safetensors llama qa llm exam multiple-choice question-answering en dataset:rajpurkar/squad dataset:ehovy/race base_model:meta-llama/Llama-3.2-3B-Instruct base_model:finetune:meta-llama/Llama-3.2-3B-Instruct license:apache-2.0 model-index region:us
---
base_model: meta-llama/Llama-3.2-3B-Instruct
datasets:
- rajpurkar/squad
- ehovy/race
language:
- en
license: apache-2.0
metrics:
- accuracy
- bleu
- rouge
pipeline_tag: question-answering
tags:
- qa
- llm
- exam
- multiple-choice
model-index:
- name: Llama-3B-QA-Enhanced
  results:
  - task:
      type: question-answering
      name: Multiple Choice Question Generation
    dataset:
      name: RACE
      type: ehovy/race
    metrics:
    - type: accuracy
      value: 0.85
      name: accuracy
    - type: bleu
      value: 0.76
      name: bleu
    - type: rouge
      value: 0.82
      name: rouge
---

# Model Card for Llama-3B-QA-Enhanced

# Model Card for Llama-3B-QA-Enhanced

This model is a fine-tuned version of Llama 3B, optimized for generating high-quality multiple-choice questions (MCQs) from input text. It combines the powerful language understanding capabilities of Llama with specialized training for educational content generation.

## Model Details

### Model Description

This model is designed to automatically generate multiple-choice questions from input text, making it particularly useful for educators, content creators, and educational technology platforms.

- **Developed by:** Ahmed Othman
- **Model type:** Fine-tuned Language Model
- **Language(s):** English
- **License:** Apache 2.0
- **Finetuned from model:** meta-llama/Llama-3.2-3B-Instruct

## Uses

### Direct Use

The model can be used directly for:
- Generating multiple-choice questions from educational texts
- Creating assessment materials
- Automated quiz generation
- Educational content development

```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("AhmedOthman/Llama-3B-QA-Enhanced")
tokenizer = AutoTokenizer.from_pretrained("AhmedOthman/Llama-3B-QA-Enhanced")

text = "Your input text here"
inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
outputs = model.generate(inputs.input_ids)
mcq = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

### Out-of-Scope Use

This model should not be used for:
- Generating factually incorrect or misleading questions
- Creating questions about sensitive or controversial topics
- Replacing human expertise in high-stakes assessment development

## Training Details

### Training Data

The model was trained on a combination of:
- SQuAD (Stanford Question Answering Dataset)
- RACE (ReAding Comprehension from Examinations)

### Training Procedure

#### Training Hyperparameters

- **Training regime:** fp16 mixed precision
- **Maximum sequence length:** 512 tokens
- **Learning rate:** 2e-5
- **Batch size:** 16
- **Number of epochs:** 3

## Evaluation

### Metrics

The model was evaluated using:
- BLEU score for question generation quality
- ROUGE score for answer relevance
- Accuracy of generated distractors
- Human evaluation for question quality

## Limitations and Bias

- Limited to English language content
- May generate simpler questions for complex topics
- Performance varies with input text quality
- May reflect biases present in training data

## Environmental Impact

- **Base Model:** Llama 3B
- **Fine-tuning Hardware:** Single A100 GPU
- **Training Time:** Approximately 8 hours

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{othman2024llama3bqa,
  author = {Othman, Ahmed},
  title = {Llama-3B-QA-Enhanced},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/AhmedOthman/Llama-3B-QA-Enhanced}}
}
```

## Model Card Contact

For questions or issues, please contact Ahmed Othman through the HuggingFace model repository.
```

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