AI prompts
base on Mixture-of-Experts for Large Vision-Language Models <p align="center">
<img src="https://s11.ax1x.com/2023/12/28/piqvDMV.png" width="250" style="margin-bottom: 0.2;"/>
<p>
<h2 align="center"> <a href="https://arxiv.org/abs/2401.15947">MoE-LLaVA: Mixture of Experts for Large Vision-Language Models</a></h2>
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2>
<h5 align="center">
[![hf_space](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/LanguageBind/MoE-LLaVA)
[![Replicate demo and cloud API](https://replicate.com/camenduru/moe-llava/badge)](https://replicate.com/camenduru/moe-llava)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/camenduru/MoE-LLaVA-jupyter/blob/main/MoE_LLaVA_jupyter.ipynb)
[![hf_space](https://img.shields.io/badge/🤗-Paper%20In%20HF-red.svg)](https://huggingface.co/papers/2401.15947)
[![arXiv](https://img.shields.io/badge/Arxiv-2401.15947-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2401.15947)
[![youtube](https://img.shields.io/badge/-YouTube-000000?logo=youtube&logoColor=FF0000)](https://www.youtube.com/watch?v=uYb38g-weEY)
[![jiqizhixin](https://img.shields.io/badge/-WeChat@机器之心-000000?logo=wechat&logoColor=07C160)](https://mp.weixin.qq.com/s/ICylR6n2LhqQRS0CAHFI1A)
[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://github.com/PKU-YuanGroup/MoE-LLaVA/blob/main/LICENSE)
[![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FPKU-YuanGroup%2FMoE-LLaVA&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Visitor&edge_flat=false)](https://hits.seeyoufarm.com)
[![GitHub issues](https://img.shields.io/github/issues/PKU-YuanGroup/MoE-LLaVA?color=critical&label=Issues)](https://github.com/PKU-YuanGroup/MoE-LLaVA/issues?q=is%3Aopen+is%3Aissue)
[![GitHub closed issues](https://img.shields.io/github/issues-closed/PKU-YuanGroup/MoE-LLaVA?color=success&label=Issues)](https://github.com/PKU-YuanGroup/MoE-LLaVA/issues?q=is%3Aissue+is%3Aclosed) <br>
</h5>
<details open><summary>💡 I also have other vision-language projects that may interest you ✨. </summary><p>
<!-- may -->
> [**Open-Sora Plan: Open-Source Large Video Generation Model**](https://arxiv.org/abs/2412.00131) <br>
> Bin Lin and Yunyang Ge and Xinhua Cheng and Zongjian Li and Bin Zhu and Shaodong Wang and Xianyi He and Yang Ye and Shenghai Yuan and Liuhan Chen and Tanghui Jia and Junwu Zhang and Zhenyu Tang and Yatian Pang and Bin She and Cen Yan and Zhiheng Hu and Xiaoyi Dong and Lin Chen and Zhang Pan and Xing Zhou and Shaoling Dong and Yonghong Tian and Li Yuan <br>
[![github](https://img.shields.io/badge/-Github-black?logo=github)](https://github.com/PKU-YuanGroup/Open-Sora-Plan) [![github](https://img.shields.io/github/stars/PKU-YuanGroup/Open-Sora-Plan.svg?style=social)](https://github.com/PKU-YuanGroup/Open-Sora-Plan) [![arXiv](https://img.shields.io/badge/Arxiv-2412.00131-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2412.00131) <br>
> [**Video-LLaVA: Learning United Visual Representation by Alignment Before Projection**](https://arxiv.org/abs/2311.10122) <br>
> Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, Li Yuan <br>
[![github](https://img.shields.io/badge/-Github-black?logo=github)](https://github.com/PKU-YuanGroup/Video-LLaVA) [![github](https://img.shields.io/github/stars/PKU-YuanGroup/Video-LLaVA.svg?style=social)](https://github.com/PKU-YuanGroup/Video-LLaVA) [![arXiv](https://img.shields.io/badge/Arxiv-2311.10122-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2311.10122) <br>
> [**LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment**](https://arxiv.org/abs/2310.01852) <br>
> Bin Zhu, Bin Lin, Munan Ning, Yang Yan, Jiaxi Cui, HongFa Wang, Yatian Pang, Wenhao Jiang, Junwu Zhang, Zongwei Li, Wancai Zhang, Zhifeng Li, Wei Liu, Li Yuan <br>
[![github](https://img.shields.io/badge/-Github-black?logo=github)](https://github.com/PKU-YuanGroup/LanguageBind) [![github](https://img.shields.io/github/stars/PKU-YuanGroup/LanguageBind.svg?style=social)](https://github.com/PKU-YuanGroup/LanguageBind) [![arXiv](https://img.shields.io/badge/Arxiv-2310.01852-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2310.01852) <br>
</p></details>
## 📣 News
* **[2024.03.16]** 🎉 We release all stage2 models, cheching our [model zoo](#-model-zoo).
