updated instruct
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#!/usr/bin/env python3
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"""
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Instruct data processor script that uses YAML configurations.
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This provides a flexible and maintainable approach for instruction fine-tuning tasks.
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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_with_yaml_config(config_path: str, **cli_overrides):
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"""Run instruct data processor with YAML configuration"""
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print(f"=== Running Instruct Data Processor with YAML config: {config_path} ===")
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cmd = [
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"python", "pipelines/instruct/data_processor.py",
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"--config", config_path
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]
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# Add CLI overrides
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for key, value in cli_overrides.items():
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if value is not None:
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cmd.extend([f"--{key.replace('_', '-')}", str(value)])
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print(f"Running command: {' '.join(cmd)}")
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print()
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try:
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result = subprocess.run(cmd, check=True, capture_output=True, text=True)
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print("✅ Instruct data processing completed successfully!")
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print(result.stdout)
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return True
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except subprocess.CalledProcessError as e:
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print(f"❌ Error running instruct data processor: {e}")
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print(f"Error output: {e.stderr}")
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return False
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def run_instruct_examples():
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"""Run instruct examples with YAML configs"""
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# Example 1: Code reasoning instruction tuning
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print("=== Example 1: Code Reasoning Instruction Tuning ===")
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success = run_with_yaml_config(
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"configs/instruct/code_reasoning.yaml",
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max_samples=1000, # Override YAML value
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output_format="conversation"
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)
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if success:
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print("✅ Code reasoning instruction tuning data processing completed!")
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# Example 2: Custom conversation dataset (if available)
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print("\n=== Example 2: Custom Conversation Dataset ===")
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if os.path.exists("data/raw/instruct/custom_conversations.jsonl"):
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success = run_with_yaml_config(
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"configs/instruct/code_reasoning.yaml", # Use code_reasoning config as base
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data_source="custom",
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data_path="data/raw/instruct/custom_conversations.jsonl",
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output_dir="./data/processed/instruct/custom"
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)
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if success:
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print("✅ Custom conversation dataset processing completed!")
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else:
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print("⚠️ Custom conversation dataset not found, skipping...")
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print(" You can create one with the 'create-sample-data' option")
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def create_sample_instruct_data():
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"""Create sample conversation dataset for testing"""
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sample_data = [
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{
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"conversation": [
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{"role": "user", "content": "Can you explain what a Python list comprehension is?"},
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{"role": "assistant", "content": "A Python list comprehension is a concise way to create lists. It allows you to generate a new list by applying an expression to each item in an existing iterable, optionally filtering items with a condition. The basic syntax is: [expression for item in iterable if condition]. For example: [x**2 for x in range(10) if x%2==0] creates a list of squares of even numbers from 0 to 8."}
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]
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},
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{
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"conversation": [
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{"role": "user", "content": "How do I handle exceptions in Python?"},
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{"role": "assistant", "content": "In Python, you handle exceptions using try-except blocks. Here's the basic structure:\n\ntry:\n # Code that might raise an exception\n risky_code()\nexcept SpecificException as e:\n # Handle specific exception\n print(f'Error: {e}')\nexcept Exception as e:\n # Handle any other exception\n print(f'Unexpected error: {e}')\nelse:\n # Runs if no exception occurred\n print('Success!')\nfinally:\n # Always runs, regardless of exceptions\n cleanup_code()\n\nYou can catch multiple specific exceptions and handle them differently."}
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]
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},
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{
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"conversation": [
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{"role": "user", "content": "What's the difference between a function and a method in Python?"},
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{"role": "assistant", "content": "In Python, the main differences between functions and methods are:\n\n1. **Definition location**: Functions are defined independently, while methods are defined inside classes.\n\n2. **Calling**: Functions are called directly by name, methods are called on objects using dot notation.\n\n3. **First parameter**: Methods automatically receive 'self' as the first parameter (the instance they're called on), functions don't have this automatic parameter.\n\nExample:\n```python\n# Function\ndef my_function(x):\n return x * 2\n\n# Method (inside a class)\nclass MyClass:\n def my_method(self, x):\n return x * 2\n\n# Usage\nresult1 = my_function(5) # Function call\nobj = MyClass()\nresult2 = obj.my_method(5) # Method call\n```"}
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]
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},
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{
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"conversation": [
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{"role": "user", "content": "How can I optimize a slow Python loop?"},
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{"role": "assistant", "content": "Here are several strategies to optimize slow Python loops:\n\n1. **Use list comprehensions** instead of explicit loops when possible\n2. **Vectorize with NumPy** for numerical operations\n3. **Use built-in functions** like map(), filter(), sum()\n4. **Avoid repeated lookups** - store frequently accessed values in variables\n5. **Use sets for membership testing** instead of lists\n6. **Consider using itertools** for efficient iteration patterns\n7. **Profile your code** with cProfile to identify bottlenecks\n8. **Use numba or Cython** for compute-intensive loops\n\nExample optimization:\n```python\n# Slow\nresult = []\nfor i in range(1000000):\n if i % 2 == 0:\n result.append(i ** 2)\n\n# Faster\nresult = [i ** 2 for i in range(1000000) if i % 2 == 0]\n\n# Even faster with NumPy\nimport numpy as np\narr = np.arange(1000000)\nresult = (arr[arr % 2 == 0] ** 2).tolist()\n```"}
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]
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},
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{
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"conversation": [
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{"role": "user", "content": "What are Python decorators and how do I use them?"},
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{"role": "assistant", "content": "Python decorators are a powerful feature that allows you to modify or extend the behavior of functions or classes without permanently modifying their code. They use the @ symbol and are placed above the function definition.\n\n**Basic decorator example:**\n```python\ndef my_decorator(func):\n def wrapper(*args, **kwargs):\n print('Before function call')\n result = func(*args, **kwargs)\n print('After function call')\n return result\n return wrapper\n\n@my_decorator\ndef greet(name):\n print(f'Hello, {name}!')\n\ngreet('Alice') # Prints before, greeting, and after messages\n```\n\n**Common use cases:**\n- Timing function execution\n- Logging\n- Authentication/authorization\n- Caching results\n- Input validation\n\n**Built-in decorators:**\n- `@property` - creates getter/setter methods\n- `@staticmethod` - methods that don't need self or cls\n- `@classmethod` - methods that receive the class as first argument\n\nDecorators make code more modular and reusable by separating concerns."}
