Qwen-VL 🤖 | 🤗 ｜ Qwen-VL-Chat 🤖 | 🤗 ｜ Qwen-VL-Chat-Int4 🤗
WeChat | Discord | Demo ｜ Paper | Colab
Qwen-VL (Qwen Large Vision Language Model) is the multimodal version of the large model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-VL accepts image, text, and bounding box as inputs, outputs text and bounding box. The features of Qwen-VL include:
- Strong performance: It significantly surpasses existing open-sourced Large Vision Language Models (LVLM) under similar model scale on multiple English evaluation benchmarks (including Zero-shot Captioning, VQA, DocVQA, and Grounding).
- Multi-lingual LVLM supporting text recognition: Qwen-VL naturally supports English, Chinese, and multi-lingual conversation, and it promotes end-to-end recognition of Chinese and English bi-lingual text in images.
- Multi-image interleaved conversations: This feature allows for the input and comparison of multiple images, as well as the ability to specify questions related to the images and engage in multi-image storytelling.
- First generalist model supporting grounding in Chinese: Detecting bounding boxes through open-domain language expression in both Chinese and English.
- Fine-grained recognition and understanding: Compared to the 224*224 resolution currently used by other open-sourced LVLM, the 448*448 resolution promotes fine-grained text recognition, document QA, and bounding box annotation.
We release two models of the Qwen-VL series:
- Qwen-VL: The pre-trained LVLM model uses Qwen-7B as the initialization of the LLM, and Openclip ViT-bigG as the initialization of the visual encoder. And connects them with a randomly initialized cross-attention layer.
- Qwen-VL-Chat: A multimodal LLM-based AI assistant, which is trained with alignment techniques. Qwen-VL-Chat supports more flexible interaction, such as multiple image inputs, multi-round question answering, and creative capabilities.
2023.9.8👍👍👍 Thanks to camenduru for contributing the wonderful Colab. Everyone can use it as a local or online Qwen-VL-Chat-Int4 Demo tutorial on one 12G GPU.2023.9.5👏👏👏 Qwen-VL-Chat achieves SOTAs on MME Benchmark, a comprehensive evaluation benchmark for multimodal large language models. It measures both perception and cognition abilities on a total of 14 subtasks.2023.9.4⭐⭐⭐ Qwen-VL series achieve SOTAs on Seed-Bench, a multimodal benchmark of 19K multiple-choice questions with accurate human annotations for evaluating Multimodal LLMs including both image and video understanding.2023.9.1🔥🔥🔥 We release the TouchStone Evaluation, which is a comprehensive assessment of multimodal language models, encompassing not only basic recognition and comprehension but also extending to literary creation. By using strong LLMs as judges and converting multimodal information into text.2023.8.31🌟🌟🌟 We release the Int4 quantized model for Qwen-VL-Chat, Qwen-VL-Chat-Int4, which requires low memory costs but achieves improved inference speed. Besides, there is no significant performance degradation on the benchmark evaluation.2023.8.22🎉🎉🎉 We release both Qwen-VL and Qwen-VL-Chat on ModelScope and Hugging Face. We also provide a paper for more details about the model, including training details and model performance.
We evaluated the model's abilities from three perspectives:
-
Standard Benchmarks: We evaluate the model's basic task capabilities on four major categories of multimodal tasks:
- Zero-shot Captioning: Evaluate model's zero-shot image captioning ability on unseen datasets;
- General VQA: Evaluate the general question-answering ability of pictures, such as the judgment, color, number, category, etc;
- Text-based VQA: Evaluate the model's ability to recognize text in pictures, such as document QA, chart QA, etc;
- Referring Expression Comprehension: Evaluate the ability to localize a target object in an image described by a referring expression.
-
TouchStone: To evaluate the overall text-image dialogue capability and alignment level with humans, we have constructed a benchmark called TouchStone, which is based on scoring with GPT4 to evaluate the LVLM model.
