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Model: akilx/qwen-english-mcq Source: Original Platform
2026-07-29 05:21:10 +08:00
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+---
+license: mit
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- causal-lm
+- unsloth
+- dpo
+- sft
+- mcq-generator
+- education
+base_model: 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](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](https://doi.org/10.5281/zenodo.20533068)  
+**Zenodo Record:** [https://zenodo.org/records/20533068](https://zenodo.org/records/20533068)
+
+```bibtex
+@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
+```xml
+<context>
+[Input text segment from document]
+</context>
+<question>
+```
+
+---
+
+## Usage Example (Python)
+
+You can load and run this model locally using standard Hugging Face `transformers`:
+
+```python
+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
+```xml
+What is intelligence demonstrated by machines called?
+</question>
+<answer>
+Artificial intelligence
+</answer>
+<distractors>
+- Natural intelligence
+- Machine learning
+- Cognitive science
+</distractors>
+```