README.md

        num_train_epochs=0.1,

# 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
- **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))
  ```

Uploaded using `huggingface_hub`.


import unsloth
from unsloth import FastLanguageModel, is_bfloat16_supported
import torch
import pandas as pd
from datasets import Dataset
from sklearn.utils import resample
from transformers import Trainer, TrainingArguments, EarlyStoppingCallback, AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer
from sentence_transformers import SentenceTransformer
from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.pipeline import Pipeline
import numpy as np
import wandb
import os
from huggingface_hub import create_repo, upload_folder

# Verify environment
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")

# Cell 1: Load model and tokenizer
max_seq_length = 2048
dtype = None
load_in_4bit = True

try:
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name="unsloth/llama-3-8b-instruct-bnb-4bit",
        max_seq_length=max_seq_length,
        dtype=dtype,
        load_in_4bit=load_in_4bit,
        use_exact_model_name=True,
        device_map="auto"
    )
    print("Model and tokenizer loaded successfully with Unsloth!")
except Exception as e:
    print(f"Error loading model with Unsloth: {e}")
    print("Falling back to transformers...")
    model_name = "unsloth/llama-3-8b-instruct-bnb-4bit"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        load_in_4bit=True,
        device_map="auto"
    )
    print("Model and tokenizer loaded with transformers!")

# Cell 2: Configure LoRA
try:
    model = FastLanguageModel.get_peft_model(
        model,
        r=32,
        target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
        lora_alpha=32,
        lora_dropout=0.5,
        bias="none",
        use_gradient_checkpointing="unsloth",
        random_state=3407,
    )
    print("LoRA configuration applied successfully!")
except Exception as e:
    print(f"Error applying LoRA: {e}")
    raise

# Cell 3: Load datasets
train_file = "train_merged_output.txt"
val_file = "dev_merged_output.txt"
test_file = "test_merged_output.txt"

cefr_mapping = {1: "A1", 2: "A2", 3: "B1", 4: "B2", 5: "C1", 6: "C2"}

def load_and_reformat(file_path):
    try:
        with open(file_path, "r") as f:
            lines = f.readlines()
        reformatted_data = []
        for line in lines:
            parts = line.strip().split("\t")
            sentence = parts[0]
            levels = parts[1:]
            for level in levels:
                level_int = int(level)
                cefr_level = cefr_mapping.get(level_int, "Unknown")
                reformatted_data.append({"sentence": sentence, "level": cefr_level})
        return pd.DataFrame(reformatted_data)
    except Exception as e:
        print(f"Error loading file {file_path}: {e}")
        raise

train_dataset = load_and_reformat(train_file)
val_dataset = load_and_reformat(val_file)
test_dataset = load_and_reformat(test_file)

print("Train dataset - Column names:", train_dataset.columns.tolist())
print("Train dataset - First 5 rows:\n", train_dataset.head())
print("Validation dataset - First 5 rows:\n", val_dataset.head())
print("Test dataset - First 5 rows:\n", test_dataset.head())

expected_columns = {"sentence", "level"}
for name, dataset in [("Train", train_dataset), ("Validation", val_dataset), ("Test", test_dataset)]:
    if not expected_columns.issubset(dataset.columns):
        missing = expected_columns - set(dataset.columns)
        print(f"Warning: {name} dataset missing expected columns: {missing}")

# Cell 4: Rename columns
column_mapping = {"sentence": "sentence", "level": "level"}
train_dataset = train_dataset.rename(columns=column_mapping)
val_dataset = val_dataset.rename(columns=column_mapping)
test_dataset = test_dataset.rename(columns=column_mapping)

print("Train dataset - Renamed column names:", train_dataset.columns.tolist())
print("Train dataset - First row after renaming:\n", train_dataset.head(1))

