naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B

Overview

HyperCLOVAX-SEED-Vision-Instruct-3B is a model developed by NAVER, built upon its proprietary backbone model and fine-tuned through post-training. It is capable of understanding both text and images, as well as generating text.

The model is primarily designed with a focus on lightweight architecture, optimizing computational efficiency. In terms of visual understanding, it can handle visual question answering (VQA), chart and diagram interpretation, and even comprehend content. HyperCLOVAX-SEED-Vision-Instruct-3B aims for a Pareto-optimal balance specifically tuned for the Korean language, and it demonstrates competitive performance using fewer visual tokens compared to other models of similar size in inference scenarios.

Particularly, the model shows relative strengths in handling Korean-language inputs and outperforms similarly sized open-source models in related benchmarks. As the first open-source vision-language model in Korea capable of visual understanding, it is expected to significantly contribute to strengthening Korea's sovereign AI capabilities.

Basic Information

• Model Architecture: LLaVA-based Vision-Language Model
  • LLM Module: Transformer-based architecture (Dense Model)
  • Vision Encoder : SigLIP-based architecture with 378x378px input resolution per grid.
  • Vision-Language Connector : C-Abstractor based architecture with AnyRes mechanism, supporting up to 1.29M total pixels across 9 grids.
• Parameter Count: 3.2B (LLM Module) + 0.43B (Vision Module)
• Input/Output Format: Text + Image + Video / Text
• Context Length: 16k
• Knowledge Cutoff Date: The model was trained on data collected before August 2024.

Training

Text

Securing high-quality data is essential even during post-training, but having humans manually create or revise large-scale datasets posed significant limitations in terms of both cost and resources. Additionally, tasks requiring domain expertise were difficult to handle, and the risk of human error was high. To overcome these challenges, we utilized an automated validation system powered by HyperCLOVA X, which improved data quality and streamlined the training process — ultimately leading to enhanced overall model performance. As a result, the model showed significant improvements in areas with definitive answers, such as mathematics and coding.

While reducing the cost of data collection is important, finding efficient training strategies is equally critical. HyperCLOVAX-SEED-Vision-Instruct-3B was developed starting from the HyperCLOVAX-SEED-Text-Base-3B and applied both Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) based on an online reinforcement algorithm called GRPO.

Vision

The Vision Understanding feature — where the model receives images and questions as input and generates text-based answers — was not part of the initial design of HyperCLOVA X. Therefore, the model architecture was carefully designed to add capabilities for handling vision-related tasks, such as image-based question answering (VQA) and chart/diagram interpretation, without compromising the existing performance of the HCX LLM. Special attention was given to handling auxiliary information within the input, especially considering the context length.

Although HyperCLOVAX-SEED-Vision-Instruct-3B is a lightweight model, it is capable of performing basic image VQA tasks and even supports OCR-free processing. One of the key focus areas for this 3B model was optimizing the efficiency of video input tokens. Since input token length directly affects computational cost, the number of tokens extracted per frame was carefully adjusted to enable efficient video understanding with as few tokens as possible. Additionally, during the RLHF training phase, vision-specific V-RLHF data was used to enhance the model’s learning, just like in the text domain.

Benchmark

Text

Model	KMMLU (5-shot, acc)	HAE-RAE (5-shot, acc)	CLiCK (5-shot, acc)	KoBEST (5-shot, acc)
HyperCLOVAX-SEED-Text-Base-3B	0.4847	0.7635	0.6386	0.7792
HyperCLOVAX-SEED-Vision-Instruct-3B	0.4422	0.6499	0.5599	0.7180
Qwen2.5-3B-instruct	0.4451	0.6031	0.5649	0.7053
gemma-3-4b-it	0.3895	0.6059	0.5303	0.7262

