Unsloth-Finetune-Template/synthetic-data.py

import os
import re

from datasets import Dataset, concatenate_datasets, load_dataset
from llama_cpp import Llama

# 1. CONFIGURATION
GGUF_MODEL_PATH = "./path/to/model.gguf"
INPUT_PARQUET_PATH = "./path/to/input.parquet"
OUTPUT_PARQUET_PATH = "./path/to/output.parquet"
NEW_ROWS_COUNT = 100

# Check if files exist
if not os.path.exists(GGUF_MODEL_PATH):
    print(f"❌ Error: GGUF model file not found at {GGUF_MODEL_PATH}")
    exit()
if not os.path.exists(INPUT_PARQUET_PATH):
    print(f"❌ Error: Input Parquet file not found at {INPUT_PARQUET_PATH}")
    exit()

# 2. LOAD GGUF MODEL - GPU (Vulkan) ONLY
print("Loading llama.cpp model...")
try:
    model = Llama(
        model_path=GGUF_MODEL_PATH,
        n_ctx=8192,
        n_gpu_layers=-1,  # ALL layers to GPU
        verbose=False,  # No logging
        n_batch=512,
        logits_all=False,
        use_mmap=True,
        use_mlock=False,
    )
    print("✅ llama.cpp model loaded with Vulkan GPU.")
except Exception as e:
    print(f"❌ Error loading model: {e}")
    exit()

# 3. LOAD EXISTING DATASET
print("Loading existing dataset from INPUT...")
try:
    original_ds = load_dataset(
        "parquet", data_files=[INPUT_PARQUET_PATH], split="train"
    )
    print(f"Original Columns: {original_ds.column_names}")
    print(f"Original Dataset Shape: {original_ds.shape}")
except Exception as e:
    print(f"❌ Error loading dataset: {e}")
    exit()

existing_labels = list(set(original_ds["label"]))

# 4. GENERATE SYNTHETIC DATA - STRUCTURED OUTPUT
print(f"Generating {NEW_ROWS_COUNT} synthetic records...")
synthetic_data = []

for i in range(NEW_ROWS_COUNT):
    try:
        messages = [
            {
                "role": "system",
                "content": "You are a data generator. Output ONLY the format below, nothing else.",
            },
            {
                "role": "user",
                "content": """YOUR PROMPT GOES HERE""",
            },
        ]

        # Generate with sampling parameters
        response = model.create_chat_completion(
            messages=messages,
            max_tokens=200,
            temperature=1.0,
            top_p=0.95,
            top_k=20,
            min_p=0.0,
        )

        # Get response text
        generated_text = response["choices"][0]["message"]["content"].strip()

        # DIRECTLY PARSE TO STRUCTURED FORMAT
        parsed_row = {}
        lines = generated_text.split("\n")

        question = None
        answer = None
        label = None
        found_question = False
        found_answer = False
        found_label = False

        for line in lines:
            line = line.strip()

            # Extract Question
            if "Question:" in line and "Answer:" not in line:
                match = re.search(
                    r"Question:\s*(.+?)(?:\nAnswer|\nLabel|$)", line, re.IGNORECASE
                )
                if match:
                    question = match.group(1).strip()
                    found_question = True

            # Extract Answer
            elif "Answer:" in line:
                match = re.search(r"Answer:\s*(.+?)(?:\nLabel|$)", line, re.IGNORECASE)
                if match:
                    answer = match.group(1).strip()
                    found_answer = True

            # Extract Label
            elif "Label:" in line:
                match = re.search(r"Label:\s*(.+)", line, re.IGNORECASE)
                if match:
                    label = match.group(1).strip()
                    found_label = True

        # VALIDATION
        if not all([question, answer]):
            print(f"⚠️ Row {i + 1}: Incomplete output. Skipping.")
            for line in lines:
                print(line)
            continue

        if not label:
            label = "unbiased"
        else:
            # Normalize label
            label = (
                label.lower().strip('"').strip("'").replace("[", "").replace("]", "")
            )

        if label not in existing_labels:
            print(f"⚠️ Row {i + 1}: Invalid label '{label}'. Skipping.")
            continue

        # Clean up
        question = re.sub(r"```.*?```", "", question).strip()
        answer = re.sub(r"```.*?```", "", answer).strip()

        parsed_row = {"question": question, "answer": answer, "label": label}

        # PRINT PARSED DATA IN TERMINAL
        print(f"✅ ROW {i + 1} PARSED:")
        print(f"   Question: {question}")
        print(f"   Answer: {answer}")
        print(f"   Label: {label}")
        print()

        synthetic_data.append(parsed_row)

    except Exception as e:
        print(f"❌ Row {i + 1}: Error: {e}")
        continue

# 5. SAVE TO PARQUET
if synthetic_data:
    print(f"Adding {len(synthetic_data)} synthetic records...")
    synthetic_ds = Dataset.from_list(synthetic_data)

    base_ds = None
    if os.path.exists(INPUT_PARQUET_PATH):
        base_ds = load_dataset(
            "parquet", data_files=[INPUT_PARQUET_PATH], split="train"
        )
        print(f"Existing: {len(base_ds)} rows")
    else:
        base_ds = original_ds

    combined_ds = concatenate_datasets([base_ds, synthetic_ds])
    print(f"Combined: {len(combined_ds)} rows")

    combined_ds.to_parquet(OUTPUT_PARQUET_PATH)
    print(f"✅ Saved to {OUTPUT_PARQUET_PATH}")
else:
    print("❌ No valid records generated.")