updated style mimciking fine tuning
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#!/usr/bin/env python3
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"""
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Styling Inference Script
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Provides a command-line interface to run the styling inference pipeline
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"""
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import sys
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import os
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import subprocess
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import argparse
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from pathlib import Path
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def run_inference_with_config(config_path: str, instruction: str, input_text: str = "", max_tokens: int = 128, stream: bool = False):
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"""Run inference using a YAML configuration file"""
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print(f"Running styling inference with config: {config_path}")
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print(f"Instruction: {instruction}")
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print(f"Input text: {input_text}")
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print(f"Max tokens: {max_tokens}")
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print(f"Streaming: {stream}")
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cmd = [
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"python", "pipelines/styling/inference.py",
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"--config", config_path,
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"--instruction", instruction,
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"--input-text", input_text,
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"--max-tokens", str(max_tokens)
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]
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if stream:
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cmd.append("--stream")
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print(f"Running: {' '.join(cmd)}")
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try:
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result = subprocess.run(cmd, capture_output=True, text=True, check=True)
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print("✅ Inference completed successfully!")
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print("Output:")
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print(result.stdout)
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return result.stdout
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except subprocess.CalledProcessError as e:
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print(f"❌ Inference failed: {e}")
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print("Error output:")
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print(e.stderr)
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return None
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def run_batch_inference_example(config_path: str, input_file: str, output_file: str, instruction: str, max_tokens: int = 128):
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"""Run batch inference example"""
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print(f"=== Batch Inference Example ===")
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print(f"Config: {config_path}")
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print(f"Input file: {input_file}")
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print(f"Output file: {output_file}")
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print(f"Instruction: {instruction}")
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print(f"Max tokens: {max_tokens}")
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# Check if input file exists
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if not Path(input_file).exists():
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print(f"❌ Input file not found: {input_file}")
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return False
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# Read input texts
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with open(input_file, 'r', encoding='utf-8') as f:
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input_texts = [line.strip() for line in f if line.strip()]
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print(f"Found {len(input_texts)} texts to process")
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# Process each text
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results = []
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for i, text in enumerate(input_texts):
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print(f"\nProcessing text {i+1}/{len(input_texts)}: {text[:50]}...")
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result = run_inference_with_config(config_path, instruction, text, max_tokens)
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if result:
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results.append(result)
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else:
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print(f"❌ Failed to process text {i+1}")
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# Save results
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with open(output_file, 'w', encoding='utf-8') as f:
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for i, (input_text, result) in enumerate(zip(input_texts, results)):
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f.write(f"Input {i+1}: {input_text}\n")
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f.write(f"Output {i+1}: {result}\n")
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f.write("-" * 50 + "\n")
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print(f"✅ Batch inference completed! Results saved to: {output_file}")
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return True
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def show_inference_features():
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"""Show the features of the styling inference pipeline"""
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print("=== Styling Inference Pipeline Features ===")
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print()
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print("1. **Model Support**:")
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print(" - Trained LoRA models from training pipeline")
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print(" - Automatic model loading from config")
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print(" - Native Unsloth inference optimization")
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print()
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print("2. **Inference Modes**:")
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print(" - Single text inference with instruction")
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print(" - Batch file processing")
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print(" - Streaming generation")
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print()
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print("3. **Generation Control**:")
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print(" - Configurable max tokens")
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print(" - Same alpaca prompt format as training")
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print(" - Automatic response extraction")
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print()
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print("4. **Usage Examples**:")
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print(" - Single inference: --instruction 'style instruction' --input-text 'your text'")
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print(" - Streaming: add --stream flag")
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print(" - Batch: use batch subcommand with input/output files")
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def create_inference_example():
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"""Create an inference example using the formal style configuration"""
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print("=== Inference Example: Formal Style Transfer ===")
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print()
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# Check if we have the required files
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config_path = "configs/styling/formal.yaml"
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if not Path(config_path).exists():
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print(f"❌ Configuration file not found: {config_path}")
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print(" Please run the data processor first to create the configuration")
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return False
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print("✅ Found configuration file!")
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print(f" Config: {config_path}")
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print()
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# Example text
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example_text = "Hey, what's up? I'm gonna go grab some food later."
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print(f"Example text: {example_text}")
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print()
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# Run inference
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success = run_inference_with_config(
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config_path=config_path,
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instruction="Rewrite the following text in a formal style",
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input_text=example_text
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)
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if success:
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print("✅ Example inference completed successfully!")
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return True
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else:
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print("❌ Example inference failed!")
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return False
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def main():
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"""Main inference function"""
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parser = argparse.ArgumentParser(description="Styling Inference Pipeline")
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subparsers = parser.add_subparsers(dest="command", help="Available commands")
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# Inference command
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infer_parser = subparsers.add_parser("infer", help="Run single inference")
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infer_parser.add_argument("--config", type=str, required=True, help="Path to YAML configuration file")
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infer_parser.add_argument("--instruction", type=str, required=True, help="Style instruction")
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infer_parser.add_argument("--input-text", type=str, default="", help="Input text to style")
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infer_parser.add_argument("--max-tokens", type=int, default=128, help="Maximum new tokens to generate")
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infer_parser.add_argument("--stream", action="store_true", help="Enable streaming generation")
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# Batch inference command
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batch_parser = subparsers.add_parser("batch", help="Run batch inference")
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batch_parser.add_argument("--config", type=str, required=True, help="Path to YAML configuration file")
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batch_parser.add_argument("--input-file", type=str, required=True, help="Input file with texts to style")
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batch_parser.add_argument("--output-file", type=str, required=True, help="Output file for styled texts")
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batch_parser.add_argument("--instruction", type=str, required=True, help="Style instruction")
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batch_parser.add_argument("--max-tokens", type=int, default=128, help="Maximum new tokens to generate")
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# Features command
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subparsers.add_parser("features", help="Show available features")
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# Example command
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subparsers.add_parser("example", help="Run example inference")
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args = parser.parse_args()
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if args.command == "infer":
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run_inference_with_config(
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args.config,
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args.instruction,
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args.input_text,
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args.max_tokens,
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args.stream
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)
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elif args.command == "batch":
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run_batch_inference_example(args.config, args.input_file, args.output_file, args.instruction, args.max_tokens)
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elif args.command == "features":
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show_inference_features()
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elif args.command == "example":
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create_inference_example()
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else:
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parser.print_help()
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if __name__ == "__main__":
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main()
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