--- library_name: transformers tags: [] --- # HumanF-MarkrAI/Gukbap-Ovis2-16B-VL🍚 ## Model Details🍚 ### Model Description - **Developed by:** HumanF-MarkrAI - **Model type:** Korean-VL-Ovis2-16B - **Language(s):** Korean + English - **Context Length:** 2048 - **License:** cc-by-4.0 - **Finetuned from model:** [AIDC-AI/Ovis2-16B](https://huggingface.co/AIDC-AI/Ovis2-16B). ### Model Sources When training, we used `H100 80GB GPU`x4. ### Implications🍚 If you want to know our model's details, please see [🔥Gukbap-LMM Blog🔥](https://kyujinpy.tistory.com/169). And also, we provided the Korean-LMM training code based Ovis!! [🔥Github🔥](https://github.com/Marker-Inc-Korea/Ovis2-FFT-Korean). Please star⭐⭐!! ### Training Method (SFT)🧐 The following papers contain the foundational methodologies for the dataset and training methods we are currently proceeding. - [LIMA](https://arxiv.org/abs/2305.11206). - [Ovis](https://arxiv.org/abs/2405.20797). ### SFT Text-Datasets (Private) When we made the `Open-Source based dataset`, we use `microsoft/WizardLM-2-8x22B` through [DeepInfra](https://deepinfra.com/). Our datasets are made by `Evolving system`, which is propsed by [WizardLM](https://wizardlm.github.io/WizardLM2/). In training, we used 1849 training dataset, and 200 validation dataset. - **Wizard-Korea-Datasets:** [MarkrAI/Markr_WizardLM_train_ver4](https://huggingface.co/datasets/MarkrAI/Markr_WizardLM_train_ver4). > Learning rate: 2e-5; Epoch: 2 ## Benchmakrs🤗 ### Global MM Benchmark Score (Zero-shot) We internally evaluated [VLMEvalKit](https://github.com/open-compass/VLMEvalKit?tab=readme-ov-file). We utilized **chatgpt-0125**, **gpt-4o-mini** and **gpt-4-turbo** in `MMBench`, `MathVista` and `MMVet`, respectively. | Model | MMStar | MathVista | HallusionBench | AI2D | OCRBench | MMVet | MMBench_V11 | AVG | |:---------:|:-----:|:------:|:-----:|:-----:|:----:|:-----:|:-----:|:-----:| | Step-1o (closed model) | 69.3 | **74.7** | **89.1** | 55.8 | **92.6** | **82.8** | 87.3 | **78.8** | | InternVL2.5-78B-MPO (Open) | **72.1** | 76.6 | 58.1 | **89.2** | 90.9 | 73.5 | **87.8** | 78.3 | | InternVL2.5-38B-MPO (Open) | 70.1 | 73.6 | 59.7 | 87.9 | 89.4 | 72.6 | 85.4 | 77.0 | | Ovis2-16B (Open) | 67.2 | 73.7 | 56.8 | 86.3 | 87.9 | 68.4 | 85.7 | 75.14 | |:---------:|:-----:|:------:|:-----:|:-----:|:----:|:-----:|:-----:|:-----:| | **Gukbap-Ovis2-16B-VL🍚** | 65.67 | 73.70 | 54.52 | 85.46 | 84.80 | 66.83 | 85.22 | **73.74** | |:---------:|:-----:|:------:|:-----:|:-----:|:----:|:-----:|:-----:|:-----:| | Gemini-2.0-Flash | 69.4 | 70.4 | 58.0 | 83.1 | 82.5 | 73.6 | 71.0 | 72.6 | | GPT-4o-20241120 | 65.1 | 59.9 | 56.2 | 84.9 | 80.6 | 74.5 | 84.3 | 72.2 | | Ovis1.6-Gemma2-9B (Open) | 62.00 | 67.10 | 84.42 | 51.96 | 82.60 | 64.68 | 82.20 | 70.71 | | **Gukbap-Gemma2-9B-VL🍚** | 62.13 | 66.00 | 84.49 | 53.01 | 82.80 | 63.90 | 82.20 | **70.65** | | LLaVA-OneVision-72B | 65.8 | 68.4 | 47.9 | 86.2 | 74.1| 60.6 | 84.5 | 69.6 | | VARCO-VISION-14B (NCSoft) | 64.1 | 67.6 | 46.8 | 83.9 | 81.5 | 53.0 | 81.2 | 68.3 | | GPT-4o-mini-20240718 | 54.8 | 52.4 | 46.1 | 77.8 | 78.5 | 66.9 | 76.0 | 64.6 | > HallusionBench score: (aAcc + fAcc + qAcc) / 3 ### Korean