instruct mode added to pipiline

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -9,3 +9,4 @@ numpy>=1.24.0
 scikit-learn>=1.3.0
 pyyaml>=6.0
 huggingface-hub>=0.15.0
+unsloth

.ipynb_checkpoints/setup-checkpoint.sh

@@ -0,0 +1,46 @@
+#!/usr/bin/env bash
+set -e  # exit on error
+
+# Step 1: Download Miniconda installer
+echo ">>> Downloading Miniconda..."
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
+
+# Step 2: Run the installer
+echo ">>> Installing Miniconda..."
+bash ~/miniconda.sh -b -p $HOME/miniconda
+
+# Step 3: Initialize conda for bash
+echo ">>> Initializing Conda..."
+eval "$($HOME/miniconda/bin/conda shell.bash hook)"
+
+# Step 4: Add conda to PATH permanently
+if ! grep -q "miniconda/bin" ~/.bashrc; then
+    echo "export PATH=\$HOME/miniconda/bin:\$PATH" >> ~/.bashrc
+fi
+source ~/.bashrc
+
+# Step 5: Verify installation
+echo ">>> Conda version:"
+conda --version
+
+# Step 6: Create environment
+echo ">>> Creating environment: llm-env (python=3.10)..."
+conda create -n llm-env python=3.10 -y
+
+# Step 7: Activate environment
+echo ">>> Activating environment..."
+conda activate llm-env
+
+# Step 8: Install dependencies
+echo ">>> Installing scikit-learn..."
+conda install scikit-learn -y
+
+if [ -f requirements.txt ]; then
+    echo ">>> Installing Python requirements..."
+    pip install -r requirements.txt
+else
+    echo ">>> No requirements.txt found, skipping pip install."
+fi
+
+echo ">>> Setup complete. To activate your environment run:"
+echo "conda activate llm-env"

configs/instruct/.ipynb_checkpoints/sample-checkpoint.yaml

@@ -10,7 +10,7 @@ task:
 # Data Processing Configuration
 data:
   source: "custom"                          # Data source: "huggingface" or "custom"
-  data_path: "./data/raw/instruct/code_reasoning.jsonl"  # Path to conversation data file
+  data_path: "data/raw/swe_reasoning_dataset (3).jsonl"  # Path to conversation data file
   data_format: "jsonl"                      # Data format: "jsonl", "json"
   
   # Field Mapping for Conversation Data
@@ -34,7 +34,7 @@ data:
 
 # Model Configuration
 model:
-  name: "unsloth/Qwen2.5-72B-Instruct"     # Model name from HuggingFace Hub (optimized for instruction following)
+  name: "unsloth/Qwen2.5-Coder-7B"     # Model name from HuggingFace Hub (optimized for instruction following)
   max_length: 2048                          # Maximum sequence length for tokenization
   max_seq_length: 2048                      # Maximum sequence length for training (RoPE scaling supported)
   dtype: null                               # Data type: null for auto detection, float16 for Tesla T4/V100, bfloat16 for Ampere+
@@ -42,7 +42,7 @@ model:
   token: null                               # HuggingFace token for gated models (e.g., "hf_...")
   
   # Training Model Parameters
-  training_model: "unsloth/Qwen2.5-72B-Instruct"  # Model to use for training
+  training_model: "unsloth/Qwen2.5-Coder-7B"  # Model to use for training
   training_max_seq_length: 2048             # Max sequence length for training
   training_dtype: null                      # Data type for training
   training_load_in_4bit: true               # 4bit quantization for training

configs/instruct/sample.yaml

@@ -10,7 +10,7 @@ task:
 # Data Processing Configuration
 data:
   source: "custom"                          # Data source: "huggingface" or "custom"
-  data_path: "./data/raw/instruct/code_reasoning.jsonl"  # Path to conversation data file
+  data_path: "data/raw/swe_reasoning_dataset (3).jsonl"  # Path to conversation data file
   data_format: "jsonl"                      # Data format: "jsonl", "json"
   
   # Field Mapping for Conversation Data
@@ -34,7 +34,7 @@ data:
 
 # Model Configuration
 model:
-  name: "unsloth/Qwen2.5-72B-Instruct"     # Model name from HuggingFace Hub (optimized for instruction following)
+  name: "unsloth/Qwen2.5-Coder-7B"     # Model name from HuggingFace Hub (optimized for instruction following)
   max_length: 2048                          # Maximum sequence length for tokenization
   max_seq_length: 2048                      # Maximum sequence length for training (RoPE scaling supported)
   dtype: null                               # Data type: null for auto detection, float16 for Tesla T4/V100, bfloat16 for Ampere+
@@ -42,7 +42,7 @@ model:
   token: null                               # HuggingFace token for gated models (e.g., "hf_...")
   