# Cell 5: Convert to HF Dataset and format
train_dataset_hf = Dataset.from_pandas(train_dataset)
val_dataset_hf = Dataset.from_pandas(val_dataset)
test_dataset_hf = Dataset.from_pandas(test_dataset)

def format_func(example):
    return {
        "text": (
            f"<|user|>\nGenerate a CEFR {example['level']} level sentence.<|end|>\n"
            f"<|assistant|>\n{example['sentence']}<|end|>\n"
        ),
        "level": example['level']
    }

train_dataset_transformed = train_dataset_hf.map(format_func)
val_dataset_transformed = val_dataset_hf.map(format_func)
test_dataset_transformed = test_dataset_hf.map(format_func)

train_dataset_transformed = train_dataset_transformed.remove_columns(['sentence'])
val_dataset_transformed = val_dataset_transformed.remove_columns(['sentence'])
test_dataset_transformed = test_dataset_transformed.remove_columns(['sentence'])

print("Train dataset columns after transformation:", train_dataset_transformed.column_names)
print("Example transformed text:", train_dataset_transformed[0]["text"])
print("Train CEFR distribution:\n", train_dataset["level"].value_counts())
print("Validation CEFR distribution:\n", val_dataset["level"].value_counts())
print("Test CEFR distribution:\n", test_dataset["level"].value_counts())

# Cell 6: Rebalance validation and test sets
train_proportions = {
    'A1': 0.0346, 'A2': 0.1789, 'B1': 0.3454,
    'B2': 0.3101, 'C1': 0.1239, 'C2': 0.0072
}

def rebalance_dataset(df, total_samples, proportions, random_state=3407):
    resampled_dfs = []
    for level, proportion in proportions.items():
        level_df = df[df['level'] == level]
        n_samples = int(total_samples * proportion)
        if len(level_df) > n_samples:
            level_df_resampled = resample(level_df, n_samples=n_samples, random_state=random_state)
        else:
            level_df_resampled = resample(level_df, n_samples=n_samples, replace=True, random_state=random_state)
        resampled_dfs.append(level_df_resampled)
    return pd.concat(resampled_dfs).sample(frac=1, random_state=random_state).reset_index(drop=True)

val_df = val_dataset.copy()
new_val_df = rebalance_dataset(val_df, len(val_df), train_proportions)
new_val_dataset = Dataset.from_pandas(new_val_df)
new_val_dataset_transformed = new_val_dataset.map(format_func)
new_val_dataset_transformed = new_val_dataset_transformed.remove_columns(['sentence'])

test_df = test_dataset.copy()
new_test_df = rebalance_dataset(test_df, len(test_df), train_proportions)
new_test_dataset = Dataset.from_pandas(new_test_df)
new_test_dataset_transformed = new_test_dataset.map(format_func)
new_test_dataset_transformed = new_test_dataset_transformed.remove_columns(['sentence'])

print("New Validation CEFR distribution:\n", new_val_df["level"].value_counts(normalize=True))
print("New Test CEFR distribution:\n", new_test_df["level"].value_counts(normalize=True))

# Cell 7: Apply SMOTE and undersampling to balance training dataset
evaluator_model = SentenceTransformer("BAAI/bge-base-en-v1.5")

def apply_smote_to_dataset(df, target_proportions, random_state=3407):
    print("Generating sentence embeddings...")
    embeddings = evaluator_model.encode(df["sentence"].tolist(), show_progress_bar=True)

    level_to_idx = {'A1': 0, 'A2': 1, 'B1': 2, 'B2': 3, 'C1': 4, 'C2': 5}
    labels = df["level"].map(level_to_idx).values

    class_counts = df["level"].value_counts().to_dict()
    print("Original class counts:", class_counts)

    total_samples = len(df)
    target_samples = {
        level_to_idx[level]: max(int(total_samples * proportion), class_counts.get(level, 0))
        for level, proportion in target_proportions.items()
    }
    print("Target sample counts:", target_samples)

    pipeline = Pipeline([
        ('oversample', SMOTE(sampling_strategy=target_samples, random_state=random_state)),
        ('undersample', RandomUnderSampler(sampling_strategy=target_samples, random_state=random_state))
    ])

    print("Applying SMOTE and undersampling...")