- The TouchStone benchmark covers a total of 300+ images, 800+ questions, and 27 categories. Such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc;
- In order to break the current limitation of GPT4 in terms of direct image input, TouchStone provides fine-grained image annotations by human labeling. These detailed annotations, along with the questions and the model's output, are then presented to GPT4 for scoring.
- The benchmark includes both English and Chinese versions.
-
Other Multimodal Benchmarks: We also evaluated our model's capabilities in other multimodal benchmarks:
- MME Benchmark, a comprehensive evaluation benchmark for multimodal large language models. Qwen-VL-Chat achieves SOTAs on both perception and cognition tracks.
- Seed-Bench, a multimodal benchmark of 19K multiple-choice questions with accurate human annotations for evaluating Multimodal LLMs. Qwen series achieves SOTAs on this benchmark.
The results of the evaluation are as follows:
Qwen-VL outperforms current SOTA generalist models on multiple VL tasks and has a more comprehensive coverage in terms of capability range.
| Model type | Model | Zero-shot Captioning | General VQA | |||||
|---|---|---|---|---|---|---|---|---|
| NoCaps | Flickr30K | VQAv2dev | OK-VQA | GQA | SciQA-Img (0-shot) |
VizWiz (0-shot) |
||
| Generalist Models |
Flamingo-9B | - | 61.5 | 51.8 | 44.7 | - | - | 28.8 |
| Flamingo-80B | - | 67.2 | 56.3 | 50.6 | - | - | 31.6 | |
| Unified-IO-XL | 100.0 | - | 77.9 | 54.0 | - | - | - | |
| Kosmos-1 | - | 67.1 | 51.0 | - | - | - | 29.2 | |
| Kosmos-2 | - | 80.5 | 51.1 | - | - | - | - | |
| BLIP-2 (Vicuna-13B) | 103.9 | 71.6 | 65.0 | 45.9 | 32.3 | 61.0 | 19.6 | |
| InstructBLIP (Vicuna-13B) | 121.9 | 82.8 | - | - | 49.5 | 63.1 | 33.4 | |
| Shikra (Vicuna-13B) | - | 73.9 | 77.36 | 47.16 | - | - | - | |
| Qwen-VL (Qwen-7B) | 121.4 | 85.8 | 78.8 | 58.6 | 59.3 | 67.1 | 35.2 | |
| Qwen-VL-Chat | 120.2 | 81.0 | 78.2 | 56.6 | 57.5 | 68.2 | 38.9 | |
| Previous SOTA (Per Task Fine-tuning) |
- | 127.0 (PALI-17B) |
84.5 (InstructBLIP -FlanT5-XL) |
86.1 (PALI-X -55B) |
66.1 (PALI-X -55B) |
72.1 (CFR) |
92.53 (LLaVa+ GPT-4) |
70.9 (PALI-X -55B) |
- For zero-shot image captioning, Qwen-VL achieves the SOTA on Flickr30K and competitive results on Nocaps with InstructBlip.
- For general VQA, Qwen-VL achieves the SOTA under the same generalist LVLM scale settings.
| Model type | Model | TextVQA | DocVQA | ChartQA | AI2D | OCR-VQA |
|---|---|---|---|---|---|---|
| Generalist Models | BLIP-2 (Vicuna-13B) | 42.4 | - | - | - | - |
| InstructBLIP (Vicuna-13B) | 50.7 | - | - | - | - | |
| mPLUG-DocOwl (LLaMA-7B) | 52.6 | 62.2 | 57.4 | - | - | |
| Pix2Struct-Large (1.3B) | - | 76.6 | 58.6 | 42.1 | 71.3 | |
| Qwen-VL (Qwen-7B) | 63.8 | 65.1 | 65.7 | 62.3 | 75.7 | |
| Specialist SOTAs (Specialist/Finetuned) |
PALI-X-55B (Single-task FT) (Without OCR Pipeline) |
71.44 | 80.0 | 70.0 | 81.2 | 75.0 |
- In text-related recognition/QA evaluation, Qwen-VL achieves the SOTA under the generalist LVLM scale settings.