   # Training Model Parameters
-  training_model: "unsloth/Qwen2.5-72B-Instruct"  # Model to use for training
+  training_model: "unsloth/Qwen2.5-Coder-7B"  # Model to use for training
   training_max_seq_length: 2048             # Max sequence length for training
   training_dtype: null                      # Data type for training
   training_load_in_4bit: true               # 4bit quantization for training

pipelines/instruct/.ipynb_checkpoints/data_processor-checkpoint.py

@@ -0,0 +1,913 @@
+import json
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from typing import Dict, List, Optional, Union, Any, Tuple
+from datasets import Dataset, load_dataset
+import os
+from dataclasses import dataclass
+from abc import ABC, abstractmethod
+from sklearn.model_selection import train_test_split
+import re
+import argparse
+import sys
+import yaml
+
+@dataclass
+class InstructConfig:
+    """Configuration for instruction fine-tuning tasks"""
+    # Data source configuration
+    data_source: str = "custom"  # "huggingface" or "custom"
+    dataset_name: Optional[str] = None  # For Hugging Face datasets
+    data_path: Optional[str] = None  # For custom datasets
+    data_format: str = "jsonl"  # jsonl, json
+
+    # Field mapping - conversation data specific
+    conversation_field: str = "conversation"  # Field containing conversation array
+    
+    # Data processing
+    max_samples: Optional[int] = None
+    train_split: float = 0.8
+    validation_split: float = 0.1
+    test_split: float = 0.1
+
+    # Text preprocessing
+    clean_text: bool = True
+    min_length: int = 10
+    max_length: int = 2048
+
+    # Output configuration
+    output_format: str = "conversation"  # conversation, alpaca
+    output_dir: str = "./data/processed/instruct"
+
+    # Hugging Face specific
+    hf_split: str = "train"
+    hf_cache_dir: Optional[str] = None
+
+    # Split configuration
+    test_split_from: str = "train"
+    val_split_from: str = "train"
+
+    # Custom data specific
+    encoding: str = "utf-8"
+
+class ConversationValidator:
+    """Validates conversation data quality and format"""
+
+    @staticmethod
+    def validate_conversation_data(data: Dict[str, List[Dict]], config: InstructConfig, is_processed: bool = False) -> Tuple[bool, List[str]]:
+        """Validate conversation dataset splits"""
+        errors = []
+        
+        # Check if we have the expected splits
+        expected_splits = ["train", "validation", "test"]
+        for split in expected_splits:
+            if split not in data:
+                errors.append(f"Missing '{split}' split")
+            elif split == "train" and not data[split]:
+                errors.append(f"Train split cannot be empty")
+        
+        if errors:
+            return False, errors
+
+        total_samples = sum(len(split_data) for split_data in data.values())
+        print(f"Validating {total_samples} total samples across all splits...")
+
+        # Determine field names based on whether data is processed or not
+        conversation_field = "conversation" if not is_processed else "conversation"
+
+        # Validate each split
+        for split_name, split_data in data.items():
+            if not split_data:
+                print(f"Skipping validation for empty {split_name} split")
+                continue
+                
+            print(f"Validating {split_name} split with {len(split_data)} samples...")
+            
+            # Check required fields
+            missing_conversation_count = 0
+
+            for i, item in enumerate(split_data):
+                if conversation_field not in item:
+                    errors.append(f"Missing conversation field '{conversation_field}' in {split_name} split, item {i}")
+                    missing_conversation_count += 1
+                else:
+                    # Validate conversation structure
+                    conversation = item[conversation_field]
+                    if not isinstance(conversation, list):
+                        errors.append(f"Conversation field must be a list in {split_name} split, item {i}")
+                    else:
+                        # Validate each turn in conversation
+                        for j, turn in enumerate(conversation):
+                            if not isinstance(turn, dict):
+                                errors.append(f"Each conversation turn must be a dict in {split_name} split, item {i}, turn {j}")
+                                continue
+                            
+                            # Check for required fields in conversation turn
+                            if "role" not in turn:
+                                errors.append(f"Missing 'role' field in conversation turn {j}, {split_name} split, item {i}")
+                            if "content" not in turn:
