martinctl
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MNLP_M2_quantized_model

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 ---
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 ---
+license: apache-2.0
+base_model: Qwen/Qwen3-0.6B-Base
+tags:
+- fine-tuned
+- multiple-choice-qa
+- mcqa
+- question-answering
+- quantized
+- 8bit
+datasets:
+- custom-mcqa-dataset
+language:
+- en
+pipeline_tag: text-generation
 ---
+# MNLP_M2_quantized_model
+This model is a quantized fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) for Multiple Choice Question Answering (MCQA) tasks.
 ## Model Details
+- **Base Model**: Qwen/Qwen3-0.6B-Base
+- **Task**: Multiple Choice Question Answering
+- **Model Type**: Quantized
+- **Training Context**: Without context
+- **Evaluation Context**: Without context
+- **Fine-tuning Method**: Causal Language Modeling
+- **Quantization**: 8bit
 ## Training Details
+- **Epochs**: 5
+- **Learning Rate**: 5e-05
+- **Batch Size**: 2
+- **Training Framework**: Transformers + PyTorch
+- **Quantization Method**: 8bit
+- **Quantization Only**: No
+## Performance
+| Metric | Baseline | Fine-tuned | Improvement |
+|--------|----------|------------|-------------|
+| Accuracy | 69.66% | 70.68% | +1.02% |
+## Training Data
+The model was fine-tuned on a custom MCQA dataset with the following characteristics:
+- Format: Multiple choice questions with 4 options (A, B, C, D)
+- Context: Not included during training
+- Evaluation: Without context
+- Quantization: Applied during training and evaluation
+## Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("MNLP_M2_quantized_model", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("MNLP_M2_quantized_model", trust_remote_code=True)
+# For MCQA tasks, provide the question and options, then generate the answer
+prompt = "Question: What is the capital of France?\nA) London\nB) Berlin\nC) Paris\nD) Madrid\nAnswer:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=5)
+answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```