    X_resampled, y_resampled = pipeline.fit_resample(embeddings, labels)

    idx_to_level = {v: k for k, v in level_to_idx.items()}
    resampled_data = []
    for embedding, label in zip(X_resampled, y_resampled):
        # Find the closest original embedding
        distances = np.linalg.norm(embeddings - embedding, axis=1)
        closest_idx = np.argmin(distances)
        sentence = df.iloc[closest_idx]["sentence"]
        resampled_data.append({
            "sentence": sentence,
            "level": idx_to_level[label]
        })

    return pd.DataFrame(resampled_data)

train_dataset_smote = apply_smote_to_dataset(train_dataset, train_proportions)
train_dataset_hf = Dataset.from_pandas(train_dataset_smote)
train_dataset_transformed = train_dataset_hf.map(format_func)
train_dataset_transformed = train_dataset_transformed.remove_columns(['sentence'])

print("SMOTE-balanced Train CEFR distribution:\n", train_dataset_smote["level"].value_counts(normalize=True))

# Cell 8: Training setup
wandb.init(project="Phi-3-CEFR-finetuning_v3",
           config={
               "model": "unsloth/llama-3-8b-instruct-bnb-4bit",
               "strategy": "gradient_checkpointing",
               "learning_rate": 2e-5,
               "batch_size": 8,
               "lora_dropout": 0.5,
           })

trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=train_dataset_transformed.shuffle(seed=3407),
    eval_dataset=new_val_dataset_transformed,
    dataset_text_field="text",
    max_seq_length=max_seq_length,
    callbacks=[
        EarlyStoppingCallback(early_stopping_patience=3, early_stopping_threshold=0.01),
    ],
    args=TrainingArguments(
        per_device_train_batch_size=8,
        gradient_accumulation_steps=1,
        warmup_ratio=0.1,
        num_train_epochs=0.1,
        learning_rate=2e-5,
        fp16=not is_bfloat16_supported(),
        bf16=is_bfloat16_supported(),
        logging_steps=50,
        optim="adamw_8bit",
        weight_decay=0.3,
        lr_scheduler_type="cosine",
        eval_strategy="steps",
        eval_steps=200,
        save_strategy="steps",
        save_steps=200,
        output_dir="outputs",
        load_best_model_at_end=True,
        metric_for_best_model="eval_loss",
        greater_is_better=False,
        seed=3407,
        report_to="wandb",
        run_name="phi3-cefr-lora-v14",
        gradient_checkpointing=True,
    ),
)

# Cell 9: Training and test evaluation
try:
    trainer_stats = trainer.train()
    print("Training completed successfully!")
    print("Trainer stats:", trainer_stats)
except Exception as e:
    print(f"Error during training: {e}")
    raise

# Tokenize test dataset
def tokenize_function(example):
    return tokenizer(example["text"], truncation=True, max_length=max_seq_length, padding=False)

new_test_dataset_tokenized = new_test_dataset_transformed.map(tokenize_function, batched=True)
new_test_dataset_tokenized = new_test_dataset_tokenized.remove_columns(['text'])
print("Test dataset structure:", new_test_dataset_tokenized[0])

# Evaluate on tokenized test dataset
try:
    eval_results = trainer.evaluate(new_test_dataset_tokenized)
    print("Test evaluation results:", eval_results)
except Exception as e:
    print(f"Error during evaluation: {e}")
    raise

# Cell 10: Save and upload the model to Hugging Face
# Save the fine-tuned model locally
output_dir = "./fine_tuned_model"
try:
    model = model.merge_and_unload()  # Merge LoRA weights with base model
    model.save_pretrained(output_dir)
    tokenizer.save_pretrained(output_dir)
    print(f"Model and tokenizer saved locally to {output_dir}")
except Exception as e:
    print(f"Error saving model locally: {e}")
    raise

# Create a new repository on Hugging Face
repo_id = "Mr-FineTuner/Test___01"
try:
    create_repo(repo_id, private=False)  # Set private=True for a private repo
    print(f"Repository {repo_id} created successfully!")
except Exception as e:
    print(f"Error creating repository: {e}")

# Upload the model to Hugging Face
try:
    upload_folder(
        folder_path=output_dir,
        repo_id=repo_id,
        repo_type="model",
        commit_message="Upload fine-tuned LLaMA-3-8B CEFR model"
    )
    print(f"Model uploaded successfully to https://huggingface.co/{repo_id}")
except Exception as e:
    print(f"Error uploading model: {e}")
    raise

# Create and upload a model card
model_card = """
# 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
- **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))
  ```

Uploaded using `huggingface_hub`.
"""
try:
    with open(f"{output_dir}/README.md", "w") as f:
        f.write(model_card)
    upload_folder(
        folder_path=output_dir,
        repo_id=repo_id,
        repo_type="model",
        commit_message="Add model card"
    )
    print(f"Model card uploaded successfully!")
except Exception as e:
    print(f"Error uploading model card: {e}")
    raise