_processor = CLIPImageProcessor.from_pretrained(path)
pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
pixel_values = pixel_values.to(torch.bfloat16).cuda()
generation_config = dict(
num_beams=1,
max_new_tokens=512,
do_sample=False,
)
# single-round conversation
question = "่ฏท่ฏฆ็ปๆ่ฟฐๅพ็"
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(question, response)
# multi-round conversation
question = "่ฏท่ฏฆ็ปๆ่ฟฐๅพ็"
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
print(question, response)
question = "่ฏทๆ นๆฎๅพ็ๅไธ้ฆ่ฏ"
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
print(question, response)
Examples
In this update, InternVL-Chat has improved support for Chinese and OCR.
As you can see, although the Lynyrd Skynyrd in the image has some letters that are out of the camera's lens, and TOUR's T is blocked, the model is still able to recognize it correctly.
This model can also conduct an in-depth analysis of AAAI's official website and identify important information on the web page.
Evaluation
See here for detailed evaluation results.
Citation
If you find this project useful in your research, please consider citing:
@article{chen2023internvl,
title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
journal={arXiv preprint arXiv:2312.14238},
year={2023}
}
License
This project is released under the MIT license. Parts of this project contain code and models (e.g., LLaMA2) from other sources, which are subject to their respective licenses.
Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
Acknowledgement
InternVL is built with reference to the code of the following projects: OpenAI CLIP, Open CLIP, CLIP Benchmark, EVA, InternImage, ViT-Adapter, MMSegmentation, Transformers, DINOv2, BLIP-2, Qwen-VL, and LLaVA-1.5. Thanks for their awesome work!