+                                errors.append(f"Missing 'content' field in conversation turn {j}, {split_name} split, item {i}")
+                            
+                            # Validate role values
+                            if "role" in turn and turn["role"] not in ["user", "assistant", "system"]:
+                                errors.append(f"Invalid role '{turn['role']}' in conversation turn {j}, {split_name} split, item {i}. Must be 'user', 'assistant', or 'system'")
+
+            print(f"{split_name} - Items missing conversation field: {missing_conversation_count}")
+
+            # Show sample of processed data for debugging
+            if split_data:
+                print(f"Sample conversation from {split_name}:")
+                for i in range(min(2, len(split_data))):
+                    item = split_data[i]
+                    conversation = item.get(conversation_field, [])
+                    print(f"  Item {i} conversation length: {len(conversation)} turns")
+                    for j, turn in enumerate(conversation[:3]):  # Show first 3 turns
+                        role = turn.get("role", "unknown")
+                        content = turn.get("content", "")[:100] + "..." if len(turn.get("content", "")) > 100 else turn.get("content", "")
+                        print(f"    Turn {j}: {role} -> '{content}'")
+
+        return len(errors) == 0, errors
+
+    @staticmethod
+    def analyze_conversation_dataset(data: Dict[str, List[Dict]], config: InstructConfig, is_processed: bool = False) -> Dict[str, Any]:
+        """Analyze conversation dataset characteristics across all splits"""
+        analysis = {
+            "splits": {},
+            "overall": {
+                "total_samples": 0,
+                "split_sizes": {},
+                "conversation_stats": {
+                    "total_turns": 0,
+                    "avg_turns_per_conversation": 0,
+                    "role_distribution": {"user": 0, "assistant": 0, "system": 0}
+                }
+            }
+        }
+
+        conversation_field = "conversation" if not is_processed else "conversation"
+        total_turns = 0
+        total_conversations = 0
+        role_counts = {"user": 0, "assistant": 0, "system": 0}
+
+        # Analyze each split
+        for split_name, split_data in data.items():
+            if not split_data:
+                split_analysis = {
+                    "total_samples": 0,
+                    "conversation_stats": {},
+                    "missing_values": {}
+                }
+                analysis["splits"][split_name] = split_analysis
+                analysis["overall"]["split_sizes"][split_name] = 0
+                continue
+                
+            split_analysis = {
+                "total_samples": len(split_data),
+                "conversation_stats": {},
+                "missing_values": {}
+            }
+
+            # Conversation statistics
+            split_turns = 0
+            split_conversations = len(split_data)
+            split_role_counts = {"user": 0, "assistant": 0, "system": 0}
+            conversation_lengths = []
+
+            for item in split_data:
+                conversation = item.get(conversation_field, [])
+                if isinstance(conversation, list):
+                    conversation_lengths.append(len(conversation))
+                    split_turns += len(conversation)
+                    for turn in conversation:
+                        if isinstance(turn, dict) and "role" in turn:
+                            role = turn["role"]
+                            if role in split_role_counts:
+                                split_role_counts[role] += 1
+
+            if conversation_lengths:
+                split_analysis["conversation_stats"] = {
+                    "total_turns": split_turns,
+                    "avg_turns_per_conversation": np.mean(conversation_lengths),
+                    "min_turns": min(conversation_lengths),
+                    "max_turns": max(conversation_lengths),
+                    "median_turns": np.median(conversation_lengths),
+                    "role_distribution": split_role_counts
+                }
+
+            # Missing values
+            missing_count = sum(1 for item in split_data if not item.get(conversation_field))
+            split_analysis["missing_values"][conversation_field] = missing_count
+
+            analysis["splits"][split_name] = split_analysis
+            analysis["overall"]["total_samples"] += len(split_data)
+            analysis["overall"]["split_sizes"][split_name] = len(split_data)
+            
+            # Accumulate overall stats
+            total_turns += split_turns
+            total_conversations += split_conversations
+            for role, count in split_role_counts.items():
+                role_counts[role] += count
+
+        # Calculate overall conversation stats
