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# 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: 0.167
- Precision (Macro): 0.042
- Recall (Macro): 0.167
- F1-Score (Macro): 0.067
- Perplexity: 14.218
- Diversity (Unique Sentences): 1.000
- Inference Time (ms): 2216.789
- Model Size (GB): 4.8
- Robustness (F1): 0.063
- **Usage**:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test___01")
tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test___01")
# 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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