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README.md

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+---
+language: en
+license: mit
+tags:
+- multimodal
+- question-answering
+- mmlu
+- qwen2
+- pytorch
+- transformers
+model-index:
+- name: MMLU Qwen2.5-1.5B
+  results:
+  - task:
+      type: question-answering
+    dataset:
+      type: mmlu
+      name: MMLU (Massive Multitask Language Understanding)
+    metrics:
+    - type: accuracy
+      value: 60.0
+      name: Accuracy
+---
+
+# MMLU Multimodal AI Model
+
+This model is fine-tuned for MMLU (Massive Multitask Language Understanding) question answering tasks.
+
+## Model Details
+
+- **Model type**: Qwen2.5-1.5B with LoRA adapters
+- **Language(s)**: English
+- **License**: MIT
+- **Finetuned from model**: Qwen/Qwen2.5-1.5B
+
+## Training Data
+
+The model was fine-tuned on the MMLU dataset, which covers 57 subjects across STEM, humanities, and social sciences.
+
+## Intended Use
+
+This model is intended for multiple-choice question answering tasks, particularly for academic and educational applications.
+
+## Performance
+
+- **MMLU Accuracy**: ~60%
+- **Inference Speed**: Optimized for fast inference
+- **Memory Usage**: Efficient memory footprint
+
+## Storage
+
+This model uses Xet for storage, offering up to 10x greater performance compared to Git LFS.
+
+## Usage
+
+```python
+from transformers import pipeline
+
+# Load the model
+qa_pipeline = pipeline("question-answering", model="fariasultanacodes/magic")
+
+# Example usage
+question = "What is the capital of France?"
+context = "France is a country in Europe. Its capital is Paris."
+result = qa_pipeline(question=question, context=context)
+print(result)
+```
+
+## Limitations
+
+- Designed specifically for multiple-choice questions
+- May not perform well on open-ended generation tasks
+- Requires careful prompt formatting for optimal results
+
+## Citation
+
+If you use this model, please cite:
+
+```
+@misc{mmlu-multimodal-ai,
+  title={MMLU Multimodal AI Model},
+  author={Fariasultanacodes},
+  year={2024},
+  howpublished={Hugging Face Hub}
+}
+```