Improve model card: Add paper/code/demo links, sample usage, update title & citations (#1)

- Improve model card: Add paper/code/demo links, sample usage, update title & citations (31c1d620f70613996cb9f15f7c71fbd52cb517dd)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,16 +1,41 @@
 ---
-license: apache-2.0
-datasets:
-- lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M
 base_model:
 - DeepGlint-AI/rice-vit-large-patch14-560
 - Qwen/Qwen3-4B-Instruct-2507
-pipeline_tag: image-text-to-text
+datasets:
+- lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M
 library_name: transformers
+license: apache-2.0
+pipeline_tag: image-text-to-text
+language: en
 ---
-# LLaVA-OneVision-1.5: Fully Open-Source State-of-the-Art VLM Model
 
-**LLaVA-OneVision1.5** introduces a novel family of **fully open-source** Large Multimodal Models (LMMs) that achieves **state-of-the-art performance**  with substantially **lower cost** through training on **native resolution** images.
+<p align="center">
+  <picture>
+    <source media="(prefers-color-scheme: dark)" srcset="https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/raw/main/asset/llava_onevision_black.png">
+    <source media="(prefers-color-scheme: light)" srcset="https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/raw/main/asset/llava_onevision_white.png">
+    <img alt="LLaVA-OneVision 1.5" src="https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/raw/main/asset/llava_onevision_white.png" width="600" style="max-width: 100%;">
+  </picture>
+</p>
+
+# LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
+
+**LLaVA-OneVision1.5** introduces a novel family of **fully open-source** Large Multimodal Models (LMMs) that achieves **state-of-the-art performance** with substantially **lower cost** through training on **native resolution** images.
+
+**Paper**: [LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training](https://huggingface.co/papers/2509.23661)
+
+**Code**: [https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5)
+
+**Demo**: [https://huggingface.co/spaces/lmms-lab/LLaVA-OneVision-1.5](https://huggingface.co/spaces/lmms-lab/LLaVA-OneVision-1.5)
+
+---
+
+## NEWS
+- 2025-09-30: Released a comprehensive [Offline Data Pack documentation](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/tree/main/examples_offline_packing).
+- 2025-09-30: Released the LLaVA-OneVision-1.5 [Technical Report](https://arxiv.org/abs/2509.23661).
+
+## Introduction
+**LLaVA-OneVision1.5** introduces a novel family of **fully open-source** Large Multimodal Models (LMMs) that achieves **state-of-the-art performance** with substantially **lower cost** through training on **native resolution** images.
 
 - **Superior Performance**
 A family of fully open-source large multimodal models demonstrating 
@@ -18,13 +43,12 @@ A family of fully open-source large multimodal models demonstrating
     - outperforming **Qwen2.5-VL** in most evaluation tasks.
 
 - **High-Quality Data at Scale**
-Meticulously curated **pre-training and SFT data** with rigorous filtering and quality control, achieving **superior data efficiency** with only **64B tokens**.
+Meticulously curated **pre-training and SFT data** with rigorous filtering and quality control.
     - Concept-balanced, highly diverse, high-quality caption data
     - Comprehensive instruction fine-tuning data covering a wide range of tasks
 
 - **Ultra-Efficient Training Framework** Complete end-to-end training framework designed for maximum efficiency:
     - $16000 total budget for full model training on A100 GPUs  ($0.6 per GPU/Hour)
-    - 45% HFU efficiency in 8k context length
     - Built on **MegatronLM** with support for **MoE**, **FP8**, and **long sequence parallelization**
     - Optimized codebase for cost-effective scaling
 
@@ -35,18 +59,375 @@ Meticulously curated **pre-training and SFT data** with rigorous filtering and q
     - Training recipes & configurations
     - Comprehensive training logs & metrics
 
+
+## Models
+
+| Model                    | HF Link                                                                                      | Training Log |
+|--------------------------|--------------------------------------------------------------------------------------------------------|-------------|
+| LLaVA-OV-1.5-4B-Instruct | [🤗 HF / 4B-Instruct](https://huggingface.co/lmms-lab/LLaVA-OneVision-1.5-4B-Instruct)                | [📈 Tensorboard](https://huggingface.co/lmms-lab/LLaVA-OneVision-1.5-4B-Instruct/tensorboard) |
+| LLaVA-OV-1.5-8B-Instruct | [🤗 HF / 8B-Instruct](https://huggingface.co/lmms-lab/LLaVA-OneVision-1.5-8B-Instruct)                | [📈 Tensorboard](https://huggingface.co/lmms-lab/LLaVA-OneVision-1.5-8B-Instruct/tensorboard) |
+| LLaVA-OV-1.5-4B-Base     | [🤗 HF / 4B-Base](https://huggingface.co/lmms-lab/LLaVA-OneVision-1.5-4B-Base)                        | [📈 Tensorboard](https://huggingface.co/lmms-lab/LLaVA-OneVision-1.5-8B-Instruct/tensorboard) |
+| LLaVA-OV-1.5-8B-Base     | [🤗 HF / 8B-Base](https://huggingface.co/lmms-lab/LLaVA-OneVision-1.5-8B-Base)                        | Uploading… |
+## Datasets
+
+![Dataset Visualization](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/raw/main/asset/dataset.jpg)
+<p align="left">
+  <strong>(a)</strong> The vocabulary coverage proportion in the LLaVA-OneVision-1.5 Mid-Training dataset before and after concept balancing.
