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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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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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- [More Information Needed]
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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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- <!-- 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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- #### 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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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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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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- [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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- ### Compute Infrastructure
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- #### Hardware
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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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- <!-- 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 [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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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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  ---
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+ ```