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README.md

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+---
+language: en
+license: apache-2.0
+tags:
+- text-generation
+- question-answering
+- mcqa
+- merged
+- sft
+- lora
+base_model: AnnaelleMyriam/SFT_M3_model
+---
+
+# MNLP M3 MCQA Merged Model
+
+This model is a merged version of:
+- **Base SFT Model**: `AnnaelleMyriam/SFT_M3_model`
+- **LoRA Adapter**: `aymanbakiri/MNLP_M3_mcqa_model_test`
+
+## Model Description
+
+This is a specialized model for Multiple Choice Question Answering (MCQA) tasks, created by:
+1. Starting with the SFT model `AnnaelleMyriam/SFT_M3_model`
+2. Fine-tuning with LoRA adapters on MCQA data
+3. Merging the LoRA weights back into the base model
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("aymanbakiri/MNLP_M3_mcqa_merged_model_test")
+tokenizer = AutoTokenizer.from_pretrained("aymanbakiri/MNLP_M3_mcqa_merged_model_test")
+
+# Example usage for MCQA
+prompt = """Question: What is the capital of France?
+Options: (A) London (B) Berlin (C) Paris (D) Madrid
+Answer:"""
+
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=5)
+answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(answer)
+```
+
+## Training Details
+
+- Base Model: SFT model fine-tuned for instruction following
+- LoRA Configuration: r=16, alpha=32, dropout=0.1
+- Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, lm_head
+- Training Data: MNLP M2 MCQA Dataset
+
+## Performance
+
+This merged model should provide better performance than the original LoRA adapter while being easier to deploy and use.