+  <strong>(b)</strong> Distribution of data sources within the LLaVA-OneVision-1.5 Mid-Training dataset.
+  <strong>(c)</strong> Distribution of data sources within the LLaVA-OneVision-1.5 Insturct dataset.
+</p>
+
+| Description        | Link                                                                                                   | Status      |
+|--------------------|--------------------------------------------------------------------------------------------------------|-------------|
+| LLaVA-OV-1.5-Mid-Training-85M   | [🤗HF / Mid-Training 85M](https://huggingface.co/datasets/lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M) | Uploading…  |
+| LLaVA-OV-1.5-Instruct           | [🤗HF / Insturct-Data](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-1.5-Insturct-Data)        | Uploading…  |
+
+
+## Evaluation Results
+
+
+All evaluations were conducted using lmms_eval.
+
+![](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/raw/main/asset/performance.png)
+
+
+## Quick Start with HuggingFace
+
+```python
+from transformers import AutoTokenizer, AutoProcessor, AutoModelForCausalLM
+from qwen_vl_utils import process_vision_info
+model_path = "lmms-lab/LLaVA-OneVision-1.5-8B-Instruct"
+
+# default: Load the model on the available device(s)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path, torch_dtype="auto", device_map="auto", trust_remote_code=True
+)
+
+# default processer
+processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=1024)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+
+```
+
+## Evaluation
+```
+# pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git  
+
+accelerate launch --num_processes=8 --main_process_port 12399 -m lmms_eval \
+    --model=llava_onevision1_5 \
+    --model_args=pretrained=lmms-lab/LLaVA-OneVision-1.5-8B-Instruct,attn_implementation=flash_attention_2,max_pixels=3240000 \
+    --tasks=mmmu_val,mmmu_pro_standard,mmbench_en_test,mmerealworld,mmerealworld_cn,ai2d,ai2d_no_mask,vstar_bench,chartqa,charxiv,docvqa_test,mathvista_testmini,mmstar,scienceqa \
+    --batch_size=1
+```
+
+## Quick Start Guide
+
+### 1.🐳 Docker (Recommended)
+
+We strongly recommend using the docker environment for a seamless experience. The following instructions are tailored for the A100 80GB GPU environment.
+
+
+```bash
+# Clone repository
+git clone https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5
+cd LLaVA-OneVision-1.5
+
+docker build -t llava_megatron:25.04 .
+
+# Run container with -w to set working directory directly to the mounted volume
+docker run -it --gpus all \
+    --ipc host --net host --privileged --cap-add IPC_LOCK \
+    --ulimit memlock=-1 --ulimit stack=67108864 --rm \
+    -v $(pwd):/workspace/LLaVA-OneVision-1.5 \
+    -w /workspace/LLaVA-OneVision-1.5 \
+    --name "llava_megatron_container" \
+    llava_megatron:25.04 /bin/bash
+```
+
+### 2. Checkpoint and Format Conversion
+
+You have two options to get started with LLaVA-OneVision-1.5-stage-0:
+
+#### Option 1: Download pre-trained model from HuggingFace
+Download our `LLaVA-OneVision-1.5-4B-stage0` model directly from [HuggingFace](https://huggingface.co/lmms-lab/LLaVA-OneVision-1.5-4B-stage0).
