--- base_model: - DeepGlint-AI/rice-vit-large-patch14-560 - Qwen/Qwen3-4B-Instruct-2507 datasets: - lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M - lmms-lab/LLaVA-OneVision-1.5-Insturct-Data library_name: transformers license: apache-2.0 pipeline_tag: image-text-to-text --- # LLaVA-OneVision-1.5: Fully Open-Source State-of-the-Art VLM Model This is the official Hugging Face model card for **LLaVA-OneVision-1.5**, a novel family of Large Multimodal Models (LMMs) presented in the paper [LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training](https://huggingface.co/papers/2509.23661). 📚 [Paper](https://huggingface.co/papers/2509.23661) | 💻 [Code](https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5) | 🏠 [Project Page](https://huggingface.co/collections/lmms-lab/llava-onevision-15-68d385fe73b50bd22de23713) | 🚀 [Demo](https://huggingface.co/spaces/lmms-lab/LLaVA-OneVision-1.5) # ✨ Key Features **LLaVA-OneVision-1.5** 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. 1. **Superior Performance** A family of fully open-source large multimodal models demonstrating **superior performance** across multiple multimodal benchmarks, **outperforming Qwen2.5-VL** in most evaluation tasks. 2. **High-Quality Data at Scale** Meticulously curated **mid-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 3. **Ultra-Efficient Training Framework** Complete end-to-end training framework designed for maximum efficiency: - **$16K total budget** for full model training - Built on **MegatronLM** with support for **MoE**, **FP8**, and **long sequence parallelization** - Optimized codebase for cost-effective scaling 4. **Fully Open Framework** for community access and reproducibility: - ✅ High-quality mid-training & SFT data - ✅ Complete training framework & code - ✅ Training recipes & configurations - ✅ Base & instruct model checkpoints - ✅ 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) | ## Dataset | Description | Link | |---|---| | Mid-training data for LLaVA-OneVision-1.5 | [🤗 Download (Uploading!)](https://huggingface.co/datasets/lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M) | | SFT data for LLaVA-OneVision-1.5 | [🤗 Download (Uploading!)](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-1.5-Insturct-Data) | ## Evaluation Results All evaluations were conducted using [lmms_eval](https://github.com/EvolvingLMMs-Lab/lmms-eval). ![image](https://cdn-uploads.huggingface.co/production/uploads/655c70d331c4978366d4b2e6/J8oBYmQkTOC6pBNLgJn9d.png) ## Quick Start with HuggingFace Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`: ```python from transformers import AutoTokenizer, AutoProcessor, AutoModelForCausalLM from qwen_vl_utils import process_vision_info model_path = "lmms-lab/LLaVA-One-Vision-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.git 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**](examples_offline_packing) and [**Offline Padding-Free Data Packing**](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**](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!
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fdcp 		anxiangsir
anxiangsir 		yiyexy
yiyexy 		wideyard
wideyard 		chengzheng345
chengzheng345 		killTheHostage
killTheHostage 		mathCrazyy
mathCrazyy 		yunglechao
yunglechao
RobitYadda
RobitYadda
## Citation If you find *LLaVA-OneVision-1.5* useful in your research, please consider to cite the following related papers: ``` @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)