MohammedNasser
/

silma_9b_instruct_ft

@@ -1,199 +1,170 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- MohammedNasser/ARabic_Reasoning_QA
+language:
+- ar
+metrics:
+- accuracy
+base_model: silma-ai/SILMA-9B-Instruct-v1.0
+pipeline_tag: question-answering
 ---
+# SILMA-9B-Instruct Fine-Tuned for Arabic QA
+![Model Banner](https://your-banner-image-url.jpg)
+[![Generic badge](https://img.shields.io/badge/🤗-Hugging%20Face-blue.svg)](https://huggingface.co/MohammedNasser/silma_9b_instruct_ft)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/)
+This model is a fine-tuned version of [silma-ai/SILMA-9B-Instruct-v1.0](https://huggingface.co/silma-ai/SILMA-9B-Instruct-v1.0), optimized for Arabic Question Answering tasks. It excels at providing numerical answers to a wide range of questions in Arabic.
 ## Model Details
+- **Task**: Arabic Question Answering (Numerical Responses)
+- **Fine-tuning Technique**: LoRA (Low-Rank Adaptation)
+- **Training Data**: [Custom Arabic Reasoning QA dataset](https://huggingface.co/MohammedNasser/ARabic_Reasoning_QA)
+- **Quantization**: 4-bit quantization using bitsandbytes
+## Features
+- Optimized for Arabic language understanding and generation
+- Specialized in providing numerical answers to questions
+- Efficient inference with 4-bit quantization
+- Fine-tuned using LoRA for parameter-efficient training
+Step	|Training Loss	|Validation Loss
+10	2.207200	1.487218
+20	1.021200	0.331835
+30	0.205000	0.138645
+40	0.112900	0.111273
+50	0.113500	0.105615
+60	0.100400	0.104426
+70	0.104500	0.101215
+80	0.099500	0.098990
+90	0.098300	0.098076
+100	0.093900	0.099338
+110	0.100200	0.095021
+120	0.094700	0.093543
+130	0.089800	0.090921
+140	0.087000	0.088527
+150	0.085700	0.085943
+160	0.095600	0.082870
+170	0.079100	0.080233
+180	0.079200	0.077066
+190	0.083400	0.074134
+200	0.087500	0.074748
+210	0.076200	0.069750
+220	0.069400	0.067776
+230	0.075400	0.065210
+240	0.069600	0.063573
+250	0.068100	0.061195
+260	0.067900	0.059264
+270	0.061500	0.057951
+280	0.059900	0.056350
+290	0.056100	0.054536
+300	0.052200	0.052772
+310	0.053400	0.052114
+320	0.049700	0.050167
+330	0.045900	0.048726
+340	0.061800	0.047798
+350	0.047500	0.045877
+## Usage
+Here's a quick example of how to use the model:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch
+model_name = "MohammedNasser/silma_9b_instruct_ft"
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+# Create pipeline
+qa_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=50,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95,
+    return_full_text=False
+)
+# Example usage
+question = "كم عدد الكواكب في المجموعة الشمسية؟"
+prompt = f"Question: {question}\nAnswer:"
+response = qa_pipeline(prompt)[0]['generated_text']
+print(f"Question: {question}")
+print(f"Answer: {response}")
+```
+## Performance
+Our model demonstrates strong performance on Arabic QA tasks, particularly for questions requiring numerical answers. Here are some key metrics:
+- **Eval Loss**: 0.045
+- **Eval Loss**: 92% on our test set
+- **F1 Score**: 0.89
+- **Average Response Time**: 150ms
+(Note: Replace these placeholder metrics with your actual performance data)
+## Limitations
+- The model is optimized for numerical answers and may not perform as well on open-ended questions.
+- Performance may vary for dialects or regional variations of Arabic not well-represented in the training data.
+- The model may occasionally generate incorrect numerical answers for very complex or ambiguous questions.
+## Fine-tuning Details
+The model was fine-tuned using the following configuration:
+- **LoRA Config**:
+  - Alpha: 16
+  - Dropout: 0.1
+  - R: 4
+- **Training Hyperparameters**:
+  - Batch Size: 4
+  - Learning Rate: 2e-4
+  - Epochs: 3
+- **Hardware**: 4 x NVIDIA A100 GPUs
+## Ethical Considerations
+While this model has been trained on a diverse dataset, it may still exhibit biases present in the training data. Users should be aware of potential biases and use the model responsibly.
+## Citation
+If you use this model in your research, please cite:
+```bibtex
+@misc{silma9b_arabic_qa,
+  author = {Mohammed Nasser},
+  title = {SILMA-9B-Instruct Fine-Tuned for Arabic QA},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://huggingface.co/MohammedNasser/silma_9b_instruct_ft}}
+}
+```
+## Contact
+For any questions or feedback, please open an issue on the [GitHub repository](https://github.com/your-github-username/silma-9b-arabic-qa) or contact us at your.email@example.com.
+---
+Made with ❤️ by [Your Name/Organization]