- Resolution is important for several above evaluations. While most open-sourced LVLM models with 224 resolution are incapable of these evaluations or can only solve these by cutting images, Qwen-VL scales the resolution to 448 so that it can be evaluated end-to-end. Qwen-VL even outperforms Pix2Struct-Large models of 1024 resolution on some tasks.
| Model type | Model | RefCOCO | RefCOCO+ | RefCOCOg | GRIT | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| val | test-A | test-B | val | test-A | test-B | val-u | test-u | refexp | ||
| Generalist Models | GPV-2 | - | - | - | - | - | - | - | - | 51.50 |
| OFA-L* | 79.96 | 83.67 | 76.39 | 68.29 | 76.00 | 61.75 | 67.57 | 67.58 | 61.70 | |
| Unified-IO | - | - | - | - | - | - | - | - | 78.61 | |
| VisionLLM-H | 86.70 | - | - | - | - | - | - | - | ||
| Shikra-7B | 87.01 | 90.61 | 80.24 | 81.60 | 87.36 | 72.12 | 82.27 | 82.19 | 69.34 | |
| Shikra-13B | 87.83 | 91.11 | 81.81 | 82.89 | 87.79 | 74.41 | 82.64 | 83.16 | 69.03 | |
| Qwen-VL-7B | 89.36 | 92.26 | 85.34 | 83.12 | 88.25 | 77.21 | 85.58 | 85.48 | 78.22 | |
| Qwen-VL-7B-Chat | 88.55 | 92.27 | 84.51 | 82.82 | 88.59 | 76.79 | 85.96 | 86.32 | - | |
| Specialist SOTAs (Specialist/Finetuned) |
G-DINO-L | 90.56 | 93.19 | 88.24 | 82.75 | 88.95 | 75.92 | 86.13 | 87.02 | - |
| UNINEXT-H | 92.64 | 94.33 | 91.46 | 85.24 | 89.63 | 79.79 | 88.73 | 89.37 | - | |
| ONE-PEACE | 92.58 | 94.18 | 89.26 | 88.77 | 92.21 | 83.23 | 89.22 | 89.27 | - | |
- Qwen-VL achieves the SOTA in all above referring expression comprehension benchmarks.
- Qwen-VL has not been trained on any Chinese grounding data, but it can still generalize to the Chinese Grounding tasks in a zero-shot way by training Chinese Caption data and English Grounding data.
We provide all of the above evaluation scripts for reproducing our experimental results. Please read eval_mm/EVALUATION.md for more information.
TouchStone is a benchmark based on scoring with GPT4 to evaluate the abilities of the LVLM model on text-image dialogue and alignment levels with humans. It covers a total of 300+ images, 800+ questions, and 27 categories, such as attribute-based Q&A, celebrity recognition, writing poetry, summarizing multiple images, product comparison, math problem solving, etc. Please read touchstone/README.md for more information.
| Model | Score |
|---|---|
| PandaGPT | 488.5 |
| MiniGPT4 | 531.7 |
| InstructBLIP | 552.4 |
| LLaMA-AdapterV2 | 590.1 |
| LLaVA | 602.7 |
| mPLUG-Owl | 605.4 |
| Qwen-VL-Chat | 645.2 |
| Model | Score |
|---|---|
| VisualGLM | 247.1 |
| Qwen-VL-Chat | 401.2 |
Qwen-VL-Chat has achieved the best results in both Chinese and English alignment evaluation.
MME is a comprehensive evaluation benchmark for multimodal large language models. It measures both perception and cognition abilities on a total of 14 subtasks, including existence, count, position, color, poster, celebrity, scene, landmark, artwork, OCR, commonsense reasoning, numerical calculation, text translation, and code reasoning.
Qwen-VL-Chat achieves SOTAs on both perception and cognition evaluation. See more details on HERE.
