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Model: akilx/qwen-english-mcq
Source: Original Platform

.gitattributes

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README.md

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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>
+```

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set current_content = message.content if message.content is not none else '' %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = current_content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (current_content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = current_content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151669,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.6.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 40960,
+  "pad_token_id": 151669,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.5.0"
+}

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+  "backend": "tokenizers",
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+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 40960,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "added_tokens_decoder": {
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