Fine-Tuned LLaMA-3-8B CEFR Model

• Base Model: unsloth/gemma-7b-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=0.01, cosine scheduler
  • Optimizer: adamw_8bit
  • Early Stopping: Patience=3, threshold=0.01
• Evaluation Metrics (Exact Matches):
  • CEFR Classifier Accuracy: 0.167
  • Precision (Macro): 0.028
  • Recall (Macro): 0.167
  • F1-Score (Macro): 0.048
• Evaluation Metrics (Within ±1 Level):
  • CEFR Classifier Accuracy: 0.500
  • Precision (Macro): 0.375
  • Recall (Macro): 0.500
  • F1-Score (Macro): 0.400
• Other Metrics:
  • Perplexity: 5.344
  • Diversity (Unique Sentences): 0.100
  • Inference Time (ms): 5802.883
  • Model Size (GB): 4.8
  • Robustness (F1): 0.045
• Confusion Matrix (Exact Matches):
  • CSV: confusion_matrix_exact.csv
  • Image: confusion_matrix_exact.png
• Confusion Matrix (Within ±1 Level):
  • CSV: confusion_matrix_within1.csv
  • Image: confusion_matrix_within1.png
• Per-Class Confusion Metrics (Exact Matches):
  • A1: TP=0, FP=0, FN=10, TN=50
  • A2: TP=10, FP=50, FN=0, TN=0
  • B1: TP=0, FP=0, FN=10, TN=50
  • B2: TP=0, FP=0, FN=10, TN=50
  • C1: TP=0, FP=0, FN=10, TN=50
  • C2: TP=0, FP=0, FN=10, TN=50
• Per-Class Confusion Metrics (Within ±1 Level):
  • A1: TP=10, FP=0, FN=0, TN=50
  • A2: TP=10, FP=30, FN=0, TN=20
  • B1: TP=10, FP=0, FN=0, TN=50
  • B2: TP=0, FP=0, FN=10, TN=50
  • C1: TP=0, FP=0, FN=10, TN=50
  • C2: TP=0, FP=0, FN=10, TN=50
• Usage:
  python

  1from transformers import AutoModelForCausalLM, AutoTokenizer 2 3model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test_02_llama_trainPercen_myValidator_2ndTry") 4tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test_02_llama_trainPercen_myValidator_2ndTry") 5 6# Example inference 7prompt = "<|user|>Generate a CEFR B1 level sentence.<|end|>" 8inputs = tokenizer(prompt, return_tensors="pt") 9outputs = model.generate(**inputs, max_length=50) 10print(tokenizer.decode(outputs[0], skip_special_tokens=True))