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LLaVA-OneVision-1.5-4B-Stage1_Alignment-Action

Name: LLaVA-OneVision-1.5-4B-Stage1_Alignment-Action
Availability: InStock

lmms-lab/LLaVA-One-Vision-1.5-Mid-Training-85M

2509.23661

DeepGlint-AI/rice-vit-large-patch14-560

finetune

apache-2.0

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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

Code: https://github.com/EvolvingLMMs-Lab/LLaVA-OneVision-1.5

Demo: https://huggingface.co/spaces/lmms-lab/LLaVA-OneVision-1.5

NEWS

2025-09-30: Released a comprehensive Offline Data Pack documentation.
2025-09-30: Released the LLaVA-OneVision-1.5 Technical Report.

Introduction

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.
High-Quality Data at Scale 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)
- Built on MegatronLM with support for MoE, FP8, and long sequence parallelization
- Optimized codebase for cost-effective scaling
Fully Open Framework for community access and reproducibility:
- High-quality pre-training & SFT data
- Complete training framework & code
- Training recipes & configurations
- Comprehensive training logs & metrics

Models

Model	HF Link	Training Log
LLaVA-OV-1.5-4B-Instruct	🤗 HF / 4B-Instruct	📈 Tensorboard
LLaVA-OV-1.5-8B-Instruct	🤗 HF / 8B-Instruct	📈 Tensorboard
LLaVA-OV-1.5-4B-Base	🤗 HF / 4B-Base	📈 Tensorboard
LLaVA-OV-1.5-8B-Base	🤗 HF / 8B-Base	Uploading…

Datasets

(a) The vocabulary coverage proportion in the LLaVA-OneVision-1.5 Mid-Training dataset before and after concept balancing. (b) Distribution of data sources within the LLaVA-OneVision-1.5 Mid-Training dataset. (c) Distribution of data sources within the LLaVA-OneVision-1.5 Insturct dataset.

Description	Link	Status
LLaVA-OV-1.5-Mid-Training-85M	🤗HF / Mid-Training 85M	Uploading…
LLaVA-OV-1.5-Instruct	🤗HF / Insturct-Data	Uploading…

Evaluation Results

All evaluations were conducted using lmms_eval.

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.

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.

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.

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.

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:

Download the Mid-Training-85M Dataset
Pack the data into webdataset format, refer to Examples offlinepacking and Offline Padding-Free Data Packing

Instruct

Download the LLaVA-OneVision-1.5-Insturct-Data
Convert the data into webdataset format, refer to Conversion for Mixed Instruction Data

Roadmaps

Q4 2025 Key Deliverables:

Ultra-efficient MoE Training
Full Video Input LLM

Contributors

Thanks so much to all of our amazing contributors!

fdcp _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 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
LLaVA-NeXT: Next-generation multi-modal assistant — LLaVA-NeXT
lmms-eval: A standardized evaluation framework for Large Multimodal Models — lmms-eval
Megatron-LM: Efficient, scalable training for large language models — Megatron-LM
Qwen2.5-VL: Strong vision-language foundation model — Qwen2.5-VL
InternVL: Open-source large-scale vision-language foundation model — InternVL
Qwen3: Next-generation Qwen LLM — Qwen
MetaCLIP: Scalable contrastive pretraining — MetaCLIP
FineVision: Open Data Is All You Need — FineVision

JasonYang66/LLaVA-OneVision-1.5-4B-Stage1_Alignment-Action

LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training

NEWS

Introduction

Models

Datasets

Evaluation Results

Quick Start with HuggingFace

Evaluation

Quick Start Guide

1.🐳 Docker (Recommended)

2. Checkpoint and Format Conversion

Option 1: Download pre-trained model from HuggingFace

Option 2: Merge initial weights yourself

3. Stage 1 Alignment-Training

4. Stage 1.5 Mid-Training

5. Stage 2 Instruct-Training

6. Convert mcore to huggingface

7. Evaluation

Fully Reproducing Guide

Mid-Training

Instruct

Roadmaps

Contributors

Citation

Acknowledgement

JasonYang66/
LLaVA-OneVision-1.5-4B-Stage1_Alignment-Action