MM Benchmark Score (Zero-shot) We internally evaluated [🔥our code🔥](https://github.com/Marker-Inc-Korea/KoVLMEval). We utilized **gpt-4o-2024-08-06** in `K-LLAVA-W` evaluation. | Model | K-MMBench | K-MMStar | K-DTCBench | K-LLAVA-W | AVG | |:---------:|:-----:|:------:|:-----:|:-----:|:----:| | GPT-4o-20241120 | NaN | NaN | NaN | 85.50 | NaN | |:---------:|:-----:|:------:|:-----:|:-----:|:----:| | **Gukbap-Ovis2-16B-VL🍚** | 88.24 | 61.00 | 79.58 | **66.67** | **73.87** | | **Ovis2-16B** | **88.31** | **61.80** | 81.25 | 61.00 | 71.94 | | Gukbap-Gemma2-9B-VL🍚 | 80.16 | 54.20 | 52.92 | 63.83 | 62.78 | | Ovis1.6-Gemma2-9B | 52.46 | 50.40 | 47.08 | 55.67 | 51.40 | | VARCO-VISION-14B | 87.16 | 58.13 | **85.42** | 51.17 | 70.47 | | llama-3.2-Korean-Bllossom-AICA-5B | 26.01 | 21.60 | 17.08 | 45.33 | 27.51 | ### MM Benchmarks - Global MM Bench dataset: [OpenCampass MM leaderboard](https://rank.opencompass.org.cn/leaderboard-multimodal) - Korean MM Bench dataset: [NCSOFT](https://huggingface.co/NCSOFT). ## Inference ```python import torch from PIL import Image from transformers import AutoModelForCausalLM #import os #os.environ["cuda_visible_devices"]="0" # load model if __name__ == '__main__': # HumanF-MarkrAI/Gukbap-Ovis2-34B-VL # AIDC-AI/Ovis2-34B model = AutoModelForCausalLM.from_pretrained("HumanF-MarkrAI/Gukbap-Ovis2-16B-VL", torch_dtype=torch.bfloat16, multimodal_max_length=2048, cache_dir="/data/cache/", trust_remote_code=True).cuda() text_tokenizer = model.get_text_tokenizer() visual_tokenizer = model.get_visual_tokenizer() # single-image input (K-LLAVA-W) image_path = './images/ex_4.jpg' images = [Image.open(image_path)] max_partition = 9 text = '이미지에서 잘리지 않은 과일은 몇 개인가요?' query = f'\n{text}' # format conversation prompt, input_ids, pixel_values = model.preprocess_inputs(query, images, max_partition=max_partition) attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id) input_ids = input_ids.unsqueeze(0).to(device=model.device) attention_mask = attention_mask.unsqueeze(0).to(device=model.device) if pixel_values is not None: pixel_values = pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device) pixel_values = [pixel_values] # generate output with torch.inference_mode(): gen_kwargs = dict( max_new_tokens=2048, do_sample=False, top_p=None, top_k=None, temperature=None, repetition_penalty=None, eos_token_id=model.generation_config.eos_token_id, pad_token_id=text_tokenizer.pad_token_id, use_cache=True ) output_ids = model.generate(input_ids, pixel_values=pixel_values, attention_mask=attention_mask, **gen_kwargs)[0] output = text_tokenizer.decode(output_ids, skip_special_tokens=True) print(f'Output:\n{output}') ``` ## Chat Prompt😶‍🌫️ ```yaml <|im_start|>user Hello! My favorite food is Gukbap🍚!<|im_end|> <|im_start|>assistant (model answer) ``` ## Gukbap-VL Series models🍚🍚 - [HumanF-MarkrAI/Gukbap-Gemma2-9B-VL](https://huggingface.co/HumanF-MarkrAI/Gukbap-Gemma2-9B-VL) - [HumanF-MarkrAI/Gukbap-Ovis2-34B-VL](https://huggingface.co/HumanF-MarkrAI/Gukbap-Ovis2-34B-VL) ## BibTeX ``` @article{HumanF-MarkrAI, title={Gukbap-Ovis2-16B-VL}, author={MarkrAI}, year={2025}, url={https://huggingface.co/HumanF-MarkrAI} } ```