* **[2024.02.03]** 🎉 We release a stronger [MoE-LLaVA-StableLM](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-1.8B-4e-384). The average performance is close to LLaVA-1.5-7B by using **2.0B** sparse activated parameters, checking our [model zoo](#-model-zoo).
* **[2024.02.02]** 🤝 Enjoying the [![Replicate demo and cloud API](https://replicate.com/camenduru/moe-llava/badge)](https://replicate.com/camenduru/moe-llava) and [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/camenduru/MoE-LLaVA-jupyter/blob/main/MoE_LLaVA_jupyter.ipynb), created by [@camenduru](https://github.com/camenduru), who generously supports our research!
* **[2024.02.01]** 🔥 People who cannot access HF can now download the model through the <img src="https://github.com/PKU-YuanGroup/MoE-LLaVA/raw/main/assets/modelscope_logo.png" width="20px" style="max-width: 100%;"> model scope, checking our [model zoo](#-model-zoo).
* **[2024.01.30]** 🔥 We release a stronger [MoE-LLaVA-Phi2](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-2.7B-4e-384). The average performance **surpasses LLaVA-1.5-7B by using 3.6B** sparse activated parameters, checking our [model zoo](#-model-zoo).
* **[2024.01.27]** 🤗 [Hugging Face demo](https://huggingface.co/spaces/LanguageBind/MoE-LLaVA) and **all codes & datasets** are available now! Welcome to **watch** 👀 this repository for the latest updates.
## 😮 Highlights
MoE-LLaVA shows excellent performance in multi-modal learning.
### 🔥 High performance, but with fewer parameters
- with just **3B sparsely activated parameters**, MoE-LLaVA demonstrates performance comparable to the LLaVA-1.5-7B on various visual understanding datasets and even surpasses the LLaVA-1.5-13B in object hallucination benchmarks.
<p align="center">
<img src="assets/intro0.jpg" width=55%>
</p>
### 🚀 Simple baseline, learning multi-modal interactions with sparse pathways.
- With the addition of **a simple MoE tuning stage**, we can complete the training of MoE-LLaVA on **8 A100 GPUs** within 1 days.
<p align="center">
<img src="assets/intro.jpg" width=65%>
</p>
## 🤗 Demo
### Gradio Web UI <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a>
Highly recommend trying out our web demo by the following command, which incorporates all features currently supported by MoE-LLaVA. We also provide [online demo](https://huggingface.co/spaces/LanguageBind/MoE-LLaVA) in Huggingface Spaces.
