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--- |
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library_name: transformers |
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license: mit |
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datasets: |
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- emhaihsan/quran-indonesia-tafseer-translation |
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language: |
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- id |
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base_model: |
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- Qwen/Qwen2.5-3B-Instruct |
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--- |
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# Model Card for Fine-Tuned Qwen2.5-3B-Instruct |
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This is a fine-tuned version of the [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) model. The fine-tuning process utilized the [Quran Indonesia Tafseer Translation](https://huggingface.co/datasets/emhaihsan/quran-indonesia-tafseer-translation) dataset, which provides translations and tafsir in Bahasa Indonesia for the Quran. |
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## Model Details |
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### Model Description |
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- **Base Model:** [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) |
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- **Fine-Tuned By:** Ellbendl Satria |
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- **Dataset:** [emhaihsan/quran-indonesia-tafseer-translation](https://huggingface.co/datasets/emhaihsan/quran-indonesia-tafseer-translation) |
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- **Language:** Bahasa Indonesia |
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- **License:** MIT |
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This model is designed for NLP tasks involving Quranic text in Bahasa Indonesia, including understanding translations and tafsir. |
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## Uses |
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### Direct Use |
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This model can be used for applications requiring the understanding, summarization, or retrieval of Quranic translations and tafsir in Bahasa Indonesia. |
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### Downstream Use |
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It is suitable for fine-tuning on tasks such as: |
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- Quranic text summarization |
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- Question answering systems related to Islamic knowledge |
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- Educational tools for learning Quranic content in Indonesian |
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### Out-of-Scope Use |
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This model is not suitable for general-purpose conversation or tasks unrelated to Quranic and Islamic texts. |
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## Bias, Risks, and Limitations |
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### Biases |
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- The model inherits any biases present in the dataset, which is specific to Islamic translations and tafsir in Bahasa Indonesia. |
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### Limitations |
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- The model is tailored for Quranic and Islamic context, and its performance outside this domain may be suboptimal. |
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- It may not accurately handle nuanced or non-standard interpretations of Quranic text. |
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### Recommendations |
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- Users should ensure that applications using this model respect cultural and religious sensitivities. |
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- Results should be verified by domain experts for critical applications. |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran-GGUF") |
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model = AutoModelForCausalLM.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran-GGUF") |
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input_text = "Apa tafsir dari Surat Al-Fatihah ayat 1?" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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