--- language: - id - en license: cc-by-nc-sa-4.0 datasets: - wikipedia - Ichsan2895/OASST_Top1_Indonesian - Ichsan2895/alpaca-gpt4-indonesian pipeline_tag: text-generation model-index: - name: Merak-7B-v5-PROTOTYPE1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 62.2 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ichsan2895/Merak-7B-v5-PROTOTYPE1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 82.07 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ichsan2895/Merak-7B-v5-PROTOTYPE1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 60.97 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ichsan2895/Merak-7B-v5-PROTOTYPE1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 45.41 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ichsan2895/Merak-7B-v5-PROTOTYPE1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 77.9 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ichsan2895/Merak-7B-v5-PROTOTYPE1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 37.23 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Ichsan2895/Merak-7B-v5-PROTOTYPE1 name: Open LLM Leaderboard ---
MERAK
# THIS IS 1st PROTOTYPE OF MERAK-7B-v5! Merak-7B is the Large Language Model of Indonesian Language This model is based on [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) and fine tuned by some of Indonesia Wikipedia articles that I cleaned before. Leveraging QLoRA (QLora: Efficient Finetuning of Quantized LLMs), Merak-7B is able to run with 16 GB VRAM. We also use DPO-Trainer for RLHF with TRL library.. Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0) Merak-7B empowers AI enthusiasts, researchers alike. Big thanks to all my friends and communities that help to build our first model. Thanks for Axolotl for a great fine tuning tool which designed to streamline the fine-tuning of various AI models. Feel free, to ask me about the model and please share the news on your social media. ## CITATION ``` @software{lian2023mistralorca1 title = {MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset}, author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca}, } @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } @ONLINE{wikidump, author = "Wikimedia Foundation", title = "Wikimedia Downloads", url = "https://dumps.wikimedia.org" } @inproceedings{wolf-etal-2020-transformers, title = "Transformers: State-of-the-Art Natural Language Processing", author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", month = oct, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", pages = "38--45" } @misc{vonwerra2022trl, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang}, title = {TRL: Transformer Reinforcement Learning}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/huggingface/trl}} } @article{dettmers2023qlora, title = {QLoRA: Efficient Finetuning of Quantized LLMs}, author = {Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke}, journal = {arXiv preprint arXiv:2305.14314}, year = {2023} } ``` [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) ## HOW TO CITE THIS PROJECT If you use the Merak-7B model in your research or project, please cite it as: ``` @article{Merak, title={Merak-7B: The LLM for Bahasa Indonesia}, author={Muhammad Ichsan}, publisher={Hugging Face} journal={Hugging Face Repository}, year={2023} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Ichsan2895__Merak-7B-v5-PROTOTYPE1) | Metric |Value| |---------------------------------|----:| |Avg. |60.96| |AI2 Reasoning Challenge (25-Shot)|62.20| |HellaSwag (10-Shot) |82.07| |MMLU (5-Shot) |60.97| |TruthfulQA (0-shot) |45.41| |Winogrande (5-shot) |77.90| |GSM8k (5-shot) |37.23|