# Fine-Tuned LLaMA-3-8B CEFR Model

This is a fine-tuned version of `unsloth/llama-3-8b-instruct-bnb-4bit` for CEFR-level sentence generation.

- **Base Model**: unsloth/llama-3-8b-instruct-bnb-4bit
- **Fine-Tuning**: LoRA with SMOTE-balanced dataset
- **Training Details**:
  - Dataset: CEFR-level sentences with SMOTE and undersampling for balance
  - LoRA Parameters: r=32, lora_alpha=32, lora_dropout=0.5
  - Training Args: learning_rate=2e-5, batch_size=8, epochs=0.1, cosine scheduler
  - Optimizer: adamw_8bit
  - Early Stopping: Patience=3, threshold=0.01
- **Evaluation Metrics**:
  - CEFR Classifier Accuracy: 0.250
  - Precision (Macro): 0.130
  - Recall (Macro): 0.250
  - F1-Score (Macro): 0.153
  - Perplexity: 14.218
  - Diversity (Unique Sentences): 0.933
  - Inference Time (ms): 2242.946
  - Model Size (GB): 4.8
  - Robustness (F1): 0.145
- **Confusion Matrix**:
  - CSV: [confusion_matrix.csv](confusion_matrix.csv)
  - Image: [confusion_matrix.png](confusion_matrix.png)
- **Per-Class Confusion Metrics**:
  - A1: TP=0, FP=2, FN=10, TN=48
  - A2: TP=0, FP=0, FN=10, TN=50
  - B1: TP=10, FP=29, FN=0, TN=21
  - B2: TP=2, FP=7, FN=8, TN=43
  - C1: TP=3, FP=7, FN=7, TN=43
  - C2: TP=0, FP=0, FN=10, TN=50
- **Usage**:
  ```python
  from transformers import AutoModelForCausalLM, AutoTokenizer

  model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test___01_withNewEval")
  tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test___01_withNewEval")

  # Example inference
  prompt = "<|user|>Generate a CEFR B1 level sentence.<|end|>"
  inputs = tokenizer(prompt, return_tensors="pt")
  outputs = model.generate(**inputs, max_length=50)
  print(tokenizer.decode(outputs[0], skip_special_tokens=True))
  ```

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