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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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  ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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+ # HumanF-MarkrAI/Gukbap-Gemma2-9B-VL🍚
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+ ## Model Details🍚
 
 
 
 
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  ### Model Description
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+ - **Developed by:** HumanF-MarkrAI
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+ - **Model type:** Korean-VL-Gemma2-9B
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+ - **Language(s):** Korean + English
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+ - **Context Length:** 2048
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+ - **License:** cc-by-nc-4.0
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+ - **Finetuned from model:** [AIDC-AI/Ovis1.6-Gemma2-9B](https://huggingface.co/AIDC-AI/Ovis1.6-Gemma2-9B).
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+
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+ ### Model Sources
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+ When training, we used `H100 80GB GPU`x4.
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+ ### Implications🍚
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+ If you want to know our model's details, please see [🔥Gukbap-LMM Blog🔥](coming_soon).
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+ And also, we provided the Korean-LMM training code based Ovis!! [🔥Github🔥](coming_soon). Please star⭐⭐!!
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+
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+ ### Training Method (SFT)🧐
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+ The following papers contain the foundational methodologies for the dataset and training methods we are currently proceeding.
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+ - [LIMA](https://arxiv.org/abs/2305.11206).
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+ - [Ovis](https://arxiv.org/abs/2405.20797).
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+
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+ ### SFT Text-Datasets (Private)
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+ When we made the `Open-Source based dataset`, we use `microsoft/WizardLM-2-8x22B` through [DeepInfra](https://deepinfra.com/).
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+ Our datasets are made by `Evolving system`, which is propsed by [WizardLM](https://wizardlm.github.io/WizardLM2/).
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+ In training, we used 1849 training dataset, and 200 validation dataset.
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+ - **Wizard-Korea-Datasets:** [MarkrAI/Markr_WizardLM_train_ver4](https://huggingface.co/datasets/MarkrAI/Markr_WizardLM_train_ver4).
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+ > Learning rate: 1e-5; Epoch: 2
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+ ## Benchmakrs🤗
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+ ### Global MM Benchmark Score (Zero-shot)
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+ We internally evaluated [VLMEvalKit](https://github.com/open-compass/VLMEvalKit?tab=readme-ov-file).
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+ We utilized **chatgpt-0125**, **gpt-4o-mini** and **gpt-4-turbo** in `MMBench`, `MathVista` and `MMVet`, respectively.
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+ | Model | MMStar | MathVista | AI2D | HallusionBench | OCRBench | MMVet | MMBench_V11 | AVG |
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+ |:---------:|:-----:|:------:|:-----:|:-----:|:----:|:-----:|:-----:|:-----:|
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+ | Step-1o (closed model) | 69.3 | **74.7** | **89.1** | 55.8 | **92.6** | **82.8** | 87.3 | **78.8** |
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+ | InternVL2.5-78B-MPO (Open) | **72.1** | 76.6 | 58.1 | **89.2** | 90.9 | 73.5 | **87.8** | 78.3 |
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+ | InternVL2.5-38B-MPO (Open) | 70.1 | 73.6 | 59.7 | 87.9 | 89.4 | 72.6 | 85.4 | 77.0 |
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+ | Ovis1.6-Gemma2-27B (Open) | 63.5 | 70.1 | 54.1 | 86.6 | 85.6 | 68.0 | 82.2 | 72.9 |
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+ | Gemini-2.0-Flash | 69.4 | 70.4 | 58.0 | 83.1 | 82.5 | 73.6 | 71.0 | 72.6 |
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+ | GPT-4o-20241120 | 65.1 | 59.9 | 56.2 | 84.9 | 80.6 | 74.5 | 84.3 | 72.2 |
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+ | **Ovis1.6-Gemma2-9B (Open)** | 62.00 | 67.10 | 84.42 | 51.96 | 82.60 | 64.68 | 82.20 | 70.71 |
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+ |:---------:|:-----:|:------:|:-----:|:-----:|:----:|:-----:|:-----:|:-----:|
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+ | **Gukbap-Gemma2-9B-VL🍚** | 62.13 | 66.00 | 84.49 | 53.01 | 82.80 | 63.90 | 82.20 | **70.65** |
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+ |:---------:|:-----:|:------:|:-----:|:-----:|:----:|:-----:|:-----:|:-----:|
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+ | LLaVA-OneVision-72B | 65.8 | 68.4 | 47.9 | 86.2 | 74.1| 60.6 | 84.5 | 69.6 |
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+ | VARCO-VISION-14B (NCSoft) | 64.1 | 67.6 | 46.8 | 83.9 | 81.5 | 53.0 | 81.2 | 68.3 |
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+ | GPT-4o-mini-20240718 | 54.8 | 52.4 | 46.1 | 77.8 | 78.5 | 66.9 | 76.0 | 64.6 |
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+ > HallusionBench score: (aAcc + fAcc + qAcc) / 3
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+ ### Korean MM Benchmark Score (Zero-shot)
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+ We internally evaluated [our code](coming_soon).
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+ We utilized **gpt-4o-2024-08-06** in `K-LLAVA-W` evaluation.
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+ | Model | K-MMBench | K-MMStar | K-DTCBench | K-LLAVA-W | AVG |
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+ |:---------:|:-----:|:------:|:-----:|:-----:|:----:|
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+ | GPT-4o-20241120 | NaN | NaN | NaN | **85.50** | NaN |
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+ |:---------:|:-----:|:------:|:-----:|:-----:|:----:|
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+ | **Gukbap-Gemma2-9B-VL🍚** | 80.16 | 54.20 | 52.92 | **63.83** | 62.78 |
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+ | **Ovis1.6-Gemma2-9B** | 52.46 | 50.40 | 47.08 | 55.67 | 51.40 |
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+ | VARCO-VISION-14B | **87.16** | **58.13** | **85.42** | 51.17 | **70.47** |
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+ | llama-3.2-Korean-Bllossom-AICA-5B | 26.01 | 21.60 | 17.08 | 45.33 | 27.51 |
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+ ### MM Benchmarks
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+ - Global MM Bench dataset: [OpenCampass MM leaderboard](https://rank.opencompass.org.cn/leaderboard-multimodal)
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+ - Korean MM Bench dataset: [NCSOFT](https://huggingface.co/NCSOFT).
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+ ## Chat Prompt😶‍🌫️
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+ ```yaml
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+ <start_of_turn>user<image>
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+ Hello! My favorite food is Gukbap🍚!<end_of_turn>
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+ <start_of_turn>model
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+ (model answer)
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+ ```
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+ ## Gukbap-VL Series models🍚🍚
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+ - [HumanF-MarkrAI/Gukbap-Qwen2.5-34B-VL](https://huggingface.co/HumanF-MarkrAI/Gukbap-Qwen2.5-34B-VL)
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+ ## BibTeX
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+ ```
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+ @article{HumanF-MarkrAI,
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+ title={Gukbap-Gemma2-9B-VL},
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+ author={MarkrAI},
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+ year={2025},
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+ url={https://huggingface.co/HumanF-MarkrAI}
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+ }
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+ ```