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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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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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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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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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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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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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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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[More Information Needed]
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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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- **Compute Region:** [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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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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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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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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datasets:
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- wikipedia
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- Ichsan2895/OASST_Top1_Indonesian
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- Ichsan2895/alpaca-gpt4-indonesian
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language:
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- id
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- en
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pipeline_tag: text-generation
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license: cc-by-nc-sa-4.0
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://huggingface.co/Ichsan2895/Merak-7B-v4/resolve/main/FINAL_LOGO/6.png" alt="MERAK" style="width: 50%; min-width: 100px; display: block; margin: auto;">
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</div>
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# THIS IS 1st PROTOTYPE OF MERAK-7B-v5!
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Merak-7B is the Large Language Model of Indonesian Language
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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.
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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..
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Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0) Merak-7B empowers AI enthusiasts, researchers alike.
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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.
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Feel free, to ask me about the model and please share the news on your social media.
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## CITATION
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```
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@software{lian2023mistralorca1
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title = {MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset},
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author = {Wing Lian and Bleys Goodson and Guan Wang and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"},
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year = {2023},
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publisher = {HuggingFace},
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journal = {HuggingFace repository},
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howpublished = {\url{https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca},
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}
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@misc{mukherjee2023orca,
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title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
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author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
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year={2023},
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eprint={2306.02707},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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@ONLINE{wikidump,
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author = "Wikimedia Foundation",
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title = "Wikimedia Downloads",
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url = "https://dumps.wikimedia.org"
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}
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@inproceedings{wolf-etal-2020-transformers,
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title = "Transformers: State-of-the-Art Natural Language Processing",
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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",
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booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
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month = oct,
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year = "2020",
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address = "Online",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
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pages = "38--45"
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}
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@misc{vonwerra2022trl,
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang},
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title = {TRL: Transformer Reinforcement Learning},
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year = {2020},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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@article{dettmers2023qlora,
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title = {QLoRA: Efficient Finetuning of Quantized LLMs},
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author = {Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
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journal = {arXiv preprint arXiv:2305.14314},
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year = {2023}
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}
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```
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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## HOW TO CITE THIS PROJECT
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If you use the Merak-7B model in your research or project, please cite it as:
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```
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@article{Merak,
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title={Merak-7B: The LLM for Bahasa Indonesia},
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author={Muhammad Ichsan},
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publisher={Hugging Face}
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journal={Hugging Face Repository},
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year={2023}
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}
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```
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