SEED-Bench is a multimodal benchmark of 19K multiple-choice questions with accurate human annotations for evaluating Multimodal LLMs, covering 12 evaluation dimensions including both image and video understanding. See more details on HERE.
Qwen-VL and Qwen-VL-Chat achieve SOTAs on this benchmark.
- python 3.8 and above
- pytorch 1.12 and above, 2.0 and above are recommended
- CUDA 11.4 and above are recommended (this is for GPU users)
Below, we provide simple examples to show how to use Qwen-VL and Qwen-VL-Chat with 🤖 ModelScope and 🤗 Transformers.
Before running the code, make sure you have setup the environment and installed the required packages. Make sure you meet the above requirements, and then install the dependent libraries.
pip install -r requirements.txtNow you can start with ModelScope or Transformers. More usage aboue vision encoder, please refer to the tutorial.
To use Qwen-VL-Chat for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, please make sure that you are using the latest code.
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
import torch
torch.manual_seed(1234)
# Note: The default behavior now has injection attack prevention off.
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True)
# use bf16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval()
# use fp16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval()
# use cpu only
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="cpu", trust_remote_code=True).eval()
# use cuda device
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat", device_map="cuda", trust_remote_code=True).eval()
# Specify hyperparameters for generation
model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-VL-Chat", trust_remote_code=True)
# 1st dialogue turn
query = tokenizer.from_list_format([
{'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'}, # Either a local path or an url
{'text': '这是什么?'},
])
response, history = model.chat(tokenizer, query=query, history=None)
print(response)
# 图中是一名女子在沙滩上和狗玩耍，旁边是一只拉布拉多犬，它们处于沙滩上。
# 2nd dialogue turn
response, history = model.chat(tokenizer, '框出图中击掌的位置', history=history)
print(response)
# <ref>击掌</ref><box>(536,509),(588,602)</box>
image = tokenizer.draw_bbox_on_latest_picture(response, history)
if image:
image.save('1.jpg')
else:
print("no box")
Running Qwen-VL
Running Qwen-VL pretrained base model is also simple.
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
import torch
torch.manual_seed(1234)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)
# use bf16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, bf16=True).eval()
# use fp16
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="auto", trust_remote_code=True, fp16=True).eval()
# use cpu only
# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cpu", trust_remote_code=True).eval()
# use cuda device
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL", device_map="cuda", trust_remote_code=True).eval()
# Specify hyperparameters for generation (No need to do this if you are using transformers>4.32.0)
# model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-VL", trust_remote_code=True)
query = tokenizer.from_list_format([
{'image': 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'}, # Either a local path or an url
{'text': 'Generate the caption in English with grounding:'},
])
inputs = tokenizer(query, return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
response = tokenizer.decode(pred.cpu()[0], skip_special_tokens=False)
print(response)
# <img>https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg</img>Generate the caption in English with grounding:<ref> Woman</ref><box>(451,379),(731,806)</box> and<ref> her dog</ref><box>(219,424),(576,896)</box> playing on the beach<|endoftext|>
image = tokenizer.draw_bbox_on_latest_picture(response)
if image:
image.save('2.jpg')
else:
print("no box")
ModelScope is an opensource platform for Model-as-a-Service (MaaS), which provides flexible and cost-effective model service to AI developers. Similarly, you can run the models with ModelScope as shown below:
from modelscope import (
snapshot_download, AutoModelForCausalLM, AutoTokenizer, GenerationConfig
)
import torch
model_id = 'qwen/Qwen-VL-Chat'
revision = 'v1.0.0'
model_dir = snapshot_download(model_id, revision=revision)
torch.manual_seed(1234)
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
if not hasattr(tokenizer, 'model_dir'):
tokenizer.model_dir = model_dir
# use bf16
# model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, bf16=True).eval()
# use fp16
model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, fp16=True).eval()
# use cpu
# model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="cpu", trust_remote_code=True).eval()
# use auto
model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True).eval()