```bash
# use phi2
deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-Phi2-2.7B-4e"
# use qwen
deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-Qwen-1.8B-4e"
# use stablelm
deepspeed --include localhost:0 moellava/serve/gradio_web_server.py --model-path "LanguageBind/MoE-LLaVA-StableLM-1.6B-4e"
```
https://github.com/PKU-YuanGroup/MoE-LLaVA/assets/62638829/8541aac6-9ef6-4fde-aa94-80d0375b9bdb
### CLI Inference
```bash
# use phi2
deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-Phi2-2.7B-4e" --image-file "image.jpg"
# use qwen
deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-Qwen-1.8B-4e" --image-file "image.jpg"
# use stablelm
deepspeed --include localhost:0 moellava/serve/cli.py --model-path "LanguageBind/MoE-LLaVA-StableLM-1.6B-4e" --image-file "image.jpg"
```
<img src="assets/imagecli.gif" />
## 🐳 Model Zoo
| Model | Activated Param | Transformers(HF) | ModelScope(HF) | Avg | VQAv2 | GQA | VizWiz | SQA-IMG | T-VQA | POPE | MME | MM-Bench | MM-Vet |
|----------|-----------|-----------|---|---|---|---|---|---|---|---|---|---|---|
| MoE-LLaVA-1.6B×4-Top2 | 2.0B | [🤗LanguageBind/MoE-LLaVA-StableLM-1.6B-4e](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-1.6B-4e) | [<img src="https://github.com/PKU-YuanGroup/MoE-LLaVA/raw/main/assets/modelscope_logo.png" width="20px" style="max-width: 100%;">PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e](https://modelscope.cn/models/PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e) | 57.3 | 76.7 | 60.3 | 36.2 | 62.6 | 50.1 | 85.7 | 1318.1 | 60.2 | 26.9 |
| MoE-LLaVA-1.8B×4-Top2 | 2.2B | [🤗LanguageBind/MoE-LLaVA-Qwen-1.8B-4e](https://huggingface.co/LanguageBind/MoE-LLaVA-Qwen-1.8B-4e) | [<img src="https://github.com/PKU-YuanGroup/MoE-LLaVA/raw/main/assets/modelscope_logo.png" width="20px" style="max-width: 100%;">PKU-YuanLab/MoE-LLaVA-Qwen-1.8B-4e](https://modelscope.cn/models/PKU-YuanLab/MoE-LLaVA-Qwen-1.8B-4e) | 56.7 | 76.2 | 61.5 | 32.6 | 63.1 | 48.0 | 87.0 | 1291.6 | 59.6 | 25.3 |
| MoE-LLaVA-2.7B×4-Top2 | 3.6B | [🤗LanguageBind/MoE-LLaVA-Phi2-2.7B-4e](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-2.7B-4e) | [<img src="https://github.com/PKU-YuanGroup/MoE-LLaVA/raw/main/assets/modelscope_logo.png" width="20px" style="max-width: 100%;">PKU-YuanLab/MoE-LLaVA-Phi2-2.7B-4e](https://modelscope.cn/models/PKU-YuanLab/MoE-LLaVA-Phi2-2.7B-4e) | 61.1 | 77.6 | 61.4 | 43.9 | 68.5 | 51.4 | 86.3 | 1423.0 | 65.2 | 34.3 |
| MoE-LLaVA-1.6B×4-Top2-384 | 2.0B | [🤗LanguageBind/MoE-LLaVA-StableLM-1.6B-4e-384](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-1.6B-4e-384) | [<img src="https://github.com/PKU-YuanGroup/MoE-LLaVA/raw/main/assets/modelscope_logo.png" width="20px" style="max-width: 100%;">PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e-384](https://modelscope.cn/models/PKU-YuanLab/MoE-LLaVA-StableLM-1.6B-4e-384) | 60.0 | 78.6 | 61.5 | 40.5 | 63.9 | 54.3 | 85.9 | 1335.7 | 63.3 | 32.3 |