Vision

Model Name	Max Token Count per Video	VideoMME (Ko)	NAVER-TV-CLIP (Ko)	VideoChatGPT (Ko)	PerceptionTest (En)	ActivityNet-QA (En)	KoNet (Ko)	MMBench-Val (En)	TextVQA-Val (En)	Korean VisIT-Bench (Ko)	Image (4 benchmarks)	Video (5 benchmarks)	All (9 benchmarks)
HyperCLOVAX-SEED-Vision-Instruct-3B	1856 tokens, 108 frames	48.2	61.0	53.6	55.2	50.6	69.2	81.8	79.2	37.0	46.68	53.70	59.54
HyperCLOVAX-SEED-Vision-Instruct-3B (without OCR)	1856 tokens, 108 frames	48.2	61.0	53.6	55.2	50.6	36.6	80.7	76.0	43.5	56.74	53.70	55.05
Qwen-2.5-VL-3B	24576 tokens, 768 frames	55.1	48.3	45.6	66.9	55.7	58.3	84.3	79.6	81.5	59.35	54.31	56.55
Qwen-2.5-VL-3B (w/ 2000 tokens)	2000 tokens, 128 frames	50.3	43.9	44.3	58.3	54.2	58.5	84.3	79.3	15.7	59.50	50.18	54.33
Qwen-2.5-VL-7B	24576 tokens, 768 frames	60.6	66.7	51.8	70.5	56.6	68.4	88.3	84.9	85.6	69.34	61.23	64.84
Gemma-3-4B	4096 tokens, 16 frames	45.4	36.8	57.1	50.6	46.3	25.0	79.2	58.9	32.3	48.91	47.24	47.98
GPT4V (gpt-4-turbo-2024-04-09)	Unknown, Original Image , 8 frames	49.1	75.0	55.5	57.4	45.7	38.7	84.2	60.4	52.0	58.88	51.59	54.83
GPT4o (gpt-4o-2024-08-06)	Unknown, 512 resize, 128 frames	61.6	66.6	61.8	50.2	41.7	60.6	84.2	73.2	50.5	67.15	56.42	61.19
InternV-2-2B	4096 tokens, 16 frames	28.9	21.1	40.2	50.5	50.3	3.3	79.3	75.1	51.1	39.74	38.19	38.88
InternV-2-4B	4096 tokens, 16 frames	33.8	36.0	22.8	54.2	52.0	22.7	83.0	76.9	51.6	46.11	39.75	42.58
InternV-2-8B	4096 tokens, 16 frames	43.7	41.2	32.4	58.5	53.2	28.5	86.6	79.0	97.0	50.32	45.79	47.81

Dependencies

• einops
• timm
• av
• decord

Example

from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer

model_name = "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B"
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device="cuda")
preprocessor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# LLM Example
# It is recommended to use the chat template with HyperCLOVAX models.
# Using the chat template allows you to easily format your input in ChatML style.
chat = [
        {"role": "system", "content": "you are helpful assistant!"},
        {"role": "user", "content": "Hello, how are you?"},
        {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
        {"role": "user", "content": "I'd like to show off how chat templating works!"},
]
input_ids = tokenizer.apply_chat_template(chat, return_tensors="pt", tokenize=True)
input_ids = input_ids.to(device="cuda")

# Please adjust parameters like top_p appropriately for your use case.
output_ids = model.generate(
        input_ids,
        max_new_tokens=64,
        do_sample=True,
        top_p=0.6,
        temperature=0.5,
        repetition_penalty=1.0,
)
print("=" * 80)
print("LLM EXAMPLE")
print(tokenizer.batch_decode(output_ids)[0])
print("=" * 80)

# VLM Example
# For image and video inputs, you can use url, local_path, base64, or bytes.
vlm_chat = [
        {"role": "system", "content": {"type": "text", "text": "System Prompt"}},
        {"role": "user", "content": {"type": "text", "text": "User Text 1"}},
        {
                "role": "user",
                "content": {
                        "type": "image",
                        "filename": "tradeoff_sota.png",
                        "image": "https://github.com/naver-ai/rdnet/blob/main/resources/images/tradeoff_sota.png?raw=true",
                        "ocr": "List the words in the image in raster order. Even if the word order feels unnatural for reading, the model will handle it as long as it follows raster order.",
                        "lens_keywords": "Gucci Ophidia, cross bag, Ophidia small, GG, Supreme shoulder bag",
                        "lens_local_keywords": "[0.07, 0.21, 0.92, 0.90] Gucci Ophidia",
                }
        },
        {
                "role": "user",
                "content": {
                        "type": "image",
                        "filename": "tradeoff.png",
                        "image": "https://github.com/naver-ai/rdnet/blob/main/resources/images/tradeoff.png?raw=true",
                }
        },
        {"role": "assistant", "content": {"type": "text", "text": "Assistant Text 1"}},
        {"role": "user", "content": {"type": "text", "text": "User Text 2"}},
        {
                "role": "user",
                "content": {
                        "type": "video",
                        "filename": "rolling-mist-clouds.mp4",
                        "video": "freenaturestock-rolling-mist-clouds.mp4",
                }
        },
        {"role": "user", "content": {"type": "text", "text": "User Text 3"}},
]

new_vlm_chat, all_images, is_video_list = preprocessor.load_images_videos(vlm_chat)
preprocessed = preprocessor(all_images, is_video_list=is_video_list)
input_ids = tokenizer.apply_chat_template(
        new_vlm_chat, return_tensors="pt", tokenize=True, add_generation_prompt=True,
)

output_ids = model.generate(
        input_ids=input_ids.to(device="cuda"),
        max_new_tokens=8192,
        do_sample=True,
        top_p=0.6,
        temperature=0.5,
        repetition_penalty=1.0,
        **preprocessed,
)
print(tokenizer.batch_decode(output_ids)[0])

• To ensure the highest level of image understanding performance, it is recommended to include additional information such as Optical Character Recognition (OCR) results and entity recognition (Lens). The provided usage examples are written under the assumption that OCR and Lens results are available. If you input data in this format, you can expect significantly improved output quality.