# Specify hyperparameters for generation (No need to do this if you are using transformers>=4.32.0)
# model.generation_config = GenerationConfig.from_pretrained(model_dir, trust_remote_code=True)
# 1st dialogue turn
# Either a local path or an url between <img></img> tags.
image_path = 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'
response, history = model.chat(tokenizer, query=f'<img>{image_path}</img>这是什么', history=None)
print(response)
# 图中是一名年轻女子在沙滩上和她的狗玩耍，狗的品种是拉布拉多。她们坐在沙滩上，狗的前腿抬起来，与人互动。
# 2nd dialogue turn
response, history = model.chat(tokenizer, '输出击掌的检测框', history=history)
print(response)
# <ref>"击掌"</ref><box>(211,412),(577,891)</box>
image = tokenizer.draw_bbox_on_latest_picture(response, history)
if image:
image.save('output_chat.jpg')
else:
print("no box")
We provide a new solution based on AutoGPTQ, and release an Int4 quantized model for Qwen-VL-Chat, Qwen-VL-Chat-Int4 Click here, which achieves nearly lossless model effects but improved performance on both memory costs and inference speed.
Here we demonstrate how to use our provided quantized models for inference. Before you start, make sure you meet the requirements (e.g., torch 2.0 and above, transformers 4.32.0 and above, etc.) and install the required packages:
pip install optimum
git clone https://github.com/JustinLin610/AutoGPTQ.git & cd AutoGPTQ
pip install -v .If you meet problems installing auto-gptq, we advise you to check out the official repo to find a wheel.
Then you can load the quantized model easily and run inference as same as usual:
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen-VL-Chat-Int4",
device_map="auto",
trust_remote_code=True
).eval()
# Either a local path or an url between <img></img> tags.
image_path = 'https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg'
response, history = model.chat(tokenizer, query=f'<img>{image_path}</img>这是什么', history=None)
print(response)We illustrate the model performance of both BF16 and Int4 models on the benchmark TouchStone, and we find that the quantized model does not suffer from significant performance degradation. Results are shown below:
| Quantization | ZH | EN |
|---|---|---|
| BF16 | 401.2 | 645.2 |
| Int4 | 386.6 | 651.4 |
We measured the average inference speed (tokens/s) of generating 1792 (2048-258) and 7934 (8192-258) tokens with the context of an image (which takes 258 tokens) under BF16 precision and Int4 quantization, respectively.
| Quantization | Speed (2048 tokens) | Speed (8192 tokens) |
|---|---|---|
| BF16 | 28.87 | 24.32 |
| Int4 | 37.79 | 34.34 |
The profiling runs on a single A100-SXM4-80G GPU with PyTorch 2.0.1 and CUDA 11.4.
We also profile the peak GPU memory usage for encoding 1792 (2048-258) tokens (including an image) as context (and generating single token) and generating 7934 (8192-258) tokens (with an image as context) under BF16 or Int4 quantization level, respectively. The results are shown below.
| Quantization | Peak Usage for Encoding 2048 Tokens | Peak Usage for Generating 8192 Tokens |
|---|---|---|
| BF16 | 22.60GB | 28.01GB |
| Int4 | 11.82GB | 17.23GB |
The above speed and memory profiling are conducted using this script.
We provide code for users to build a web UI demo. Before you start, make sure you install the following packages:
pip install -r requirements_web_demo.txt
Then run the command below and click on the generated link:
python web_demo_mm.py
If you meet problems, please refer to FAQ and the issues first to search a solution before you launch a new issue.
Researchers and developers are free to use the codes and model weights of both Qwen-VL and Qwen-VL-Chat. We also allow their commercial use. Check our license at LICENSE for more details.
If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝 :)
@article{Qwen-VL,
title={Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities},
author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
journal={arXiv preprint arXiv:2308.12966},
year={2023}
}If you are interested to leave a message to either our research team or product team, feel free to send an email to qianwen_opensource@alibabacloud.com.