qwen-english-mcq/README.md

license, language, pipeline_tag, tags, base_model

license	language	pipeline_tag	tags	base_model
mit	en	text-generation	causal-lm unsloth dpo sft mcq-generator education	Qwen/Qwen3-1.7B

Qwen English MCQ Generator (Instruction-less) 📝

An optimized, instruction-less causal language model fine-tuned for high-quality Multiple Choice Question (MCQ) generation from English documents.

This model is based on Qwen/Qwen3-1.7B and has been aligned using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) via Unsloth.

Project Resources

• GitHub Repository: https://github.com/Akil-x/Doc-to-MCQ-Generator

Model Details

• Developed by: Akil Al-Sharafi, Jihad Nassari, Bashar Alwan, Adeeb Hassan, and Akram AL-subari
• Base Model: Qwen/Qwen3-1.7B
• Training Method: SFT (LoRA) + DPO (LoRA) via Unsloth
• Inference Mode: Causal Auto-Completion (Instruction-less / Prompt-less)
• Primary Domain: Educational Assessment / Automatic Exam Generation

Accompanying Research Paper

While this English model is developed as a sister model using a similar SFT + DPO fine-tuning pipeline, the core methodology, optimization frameworks, and evaluations are detailed in the accompanying research paper (which focuses primarily on the Arabic model):

Paper Title: Bridging the Performance Gap in Arabic MCQ Generation: Instruction-less Fine-Tuning of Compact 1.7B Models via DPO and RLAIF to Match Giant 120B LLMs
Preprint / DOI: 10.5281/zenodo.20533068
Zenodo Record: https://zenodo.org/records/20533068

@misc{alsharafi2026bridging,
  author       = {Al-Sharafi, Akil and Nassari, Jihad and Alwan, Bashar and Hassan, Adeeb and AL-subari, Akram},
  title        = {Bridging the Performance Gap in Arabic MCQ Generation: Instruction-less Fine-Tuning of Compact 1.7B Models via DPO and RLAIF to Match Giant 120B LLMs},
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.20533068},
  url          = {https://doi.org/10.5281/zenodo.20533068}
}

---

How It Works (Instruction-less Causal Auto-Completion)

Unlike general-purpose models that require natural language instructions (e.g. "Generate a question..."), this model acts as a pure causal text completer trained on custom XML tags. It expects the text context wrapped in <context>...</context> followed by the opening <question> tag. It will naturally autocomplete the MCQ sequence (Question, Answer, and Distractors) and stop generating at </distractors>.

Prompt Prefix Format

<context>
[Input text segment from document]
</context>
<question>

---

Usage Example (Python)

You can load and run this model locally using standard Hugging Face transformers:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "akilx/qwen-english-mcq"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_4bit=True  # Recommended for low VRAM
)

# Format the input prefix
context_text = "Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to natural intelligence."
prompt = f"<context>\n{context_text}\n</context>\n<question>\n"

inputs = tokenizer([prompt], return_tensors="pt").to("cuda")

# Generate completion
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        use_cache=True,
        stop_strings=["</distractors>"],
        tokenizer=tokenizer,
        temperature=0.1
    )

# Decode output
decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
generated_mcq = decoded.split("</context>")[-1].strip()
print(generated_mcq)

Expected Generated Output

What is intelligence demonstrated by machines called?
</question>
<answer>
Artificial intelligence
</answer>
<distractors>
- Natural intelligence
- Machine learning
- Cognitive science
</distractors>