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---
library_name: transformers
license: mit
datasets:
- emhaihsan/quran-indonesia-tafseer-translation
language:
- id
base_model:
- Qwen/Qwen2.5-3B-Instruct
---
# Model Card for Fine-Tuned Qwen2.5-3B-Instruct
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.
## Model Details
### Model Description
- **Base Model:** [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
- **Fine-Tuned By:** Ellbendl Satria
- **Dataset:** [emhaihsan/quran-indonesia-tafseer-translation](https://huggingface.co/datasets/emhaihsan/quran-indonesia-tafseer-translation)
- **Language:** Bahasa Indonesia
- **License:** MIT
This model is designed for NLP tasks involving Quranic text in Bahasa Indonesia, including understanding translations and tafsir.
## Uses
### Direct Use
This model can be used for applications requiring the understanding, summarization, or retrieval of Quranic translations and tafsir in Bahasa Indonesia.
### Downstream Use
It is suitable for fine-tuning on tasks such as:
- Quranic text summarization
- Question answering systems related to Islamic knowledge
- Educational tools for learning Quranic content in Indonesian
### Biases
- The model inherits any biases present in the dataset, which is specific to Islamic translations and tafsir in Bahasa Indonesia.
### Recommendations
- Users should ensure that applications using this model respect cultural and religious sensitivities.
- Results should be verified by domain experts for critical applications.
## How to Get Started with the Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran")
model = AutoModelForCausalLM.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran")
# Move the model to GPU
model.to("cuda")
# Define the input message
messages = [
{
"role": "user",
"content": "Tafsirkan ayat ini اِهْدِنَا الصِّرَاطَ الْمُسْتَقِيْمَۙ"
}
]
# Generate the prompt using the tokenizer
prompt = tokenizer.apply_chat_template(messages, tokenize=False,
add_generation_prompt=True)
# Tokenize the prompt and move inputs to GPU
inputs = tokenizer(prompt, return_tensors='pt', padding=True,
truncation=True).to("cuda")
# Generate the output using the model
outputs = model.generate(**inputs, max_length=150,
num_return_sequences=1)
# Decode the output
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Print the result
print(text.split("assistant")[1])
``` |