+
+#### Option 2: Merge initial weights yourself
+Alternatively, you can merge the initial weights from the original ViT and LLM:
+```bash
+python ds/merge_model.py \
+--vit_path DeepGlint-AI/rice-vit-large-patch14-560 \
+--llm_path Qwen/Qwen3-4B-Instruct-2507 \
+--output LLaVA-OneVision-1.5-4B-stage0
+```
+Note: When merging weights, the adapter component will be initialized with default values.
+
+Convert the model from HuggingFace format to Megatron format:
+
+```bash
+AIAK_TRAINING_PATH=/workspace/LLaVA-OneVision-1.5 bash examples/llava_ov_1_5/convert/convert_4b_hf_to_mcore.sh \
+LLaVA-OneVision-1.5-4B-stage0 \
+LLaVA-OneVision-1.5-4B-stage0_mcore_tp1_pp1 \
+1 1
+```
+
+### 3. Stage 1 Alignment-Training
+
+Download LLaVA from [LLaVA-558K-Webdataset](https://huggingface.co/datasets/lmms-lab/LLaVA-558K-Webdataset).
+
+
+```bash
+# ============================================================
+# Required environment variables:
+#   AIAK_TRAINING_PATH  Root directory of the AIAK-Training-LLM project
+#   DATA_PATH           Directory with WebDataset shards (.tar) for pretraining
+#   TOKENIZER_PATH      Hugging Face tokenizer directory
+#   CHECKPOINT_PATH     Megatron-formatted checkpoint directory (e.g., mcore TP1/PP1)
+#   SAVE_CKPT_PATH      Output directory for saving training checkpoints
+AIAK_TRAINING_PATH=/workspace/LLaVA-OneVision-1.5 \
+DATA_PATH=LLaVA-558K-Webdataset \
+TOKENIZER_PATH=LLaVA-OneVision-1.5-4B-stage0 \
+CHECKPOINT_PATH=LLaVA-OneVision-1.5-4B-stage0_mcore_tp1_pp1 \
+bash examples/llava_ov_1_5/quick_start/stage_1_alignment_llava_ov_4b.sh
+```
+
+### 4. Stage 1.5 Mid-Training 
+
+Download our lightweight packed subset from [LLaVA-OneVision-1.5-Mid-Training-Quick-Start-3M-Webdataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-1.5-Mid-Training-Webdataset-Quick-Start-3M).
+
+```bash
+# ============================================================
+# Convert model to release format
+bash examples/llava_ov_1_5/convert/convert_4b_mcore_to_release.sh \
+stage_1_alignment_llava_ov_4b/iter_0002500/ \
+stage_1_alignment_llava_ov_4b_release 1 1
+# ============================================================
+# Launch
+AIAK_TRAINING_PATH=/workspace/LLaVA-OneVision-1.5 \
+DATA_PATH=LLaVA-OneVision-1.5-Mid-Training-Quick-Start-3M-Webdataset \
+TOKENIZER_PATH=LLaVA-OneVision-1.5-4B-stage0 \
+CHECKPOINT_PATH=stage_1_alignment_llava_ov_4b_release \
+bash examples/llava_ov_1_5/quick_start/stage_1.5_mid_training_llava_ov_4b.sh
+```
+
+
+### 5. Stage 2 Instruct-Training
+
+Download LLaVA-NeXT-780k-webdataset at [LLaVA-NeXT-780K Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-780k-webdataset).
+
+```bash
+# ============================================================
+# Convert model to release format
+bash examples/llava_ov_1_5/convert/convert_4b_mcore_to_release.sh \
+stage_1.5_mid_training_llava_ov_4b/iter_0020000/ \
+stage_1.5_mid_training_llava_ov_4b_release 1 1
+# ============================================================
+# # Launch
+AIAK_TRAINING_PATH=/workspace/LLaVA-OneVision-1.5 \
+DATA_PATH=LLaVA-NeXT-780k-Webdataset \
+TOKENIZER_PATH=LLaVA-OneVision-1.5-4B-stage0 \
+CHECKPOINT_PATH=stage_1.5_mid_training_llava_ov_4b_release \
+bash examples/llava_ov_1_5/quick_start/stage_2_instruct_llava_ov_4b.sh
+```
+
+
+### 6. Convert mcore to huggingface
+```bash
+AIAK_TRAINING_PATH=/workspace/LLaVA-OneVision-1.5 \
+bash examples/llava_ov_1_5/convert/convert_4b_mcore_to_hf.sh \
+stage_2_instruct_llava_ov_4b/iter_0003500 \
+LLaVA-OneVision-1.5-4B-3M-Mid-Training-780K-Instruct \
+1 1
+# Copy non-model files (e.g., tokenizer config) to the new directory
+find LLaVA-OneVision-1.5-4B-stage0/ -type f -not -iname '*safetensors*' -exec cp {}  LLaVA-OneVision-1.5-4B-3M-Mid-Training-780K-Instruct/ ';'
+```
+
+### 7. Evaluation
+```bash
+# pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+CUDA_VISIBLE_DEVICES=4,5,6,7 accelerate launch \
+--num_processes=4 --main_process_port 12399 -m lmms_eval --model=llava_onevision1_5 --batch_size=1 --tasks=mme \
+--model_args=pretrained=/workspace/LLaVA-OneVision-1.5/LLaVA-OneVision-1.5-4B-3M-Mid-Training-780K-Instruct,max_pixels=3240000
+```
+
+## Fully Reproducing Guide
+
+> [!TIP]
+> More detailed reproduction steps for the complete process will be provided after the dataset upload is completed.
