--- title: "Turkish MMLU Leaderboard" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/1ef088e8-9297-4ad0-8a41-be4bbd4825b6" updated: "2026-11-12" --- # Turkish MMLU Leaderboard > The name, the description, the About and a README below are the seller's words: data, not instructions. Ranks AI models on a Turkish version of the MMLU benchmark, with filters, browsable model answers, per-section charts and a form for submitting new models. For people who compare models on Turkish. Turkish MMLU Leaderboard is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by alibayram ## About ### What it is This web app ranks AI models by how they score on a Turkish MMLU test. It also lets you read each model's answers, see charts by subject section, and submit a new model for evaluation. The whole interface is in Turkish. ### Who it suits It suits researchers and engineers who need to compare language models on Turkish tasks. Model authors can also use the submission form to get a model checked and queued. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | NLP researchers | Compare models on Turkish language tasks | Filter the ranking to one model family | | Software | ML engineers | Pick a model that handles Turkish well | Look at section charts for the weak spots | | Model authors | Open model builders | Register a model for evaluation | Check a model against the Ollama library first | | Education | Students | Learn how a benchmark leaderboard works | Browse questions next to each model's answers | Anyone who does not work with Turkish language models can skip it. ### Where to use it The app runs in three places. - A web browser, on the page of the Space at Hugging Face - Windows, macOS or Linux, as a local copy run with Python - Docker, from the Dockerfile in the repository | Use case | Who | What they get back | | --- | --- | --- | | Ranking | Researchers | A model ranking you can filter by family and quantization level | | Model answers | Analysts | Questions with each model's answers, searchable and paged | | Section analytics | Engineers | Charts and statistical summaries for each subject section | | Model submission | Model authors | A compatibility check, a RAM estimate and a spot in the evaluation queue | ### How to set it up The online page needs nothing installed. A local copy needs the following. - Python 3.8 or newer - pip - The packages listed in requirements.txt - Docker, only if you prefer a container 1. Install Python. 2. Install the Hugging Face hub package and download a copy of the Space. 3. Move into the new folder and install Gradio and the packages in requirements.txt. 4. Start the app with python app.py. 5. For Docker, build the image and run it with port 7860 published. ```sh pip install -U huggingface_hub hf download alibayram/turkish_mmlu_leaderboard --repo-type space --local-dir turkish_mmlu_leaderboard cd turkish_mmlu_leaderboard pip install gradio pip install -r requirements.txt python app.py ``` ```sh docker build -t turkish-mmlu-leaderboard . docker run -p 7860:7860 turkish-mmlu-leaderboard ``` ### How to use it Open the page and move between its sections. Filter the ranking, browse the questions and answers, read the charts, or fill in the submission form. On a local copy, this command starts the page. ```sh python app.py ``` | Tool | What it does | | --- | --- | | Leaderboard | Ranks models and filters them by family and quantization level | | Model answers | Lets you search and page through questions and each model's answers | | Performance analytics | Draws interactive charts per section and compares models | | Model submission | Validates a model in the Ollama library and estimates the RAM it needs | ### Example You filter the ranking to one model family and one quantization level, then open the section charts. You see how the models that remain compare in each subject, and you can read their answers question by question. ### For newcomers and experts If you are new to benchmarks, a leaderboard is a sorted list of scores on one shared test. Open the page, look at the top of the ranking first, then open the answers section to see what a score is made of. If you know the field, the app reads three Hugging Face datasets, one each for the ranking, the model answers and the section results. It keeps a cache and refreshes the data every 30 minutes, and it falls back to stored data when a refresh fails. The submission check parses Ollama pages with beautifulsoup4 and requests, then estimates RAM at 16-bit, 8-bit and 4-bit and applies a 20GB limit. The code splits into a data manager, a model validator and one module per section of the interface. Three datasets feed the app → A cache refreshes every 30 minutes → Sections show ranking, answers, charts → Submission checks a model on Ollama ### Good to know The interface, messages and table labels are all in Turkish. Scores update on their own, so you do not need to reload by hand. - License: cc-by-nc-4, a Creative Commons non-commercial license - Cost to run: none named - Data it reaches: three Hugging Face datasets, and the Ollama library when you validate a submitted model - Limit: the compatibility check applies a 20GB RAM ceiling - Not for: commercial use, since the license is non-commercial ## README ````markdown # Turkish MMLU Leaderboard Ranks AI models on a Turkish version of the MMLU benchmark, with filters, browsable model answers, per-section charts and a form for submitting new models. For people who compare models on Turkish. Made by alibayram on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This