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]
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}
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]
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# Create directory structure
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data_dir = Path("data/raw/instruct")
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data_dir.mkdir(parents=True, exist_ok=True)
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# Save sample data
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import json
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sample_file = data_dir / "code_reasoning.jsonl"
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with open(sample_file, 'w', encoding='utf-8') as f:
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for item in sample_data:
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f.write(json.dumps(item, ensure_ascii=False) + '\n')
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print(f"✅ Created sample conversation dataset: {sample_file}")
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print(f" Contains {len(sample_data)} conversation examples")
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print(f" Format: conversation array with role/content pairs")
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print(f" Ready to use with configs/instruct/code_reasoning.yaml")
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def create_custom_instruct_config():
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"""Create a custom instruct configuration file"""
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custom_config = """# Custom Instruct Configuration
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task:
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name: "general_chat"
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type: "instruction_following"
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data:
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source: "custom"
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data_path: "./data/raw/instruct/general_chat.jsonl"
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data_format: "jsonl"
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conversation_field: "conversation"
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max_length: 2048
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min_length: 10
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clean_text: true
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train_split: 0.8
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validation_split: 0.1
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test_split: 0.1
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output_format: "conversation"
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output_dir: "./data/processed/instruct/general_chat"
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model:
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name: "unsloth/Qwen2.5-7B-Instruct"
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max_length: 2048
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max_seq_length: 2048
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dtype: null
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load_in_4bit: true
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token: null
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training_model: "unsloth/Qwen2.5-7B-Instruct"
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training_max_seq_length: 2048
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training_dtype: null
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training_load_in_4bit: true
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training:
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num_epochs: 1
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batch_size: 1
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learning_rate: 2e-4
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weight_decay: 0.01
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warmup_steps: 5
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max_steps: 50
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gradient_accumulation_steps: 4
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lr_scheduler_type: "linear"
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seed: 3407
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lora_r: 16
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lora_alpha: 16
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lora_dropout: 0
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target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
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output_dir: "./outputs"
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model_output_dir: "./models/instruct/general_chat"
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inference:
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batch_size: 1
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max_new_tokens: 256
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temperature: 0.8
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min_p: 0.1
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use_cache: true
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"""
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config_path = "configs/instruct/general_chat.yaml"
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os.makedirs(os.path.dirname(config_path), exist_ok=True)
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with open(config_path, 'w') as f:
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f.write(custom_config)
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print(f"✅ Created custom instruct config: {config_path}")
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print(" This config is set up for general chat instruction tuning")
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def handle_direct_args():
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"""Handle direct command-line arguments by passing them to the instruct pipeline"""
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parser = argparse.ArgumentParser(description="Instruct Data Processor")
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# Add all the same arguments as the instruct pipeline
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parser.add_argument("--config", type=str, help="Path to YAML configuration file")
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parser.add_argument("--data-source", choices=["huggingface", "custom"], help="Data source")
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parser.add_argument("--dataset-name", type=str, help="HuggingFace dataset name")
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parser.add_argument("--data-path", type=str, help="Path to custom data file")
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parser.add_argument("--data-format", choices=["jsonl", "json"], help="Data format")
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parser.add_argument("--conversation-field", type=str, help="Conversation field name")
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parser.add_argument("--max-samples", type=int, help="Maximum samples to process")
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parser.add_argument("--train-split", type=float, help="Training split ratio")
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parser.add_argument("--validation-split", type=float, help="Validation split ratio")
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parser.add_argument("--test-split", type=float, help="Test split ratio")
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parser.add_argument("--output-dir", type=str, help="Output directory")
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# Logging
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parser.add_argument("--log-level", choices=["DEBUG", "INFO", "WARNING", "ERROR"], default="INFO", help="Logging level")
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args = parser.parse_args()
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# Build command to call the instruct pipeline
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cmd = ["python", "pipelines/instruct/data_processor.py"]
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# Add all arguments that were provided
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for arg_name, arg_value in vars(args).items():
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if arg_value is not None:
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if isinstance(arg_value, bool):
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if arg_value: # Only add flag if True
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cmd.append(f"--{arg_name.replace('_', '-')}")
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else:
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cmd.extend([f"--{arg_name.replace('_', '-')}", str(arg_value)])
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print(f"Running: {' '.join(cmd)}")
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print()
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try:
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result = subprocess.run(cmd, check=True, capture_output=True, text=True)
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print("✅ Instruct data processing completed successfully!")