| MoE-LLaVA-2.7B×4-Top2-384 | 3.6B | [🤗LanguageBind/MoE-LLaVA-Phi2-2.7B-4e-384](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-2.7B-4e-384) | [<img src="https://github.com/PKU-YuanGroup/MoE-LLaVA/raw/main/assets/modelscope_logo.png" width="20px" style="max-width: 100%;">PKU-YuanLab/MoE-LLaVA-Phi2-2.7B-4e-384](https://modelscope.cn/models/PKU-YuanLab/MoE-LLaVA-Phi2-2.7B-4e-384) | **62.9** | 79.9 | 62.6 | 43.7 | 70.3 | 57.0 | 85.7 | 1431.3 | 68.0 | 35.9 |
| LLaVA-1.5 | 7B | [🤗liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) | - | 62.0 | 78.5 | 62.0 | 50.0 | 66.8 | 58.2 | 85.9 | 1510.7 | 64.3 | 30.5 |
<!--
| LLaVA-1.5 | 13B | [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) | 64.9 | 80.0 | 63.3 | 53.6 | 71.6 | 61.3 | 85.9 | 1531.3 | 67.7 | 35.4 |
-->
<details>
🚨 **Please know https://github.com/PKU-YuanGroup/MoE-LLaVA/issues/27.**
<summary>Stage2 Model</summary>
| Model | Checkpoint |
|----------|-----------|
| MoE-LLaVA-1.6B×4-Top2 | [LanguageBind/MoE-LLaVA-StableLM-Stage2](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-Stage2) |
| MoE-LLaVA-1.6B×4-Top2-384 | [LanguageBind/MoE-LLaVA-StableLM-Stage2-384](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-Stage2-384) |
| MoE-LLaVA-1.8B×4-Top2 | [LanguageBind/MoE-LLaVA-Qwen-Stage2](https://huggingface.co/LanguageBind/MoE-LLaVA-Qwen-Stage2) |
| MoE-LLaVA-2.7B×4-Top2 | [LanguageBind/MoE-LLaVA-Phi2-Stage2](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-Stage2) |
| MoE-LLaVA-2.7B×4-Top2-384 | [LanguageBind/MoE-LLaVA-Phi2-Stage2-384](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-Stage2-384) |
</details>
<details>
<summary>Pretrain Model</summary>
| Model | Checkpoint |
|----------|-----------|
| MoE-LLaVA-1.6B×4-Top2 | [LanguageBind/MoE-LLaVA-StableLM-Pretrain](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-Pretrain) |
| MoE-LLaVA-1.6B×4-Top2-384 | [LanguageBind/MoE-LLaVA-StableLM-384-Pretrain](https://huggingface.co/LanguageBind/MoE-LLaVA-StableLM-384-Pretrain) |
| MoE-LLaVA-1.8B×4-Top2 | [LanguageBind/MoE-LLaVA-Qwen-Pretrain](https://huggingface.co/LanguageBind/MoE-LLaVA-Qwen-Pretrain) |
| MoE-LLaVA-2.7B×4-Top2 | [LanguageBind/MoE-LLaVA-Phi2-Pretrain](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-Pretrain) |
| MoE-LLaVA-2.7B×4-Top2-384 | [LanguageBind/MoE-LLaVA-Phi2-384-Pretrain](https://huggingface.co/LanguageBind/MoE-LLaVA-Phi2-384-Pretrain) |
</details>
## ⚙️ Requirements and Installation
We recommend the requirements as follows.
* Python == 3.10
* Pytorch == 2.0.1
* CUDA Version >= 11.7
* **Transformers == 4.37.0**
* **Tokenizers==0.15.1**
* Install required packages:
```bash
git clone https://github.com/PKU-YuanGroup/MoE-LLaVA
cd MoE-LLaVA
conda create -n moellava python=3.10 -y
conda activate moellava
pip install --upgrade pip # enable PEP 660 support
pip install -e .
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
# Below are optional. For Qwen model.
git clone https://github.com/Dao-AILab/flash-attention
cd flash-attention && pip install .
# Below are optional. Installing them might be slow.
# pip install csrc/layer_norm
# If the version of flash-attn is higher than 2.1.1, the following is not needed.
# pip install csrc/rotary
```
> [!Warning]
> <div align="left">
> <b>
> 🚨 We find that using flash attention2 makes performance degradation.
