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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- text-generation |
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- question-answering |
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- mcqa |
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- merged |
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- sft |
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- lora |
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base_model: AnnaelleMyriam/SFT_M3_model |
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--- |
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# MNLP M3 MCQA Merged Model |
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This model is a merged version of: |
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- **Base SFT Model**: `AnnaelleMyriam/SFT_M3_model` |
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- **LoRA Adapter**: `aymanbakiri/MNLP_M3_mcqa_model_adapter` |
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## Model Description |
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This is a specialized model for Multiple Choice Question Answering (MCQA) tasks, created by: |
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1. Starting with the SFT model `AnnaelleMyriam/SFT_M3_model` |
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2. Fine-tuning with LoRA adapters on MCQA data |
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3. Merging the LoRA weights back into the base model |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("aymanbakiri/MNLP_M3_mcqa_model_test") |
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tokenizer = AutoTokenizer.from_pretrained("aymanbakiri/MNLP_M3_mcqa_model_test") |
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# Example usage for MCQA |
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prompt = """Question: What is the capital of France? |
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Options: (A) London (B) Berlin (C) Paris (D) Madrid |
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Answer:""" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=5) |
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answer = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(answer) |
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``` |
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## Training Details |
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- Base Model: SFT model fine-tuned for instruction following |
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- LoRA Configuration: r=16, alpha=32, dropout=0.1 |
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- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, lm_head |
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- Training Data: MNLP M2 MCQA Dataset |
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## Performance |
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This merged model should provide better performance than the original LoRA adapter while being easier to deploy and use. |
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