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print(result.stdout)
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return True
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except subprocess.CalledProcessError as e:
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print(f"❌ Error running instruct data processor: {e}")
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print(f"Error output: {e.stderr}")
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return False
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def show_instruct_features():
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"""Show the features of the instruct data processor"""
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print("=== Instruct Data Processor Features ===")
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print()
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print("1. **Instruction Fine-tuning Tasks**:")
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print(" - Code reasoning and explanation")
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print(" - General conversation and chat")
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print(" - Question answering")
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print(" - Task-specific instruction following")
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print()
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print("2. **Conversation Data Formats Supported**:")
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print(" - HuggingFace conversation datasets")
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print(" - Custom JSONL/JSON files with conversation arrays")
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print(" - ShareGPT format with role/content structure")
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print(" - Automatic train/validation/test splits")
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print()
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print("3. **Conversation Validation**:")
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print(" - Role validation (user/assistant/system)")
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print(" - Content length and quality checks")
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print(" - Conversation structure validation")
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print(" - Turn-level statistics and analysis")
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print()
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print("4. **Advanced Features**:")
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print(" - Configurable conversation field mapping")
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print(" - Text preprocessing options")
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print(" - Automatic dataset saving/loading")
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print(" - YAML configuration support")
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print(" - Compatible with Unsloth chat templates")
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print()
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print("=== Usage Examples ===")
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print()
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print("1. Use YAML config only:")
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print(" python scripts/instruct/data_processor.py --config configs/instruct/code_reasoning.yaml")
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print()
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print("2. Override YAML values:")
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print(" python scripts/instruct/data_processor.py --config configs/instruct/code_reasoning.yaml --max-samples 500")
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print()
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print("3. Create sample data:")
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print(" python scripts/instruct/data_processor.py create-sample-data")
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print()
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print("4. Create custom config:")
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print(" python scripts/instruct/data_processor.py create-config")
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def main():
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"""Main function"""
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if len(sys.argv) > 1:
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# Check if it's a subcommand
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if sys.argv[1] in ["examples", "create-sample-data", "create-config", "features"]:
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# Handle subcommands
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if sys.argv[1] == "examples":
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run_instruct_examples()
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elif sys.argv[1] == "create-sample-data":
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create_sample_instruct_data()
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elif sys.argv[1] == "create-config":
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create_custom_instruct_config()
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elif sys.argv[1] == "features":
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show_instruct_features()
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else:
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# Handle direct arguments (pass through to pipeline)
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handle_direct_args()
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else:
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print("Instruct Data Processor")
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print("======================")
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print()
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print("This script runs the instruct data processor for instruction fine-tuning tasks.")
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print("It supports both YAML configurations and command-line overrides.")
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print()
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print("Usage:")
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print(" python scripts/instruct/data_processor.py examples # Run examples")
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print(" python scripts/instruct/data_processor.py create-sample-data # Create sample dataset")
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print(" python scripts/instruct/data_processor.py create-config # Create custom config")
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print(" python scripts/instruct/data_processor.py features # Show features")
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print()
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print("Direct pipeline usage:")
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print(" python scripts/instruct/data_processor.py --config configs/instruct/code_reasoning.yaml")
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print(" python scripts/instruct/data_processor.py --data-source custom --data-path ./conversations.jsonl")
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print()
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print("Key Features:")
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print(" ✅ Instruction fine-tuning with conversation data")
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print(" ✅ Multiple data source support")
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print(" ✅ YAML configuration files")
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print(" ✅ CLI argument overrides")
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print(" ✅ Conversation validation and analysis")
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print(" ✅ Compatible with Unsloth chat templates")
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if __name__ == "__main__":
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main()
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