> </b>
> </div>
## 🗝️ Training & Validating
The training & validating instruction is in [TRAIN.md](docs/TRAIN.md) & [EVAL.md](docs/EVAL.md).
## 💡 Customizing your MoE-LLaVA
The instruction is in [CUSTOM.md](docs/CUSTOM.md).
## 😍 Visualization
The instruction is in [VISUALIZATION.md](docs/VISUALIZATION.md).
## 🤖 API
**We open source all codes.** If you want to load the model (e.g. ```LanguageBind/MoE-LLaVA-Phi2-2.7B-4e```) on local, you can use the following code snippets.
**Using the following command to run the code.**
```bash
deepspeed --include localhost:0 predict.py
```
```python
import torch
from PIL import Image
from moellava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from moellava.conversation import conv_templates, SeparatorStyle
from moellava.model.builder import load_pretrained_model
from moellava.utils import disable_torch_init
from moellava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
def main():
disable_torch_init()
image = 'moellava/serve/examples/extreme_ironing.jpg'
inp = 'What is unusual about this image?'
model_path = 'LanguageBind/MoE-LLaVA-Phi2-2.7B-4e' # LanguageBind/MoE-LLaVA-Qwen-1.8B-4e or LanguageBind/MoE-LLaVA-StableLM-1.6B-4e
device = 'cuda'
load_4bit, load_8bit = False, False # FIXME: Deepspeed support 4bit or 8bit?
model_name = get_model_name_from_path(model_path)
tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device)
image_processor = processor['image']
conv_mode = "phi" # qwen or stablelm
conv = conv_templates[conv_mode].copy()
roles = conv.roles
image_tensor = image_processor.preprocess(Image.open(image).convert('RGB'), return_tensors='pt')['pixel_values'].to(model.device, dtype=torch.float16)
print(f"{roles[1]}: {inp}")
inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=True,
temperature=0.2,
max_new_tokens=1024,
use_cache=True,
stopping_criteria=[stopping_criteria])
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:], skip_special_tokens=True).strip()
print(outputs)
if __name__ == '__main__':
main()
```
## 🙌 Related Projects
* [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) This framework empowers the model to efficiently utilize the united visual tokens.
* [LanguageBind](https://github.com/PKU-YuanGroup/LanguageBind) An open source five modalities language-based retrieval framework.
## 👍 Acknowledgement
* [LLaVA](https://github.com/haotian-liu/LLaVA) The codebase we built upon and it is an efficient large language and vision assistant.
## 🔒 License
* The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](https://github.com/PKU-YuanGroup/MoE-LLaVA/blob/main/LICENSE) file.
* The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
## ✏️ Citation
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
```BibTeX
@article{lin2024moe,
title={MoE-LLaVA: Mixture of Experts for Large Vision-Language Models},
author={Lin, Bin and Tang, Zhenyu and Ye, Yang and Cui, Jiaxi and Zhu, Bin and Jin, Peng and Zhang, Junwu and Ning, Munan and Yuan, Li},
journal={arXiv preprint arXiv:2401.15947},
year={2024}
}
```
```BibTeX
@article{lin2023video,
title={Video-LLaVA: Learning United Visual Representation by Alignment Before Projection},
author={Lin, Bin and Zhu, Bin and Ye, Yang and Ning, Munan and Jin, Peng and Yuan, Li},
journal={arXiv preprint arXiv:2311.10122},
year={2023}
}
```
## ✨ Star History
[![Star History](https://api.star-history.com/svg?repos=PKU-YuanGroup/MoE-LLaVA&type=Date)](https://star-history.com/#PKU-YuanGroup/MoE-LLaVA&Date)
## 🤝 Contributors
<a href="https://github.com/PKU-YuanGroup/MoE-LLaVA/graphs/contributors">
<img src="https://contrib.rocks/image?repo=PKU-YuanGroup/MoE-LLaVA" />
</a>
", Assign "at most 3 tags" to the expected json: {"id":"7401","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"