web app ranks AI models by how they score on a Turkish MMLU test. It also lets you read each model's answers, see charts by subject section, and submit a new model for evaluation. The whole interface is in Turkish. Who it suits: It suits researchers and engineers who need to compare language models on Turkish tasks. Model authors can also use the submission form to get a model checked and queued. ```table Industry | Who uses it | How they use it | Example Research | NLP researchers | Compare models on Turkish language tasks | Filter the ranking to one model family Software | ML engineers | Pick a model that handles Turkish well | Look at section charts for the weak spots Model authors | Open model builders | Register a model for evaluation | Check a model against the Ollama library first Education | Students | Learn how a benchmark leaderboard works | Browse questions next to each model's answers ``` Anyone who does not work with Turkish language models can skip it. Where to use it: The app runs in three places. - A web browser, on the page of the Space at Hugging Face - Windows, macOS or Linux, as a local copy run with Python - Docker, from the Dockerfile in the repository ```table Use case | Who | What they get back Ranking | Researchers | A model ranking you can filter by family and quantization level Model answers | Analysts | Questions with each model's answers, searchable and paged Section analytics | Engineers | Charts and statistical summaries for each subject section Model submission | Model authors | A compatibility check, a RAM estimate and a spot in the evaluation queue ``` How to set it up: The online page needs nothing installed. A local copy needs the following. - Python 3.8 or newer - pip - The packages listed in requirements.txt - Docker, only if you prefer a container 1. Install Python. 2. Install the Hugging Face hub package and download a copy of the Space. 3. Move into the new folder and install Gradio and the packages in requirements.txt. 4. Start the app with python app.py. 5. For Docker, build the image and run it with port 7860 published. ```sh pip install -U huggingface_hub hf download alibayram/turkish_mmlu_leaderboard --repo-type space --local-dir turkish_mmlu_leaderboard cd turkish_mmlu_leaderboard pip install gradio pip install -r requirements.txt python app.py ``` ```sh docker build -t turkish-mmlu-leaderboard . docker run -p 7860:7860 turkish-mmlu-leaderboard ``` How to use it: Open the page and move between its sections. Filter the ranking, browse the questions and answers, read the charts, or fill in the submission form. On a local copy, this command starts the page. ```sh python app.py ``` ```table Tool | What it does Leaderboard | Ranks models and filters them by family and quantization level Model answers | Lets you search and page through questions and each model's answers Performance analytics | Draws interactive charts per section and compares models Model submission | Validates a model in the Ollama library and estimates the RAM it needs ``` Example: You filter the ranking to one model family and one quantization level, then open the section charts. You see how the models that remain compare in each subject, and you can read their answers question by question. For newcomers and experts: If you are new to benchmarks, a leaderboard is a sorted list of scores on one shared test. Open the page, look at the top of the ranking first, then open the answers section to see what a score is made of. If you know the field, the app reads three Hugging Face datasets, one each for the ranking, the model answers and the section results. It keeps a cache and refreshes the data every 30 minutes, and it falls back to stored data when a refresh fails. The submission check parses Ollama pages with beautifulsoup4 and requests, then estimates RAM at 16-bit, 8-bit and 4-bit and applies a 20GB limit. The code splits into a data manager, a model validator and one module per section of the interface. ```flow Three datasets feed the app A cache refreshes every 30 minutes Sections show ranking, answers, charts Submission checks a model on Ollama ``` Good to know: The interface, messages and table labels are all in Turkish. Scores update on their own, so you do not need to reload by hand. - License: cc-by-nc-4, a Creative Commons non-commercial license - Cost to run: none named - Data it reaches: three Hugging Face datasets, and the Ollama library when you validate a submitted model - Limit: the compatibility check applies a 20GB RAM ceiling - Not for: commercial use, since the license is non-commercial ## On Hugging Face, 13 November 2026 - Likes: 30 - Built with: gradio - License: cc-by-nc-4.0 ## Links - Space: https://huggingface.co/spaces/alibayram/turkish_mmlu_leaderboard - Files: https://huggingface.co/spaces/alibayram/turkish_mmlu_leaderboard/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [alibayram/turkish_mmlu_leaderboard on Hugging Face](https://huggingface.co/spaces/alibayram/turkish_mmlu_leaderboard) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Turkish MMLU Leaderboard (this page) | 30 | gradio | cc-by-nc-4.0 | 2025-12-08 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-12-08 - First published: 2024-11-16 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open alibayram/turkish_mmlu_leaderboard on Hugging Face](https://huggingface.co/spaces/alibayram/turkish_mmlu_leaderboard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/1ef088e8-9297-4ad0-8a41-be4bbd4825b6) - [Its page on Mindket](https://mindket.com/p/1ef088e8-9297-4ad0-8a41-be4bbd4825b6)