Models	Vicuna-80	GQA	HallusionBench	MME-P	MMVP	TouchStone	TextVQA	MME-C	MathVista	MM-Vet	MMMU-val	SQA (img)	LLaVA (In-the-Wild)	MLLM-Bench	MMB-en	MMB-cn	SEEDBench (img, v1)
Large VLMs
BLIP-2	-	-	-	-	-	-	-	-	-	22.4	34.4	-	-	3.0*	-	-	49.7
InstructBLIP	-	49.5	-	-	-	-	-	-	-	25.6	-	-	58.2	-	44.0	-	-
Qwen-VL-Chat	-	57.5	-	1487.6	-	-	61.5	360.7	-	31.1	-	68.2	-	-	60.6	56.7	65.4
LLaVA-1.5-7B	13.8*	62.0	36.6*	1504.4*	24.7*	594.9*	58.2	324.6*	25.0*	31.1	35.1*	66.8	65.4	23.0*	64.3	58.3	66.1
LLaVA-1.5-13B	22.5	63.3	36.5*	1531.3	38.0*	617.7*	61.3	295.4	28.3*	35.4	34.4*	71.6	72.5	-	67.7	63.6	68.2
LVIS-7B	-	62.6	-	-	-	-	58.7	-	-	31.5	-	-	67.0	29.0*	66.2	-	-
LVIS-13B	-	63.6*	-	-	-	-	62.5*	-	-	37.4*	-	-	71.3*	-	68.0*	-	-
ShareGPT4V-7B	13.8*	63.3	36.0*	1540.1*	34.0*	637.2*	60.4	346.1*	24.7*	37.6	35.4*	68.4*	72.6	30.2*	68.8	61.0*	69.7
ShareGPT4V-13B	17.5*	64.8	39.0*	1576.1*	35.3*	648.7*	62.2	309.3*	28.8*	43.1	35.6*	70.0*	79.9	35.5*	71.2	61.7*	70.8
4B-scale Lite VLMs
MobileVLM-v2	5.0*	61.1	30.8*	1440.5	18.7*	541.0*	57.5	261.8*	28.3*	26.1*	30.8*	70.0	53.2*	15.7*	63.2	43.2*	64.5*
Mipha-3B	16.2*	63.9	34.3*	1488.9	32.0*	619.0*	56.6	285.0*	27.8*	33.5*	35.8*	70.9	64.7*	23.1*	69.7	42.9*	71.2*
TinyLLaVA	15.6*	62.1	37.2*	1465.5*	33.3*	663.5*	60.3	281.1*	30.3*	37.5	38.4	73.0	70.8*	29.8*	69.7*	42.8*	70.4*
Ours
ALLaVA-Phi2	49.4	48.8	24.8	1316.2	36.0	632.0	49.5	301.8	27.4	32.2	35.3	67.6	69.4	43.6	64.0	40.8	65.2
ALLaVA-StableLM2	38.8	49.8	25.3	1311.7	34.0	655.2	51.7	257.9	27.7	31.7	33.3	64.7	72.0	39.3	64.6	49.8	65.7
ALLaVA-Phi3	56.9	52.2	48.1	1382.3	32.7	667.8	53.0	347.1	32.9	37.8	41.1	64.0	68.5	54.8	68.1	55.3	69.0

* denotes the results of our evaluation. Bold numbers are the best results among all 4B-scale LVLMs.The detailed information of each benchmark is shown in Table 4 of our technical report.

🏭 Inference

All models can be loaded from 🤗 with .from_pretrained(). Check out the example scripts and make sure you have the same outputs as shown in the scripts.

🏋️‍♂️ Training

Data

ALLaVA uses 1.0M and 1.5M data for PT. and FT., respectively.

Code

The training code is largely based on LLaVA-v1.5. We wholeheartedly express our gratitude for their invaluable contributions to open-sourcing LVLMs.

Hyperparameters

Global Batch Size	ZeRO Stage	Optimizer	Max LR	Min LR	Scheduler	Weight decay
256 (PT) / 128 (FT)	1	AdamW	2e-5	2e-6	CosineAnnealingWarmRestarts	0

The LM backbone, projector are trainable, while the vision encoder is kept frozen. The trainabilities of each module are the same for both stages.

📚 ALLaVA-4V Data

The majority part of training data is ALLaVA-4V. See here to prepare it for training.

🙌 Contributors

Project Leader: Guiming Hardy Chen
Data: Shunian Chen, Junying Chen, Xiangbo Wu
Evaluation: Ruifei Zhang
Deployment: Xiangbo Wu, Zhiyi Zhang
Advising: Zhihong Chen, Benyou Wang
Others: Jianquan Li, Xiang Wan

📝 Citation

If you find our data useful, please consider citing our work! We are FreedomIntelligence from Shenzhen Research Institute of Big Data and The Chinese University of Hong Kong, Shenzhen

@article{chen2024allava,
  title={ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model},
  author={Chen, Guiming Hardy and Chen, Shunian and Zhang, Ruifei and Chen, Junying and Wu, Xiangbo and Zhang, Zhiyi and Chen, Zhihong and Li, Jianquan and Wan, Xiang and Wang, Benyou},
  journal={arXiv preprint arXiv:2402.11684},
  year={2024}
}

FreedomIntelligence/ALLaVA-StableLM2-1_6B

ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model

Benchmark Result