normcqgen-model

NormCQGen Model - Norwegian MCQ Generator

This is a LoRA adapter fine-tuned on openai/gpt-oss-20b for generating Norwegian multiple-choice questions (MCQ). The model was trained using supervised fine-tuning (SFT) with the TRL library on the normcqgen-thinking dataset.

Model Details

Model Description

This LoRA adapter specializes in generating high-quality Norwegian multiple-choice questions suitable for educational assessments and quiz creation. The adapter applies selective fine-tuning to attention projection layers and Mixture-of-Experts (MoE) layers of the base model.

• Developed by: solarmar
• Model type: LoRA adapter for causal language modeling
• Language: Norwegian (Bokmål)
• License: Apache 2.0 (same as base model)
• Fine-tuned from: openai/gpt-oss-20b
• Adapter size: 143 MB

Model Architecture

LoRA Configuration:

• Rank (r): 16
• Alpha: 16
• Dropout: 0.05 (training), 0.0 (inference)
• Target Modules:
  • Attention projections: q_proj, k_proj, v_proj, o_proj
• Target Parameters (MoE expert layers):
  • Layer 15: mlp.experts.gate_up_proj, mlp.experts.down_proj
  • Layer 19: mlp.experts.gate_up_proj, mlp.experts.down_proj
  • Layer 21: mlp.experts.gate_up_proj, mlp.experts.down_proj
  • Layer 23: mlp.experts.gate_up_proj, mlp.experts.down_proj

Intended Uses

Direct Use

This model is designed to generate Norwegian multiple-choice questions for:

• Educational assessments
• Quiz creation
• Learning materials development
• Knowledge evaluation tasks

Out-of-Scope Use

• Generating content in languages other than Norwegian
• Non-MCQ text generation tasks
• Critical decision-making without human review
• Tasks requiring up-to-date factual information (model knowledge cutoff applies)

How to Use

Using PEFT (Recommended)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained(
    "openai/gpt-oss-20b",
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("openai/gpt-oss-20b")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "solarmar/normcqgen-model")

# Prepare input
messages = [{"role": "user", "content": "Generer et flervalgsspørsmål om fotosyntese."}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)

# Generate
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Using AutoPeftModel

from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM

# Load model with adapter
model = AutoPeftModelForCausalLM.from_pretrained(
    "solarmar/normcqgen-model",
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("solarmar/normcqgen-model")

# Generate
messages = [{"role": "user", "content": "Lag et spørsmål om norsk historie."}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was fine-tuned on solarmar/normcqgen-thinking, a dataset of Norwegian multiple-choice questions with reasoning traces.

Training Procedure

Training Hyperparameters:

• Training method: Supervised Fine-Tuning (SFT)
• Number of epochs: 2
• Per-device train batch size: 32
• Per-device eval batch size: 32
• Gradient accumulation steps: 4
• Effective batch size: 128 (32 × 4)
• Learning rate: 2e-4
• Weight decay: 0.01
• Warmup ratio: 0.03
• Max sequence length: 1024 tokens
• Logging steps: 5
• Evaluation steps: 10
• Save steps: 10

LoRA Hyperparameters:

• LoRA rank (r): 16
• LoRA alpha: 16
• LoRA dropout: 0.05
• Target modules: All attention projections (q, k, v, o)
• Target parameters: MoE expert layers at positions 15, 19, 21, 23

Training Results

The model achieved strong performance with 83.17% token-level accuracy while maintaining reasonable diversity in predictions (entropy: 0.748).

Framework Versions

• PEFT: 0.18.0
• TRL: 0.25.1
• Transformers: 4.57.1
• PyTorch: 2.5.1+cu121
• Datasets: 4.4.1
• Tokenizers: 0.22.1

Limitations and Bias

• The model is specialized for Norwegian language and may not perform well in other languages
• Performance depends on the quality and coverage of the training dataset
• May reflect biases present in the training data
• Generated questions should be reviewed by educators before use in formal assessments
• The model's knowledge is limited to the base model's training data cutoff

Citation

If you use this model, please cite:

@misc{normcqgen2024,
  author = {solarmar},
  title = {NormCQGen: Norwegian MCQ Generation Model},
  year = {2024},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/solarmar/normcqgen-model}}
}

TRL Citation

This model was trained using TRL (Transformer Reinforcement Learning):

@misc{vonwerra2022trl,
  title = {{TRL: Transformer Reinforcement Learning}},
  author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
  year = 2020,
  journal = {GitHub repository},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/huggingface/trl}}
}

Acknowledgments

• Base model: openai/gpt-oss-20b
• Training framework: TRL
• PEFT library: Hugging Face PEFT