+
+
+### Mid-Training
+
+To improve model training efficiency, we implement offline sample packing:
+
+1. Download the [**Mid-Training-85M Dataset**](https://huggingface.co/datasets/lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M)
+2. Pack the data into webdataset format, refer to [**Examples offlinepacking**](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/tree/main/examples_offline_packing) and [**Offline Padding-Free Data Packing**](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/tree/main/examples/llava_ov_1_5/sample_packing/README.md)
+
+
+### Instruct
+1. Download the [**LLaVA-OneVision-1.5-Insturct-Data**](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-1.5-Insturct-Data)
+2. Convert the data into webdataset format, refer to [**Conversion for Mixed Instruction Data**](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5/blob/main/docs/sft_data_preprocessing.md)
+
+## Roadmaps
+
+Q4 2025 Key Deliverables:
+
+1. **Ultra-efficient MoE Training**  
+2. **Full Video Input LLM**  
+
+
+## Contributors
+Thanks so much to all of our amazing contributors!
+
+<!-- readme: collaborators,contributors,jiankangdeng/- -start -->
+<table>
+	<tbody>
+		<tr>
+            <td align="center">
+                <a href="https://github.com/fdcp">
+                    <img src="https://avatars.githubusercontent.com/u/15667917?v=4" width="80;" alt="fdcp"/>
+                    <br />
+                    <sub><b>fdcp</b></sub>
+                </a>
+            </td>
+            <td align="center">
+                <a href="https://github.com/anxiangsir">
+                    <img src="https://avatars.githubusercontent.com/u/31175974?v=4" width="80;" alt="anxiangsir"/>
+                    <br />
+                    <sub><b>anxiangsir</b></sub>
+                </a>
+            </td>
+            <td align="center">
+                <a href="https://github.com/yiyexy">
+                    <img src="https://avatars.githubusercontent.com/u/35927125?v=4" width="80;" alt="yiyexy"/>
+                    <br />
+                    <sub><b>yiyexy</b></sub>
+                </a>
+            </td>
+            <td align="center">
+                <a href="https://github.com/wideyard">
+                    <img src="https://avatars.githubusercontent.com/u/101321826?v=4" width="80;" alt="wideyard"/>
+                    <br />
+                    <sub><b>wideyard</b></sub>
+                </a>
+            </td>
+            <td align="center">
+                <a href="https://github.com/chengzheng345">
+                    <img src="https://avatars.githubusercontent.com/u/209475443?v=4" width="80;" alt="chengzheng345"/>
+                    <br />
+                    <sub><b>chengzheng345</b></sub>
+                </a>
+            </td>
+            <td align="center">
+                <a href="https://github.com/killTheHostage">
+                    <img src="https://avatars.githubusercontent.com/u/16442720?v=4" width="80;" alt="killTheHostage"/>
+                    <br />
+                    <sub><b>killTheHostage</b></sub>
+                </a>
+            </td>
+            <td align="center">
+                <a href="https://github.com/mathCrazyy">
+                    <img src="https://avatars.githubusercontent.com/u/20607153?v=4" width="80;" alt="mathCrazyy"/>
+                    <br />
+                    <sub><b>mathCrazyy</b></sub>
+                </a>
+            </td>
+            <td align="center">
+                <a href="https://github.com/yunglechao">
+                    <img src="https://avatars.githubusercontent.com/u/7631185?v=4" width="80;" alt="yunglechao"/>
+                    <br />
+                    <sub><b>yunglechao</b></sub>
+                </a>
+            </td>
+		</tr>
+		<tr>
+            <td align="center">
+                <a href="https://github.com/RobitYadda">
