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README.md
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library_name: transformers
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- unsloth
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---
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# Model
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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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###
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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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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[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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#### 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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**APA:**
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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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## Model Card Contact
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[More Information Needed]
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library_name: transformers
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tags:
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- unsloth
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license: gpl-3.0
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language:
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- id
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base_model:
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- meta-llama/Llama-3.2-3B-Instruct
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pipeline_tag: question-answering
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# Model Chatbot Pegon.ai
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## Gambaran Model
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Model ini adalah AI percakapan yang dirancang untuk membantu memahami dan menghasilkan teks terkait aksara Pegon, sebuah varian aksara Arab yang digunakan untuk menulis bahasa Jawa, sunda, dan madura, Khususnya dalam konteks teks-teks Islami. Chatbot ini dilatih untuk merespons pertanyaan dan memberikan informasi mengenai huruf Pegon dan penggunaannya.
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Model ini dapat:
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- Memberikan informasi tentang sejarah dan struktur aksara Pegon.
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- Menjelaskan aturan dan pola dalam ortografi Pegon.
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## Penggunaan yang Dimaksud
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Model ini dirancang untuk digunakan dalam lingkungan pendidikan, proyek penelitian, atau untuk pertanyaan umum mengenai aksara Pegon. Model ini dapat membantu pelajar bahasa, ahli bahasa, dan peneliti untuk mengeksplorasi nuansa penulisan Pegon. Anda dapat menggunakannya untuk bertanya tentang karakter, kata, atau topik umum yang berkaitan dengan aksara Pegon.
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### Contoh Pertanyaan:
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- "Apa perbedaan antara Pegon dan aksara Jawa?"
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- "Siapa penemu huruf Pegon?"
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- "Kenapa huruf pegon diciptakan?"
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## Detail Model
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- **Tipe Model**: Chatbot
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- **Bahasa**: Bahasa Indonesia (dengan pemahaman terhadap aksara Pegon dan beberapa bahasa terkait seperti Jawa dan Arab)
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- **Data Latih**: Model ini telah di-fine-tune menggunakan dataset teks aksara Pegon dan sumber daya linguistik terkait.
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- **Versi**: 1.0.0
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## Penggunaan
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Anda dapat berinteraksi dengan model ini menggunakan library `transformers` dari Hugging Face.
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### Contoh Kode:
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```python
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import torch
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from transformers import pipeline
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model_id = "kucingcoder/Capstone-Chatbot-Pegon.ai"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype="auto",
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are an experienced customer support person named Tono.\nBe polite to users and answer all Pegon related queries."},
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{"role": "user", "content": "Apa itu huruf pegon?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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