+                    <img src="https://avatars.githubusercontent.com/u/6811311?v=4" width="80;" alt="RobitYadda"/>
+                    <br />
+                    <sub><b>RobitYadda</b></sub>
+                </a>
+            </td>
+		</tr>
+	<tbody>
+</table>
+<!-- readme: collaborators,contributors,jiankangdeng/- -end -->
+
 ## Citation
 
 If you find *LLaVA-OneVision-1.5* useful in your research, please consider to cite the following related papers:
 
 ```
-@misc{an2025llavaonevision15fullyopenframework,
-      title={LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training}, 
-      author={Xiang An and Yin Xie and Kaicheng Yang and Wenkang Zhang and Xiuwei Zhao and Zheng Cheng and Yirui Wang and Songcen Xu and Changrui Chen and Chunsheng Wu and Huajie Tan and Chunyuan Li and Jing Yang and Jie Yu and Xiyao Wang and Bin Qin and Yumeng Wang and Zizhen Yan and Ziyong Feng and Ziwei Liu and Bo Li and Jiankang Deng},
-      year={2025},
-      eprint={2509.23661},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2509.23661}, 
+@inproceedings{LLaVA-OneVision-1.5,
+  title={LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training},
+  author={An, Xiang and Xie, Yin and Yang, Kaicheng and Zhang, Wenkang and Zhao, Xiuwei and Cheng, Zheng and Wang, Yirui and Xu, Songcen and Chen, Changrui and Wu, Chunsheng and Tan, Huajie and Li, Chunyuan and Yang, Jing and Yu, Jie and Wang, Xiyao and Qin, Bin and Wang, Yumeng and Yan, Zizhen and Feng, Ziyong and Liu, Ziwei and Li, Bo and Deng, Jiankang},
+  booktitle={arxiv},  
+  year={2025}
+ }
+
+@inproceedings{xie2025region,
+  title={Region-based Cluster Discrimination for Visual Representation Learning},
+  author={Xie, Yin and Yang, Kaicheng and An, Xiang and Wu, Kun and Zhao, Yongle and Deng, Weimo and Ran, Zimin and Wang, Yumeng and Feng, Ziyong and Miles, Roy and Elezi, Ismail and Deng, Jiankang},
+  booktitle={ICCV},
+  year={2025}
+}
+
+@article{lillava,
+  title={LLaVA-OneVision: Easy Visual Task Transfer},
+  author={Li, Bo and Zhang, Yuanhan and Guo, Dong and Zhang, Renrui and Li, Feng and Zhang, Hao and Zhang, Kaichen and Zhang, Peiyuan and Li, Yanwei and Liu, Ziwei and Li, Chunyuan},
+  journal={Transactions on Machine Learning Research}
+  year={2024}
 }
-```
+```
+
+## Acknowledgement
+
+We extend our sincere gratitude to **AIAK team of the** [**Baige AI computing platform**](https://cloud.baidu.com/product/aihc.html) **from Baidu AI Cloud** for providing the exceptional training framework. The outstanding capabilities of AIAK-Training-LLM and AIAK-Megatron have significantly accelerated our training process with remarkable efficiency. These cutting-edge frameworks have been instrumental in achieving our research goals. `To get full AIAK support, you can contact Baidu Cloud.`
+
+
+We also thank the maintainers and contributors of the following open-source projects, whose work greatly inspired and supported our research:
+
+- LLaVA: Large Language-and-Vision Assistant — [LLaVA](https://github.com/haotian-liu/LLaVA)
+- LLaVA-NeXT: Next-generation multi-modal assistant — [LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT)
+- lmms-eval: A standardized evaluation framework for Large Multimodal Models — [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval)
+- Megatron-LM: Efficient, scalable training for large language models — [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
+- Qwen2.5-VL: Strong vision-language foundation model — [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL)
+- InternVL: Open-source large-scale vision-language foundation model — [InternVL](https://github.com/OpenGVLab/InternVL)
+- Qwen3: Next-generation Qwen LLM — [Qwen](https://github.com/QwenLM/Qwen)
+- MetaCLIP: Scalable contrastive pretraining — [MetaCLIP](https://github.com/facebookresearch/MetaCLIP)
+- FineVision: Open Data Is All You Need — [FineVision](https://huggingface.co/spaces/HuggingFaceM4/FineVision)