+        if total_conversations > 0:
+            analysis["overall"]["conversation_stats"]["total_turns"] = total_turns
+            analysis["overall"]["conversation_stats"]["avg_turns_per_conversation"] = total_turns / total_conversations
+            analysis["overall"]["conversation_stats"]["role_distribution"] = role_counts
+
+        return analysis
+
+class BaseInstructDataLoader(ABC):
+    """Abstract base class for instruction data loaders"""
+
+    @abstractmethod
+    def load(self, config: InstructConfig) -> Dict[str, List[Dict]]:
+        """Load data and return dictionary with train/val/test splits"""
+        pass
+
+    @abstractmethod
+    def preprocess(self, data: Dict[str, List[Dict]], config: InstructConfig) -> Dict[str, List[Dict]]:
+        """Apply preprocessing steps to all splits"""
+        pass
+
+class HuggingFaceInstructDataLoader(BaseInstructDataLoader):
+    """Load conversation datasets from Hugging Face Hub"""
+
+    def load(self, config: InstructConfig) -> Dict[str, List[Dict]]:
+        """Load dataset from Hugging Face Hub with flexible split handling"""
+        if not config.dataset_name:
+            raise ValueError("Dataset name is required for Hugging Face datasets")
+
+        print(f"Loading Hugging Face conversation dataset: {config.dataset_name}")
+
+        try:
+            dataset = load_dataset(
+                config.dataset_name,
+                cache_dir=config.hf_cache_dir
+            )
+
+            available_splits = list(dataset.keys())
+            print(f"Available splits in dataset: {available_splits}")
+
+            splits_data = {
+                "train": [],
+                "validation": [],
+                "test": []
+            }
+
+            # Handle train split
+            if "train" in available_splits:
+                train_dataset = dataset["train"]
+                print(f"Using 'train' split with {len(train_dataset)} samples")
+                splits_data["train"] = list(train_dataset)
+            else:
+                print("No 'train' split found in dataset!")
+                raise ValueError(f"Dataset {config.dataset_name} does not have a 'train' split")
+
+            # Handle validation and test splits (similar logic to styling pipeline)
+            # ... [validation and test split handling logic similar to styling pipeline]
+
+            # Apply max_samples limit if specified
+            if config.max_samples:
+                for split_name in splits_data:
+                    if splits_data[split_name]:
+                        original_size = len(splits_data[split_name])
+                        splits_data[split_name] = splits_data[split_name][:config.max_samples]
+                        print(f"Limited {split_name} split from {original_size} to {len(splits_data[split_name])} samples")
+
+            print(f"Successfully loaded dataset {config.dataset_name}")
+            return splits_data
+
+        except Exception as e:
+            print(f"Error loading dataset {config.dataset_name}: {e}")
+            raise
+
+    def preprocess(self, data: Dict[str, List[Dict]], config: InstructConfig) -> Dict[str, List[Dict]]:
+        """Apply preprocessing steps to all splits separately"""
+        processed_splits = {}
+
+        print(f"=== PREPROCESSING CONVERSATION DATA ===")
+        
+        for split_name, split_data in data.items():
+            print(f"Processing {split_name} split with {len(split_data)} items...")
+            
+            processed_data = []
+            processed_count = 0
+            skipped_count = 0
+
+            for i, item in enumerate(split_data):
+                processed_item = self._preprocess_item(item, config)
+                if processed_item is not None:
+                    processed_data.append(processed_item)
+                    processed_count += 1
+                else:
+                    skipped_count += 1
+
+            processed_splits[split_name] = processed_data
+            print(f"{split_name} - Preprocessed {processed_count} samples, skipped {skipped_count} samples")
+
+        return processed_splits
+
+    def _preprocess_item(self, item: Dict, config: InstructConfig) -> Optional[Dict]:
+        """Preprocess a single conversation item"""
+        conversation = item.get(config.conversation_field, [])
+        
+        if not isinstance(conversation, list) or not conversation:
+            return None
+        
+        # Validate conversation structure
+        valid_conversation = []
+        for turn in conversation:
+            if not isinstance(turn, dict):
+                continue
+            if "role" not in turn or "content" not in turn:
+                continue
+            if turn["role"] not in ["user", "assistant", "system"]:
+                continue
+            
+            content = str(turn["content"]).strip()
+            if len(content) < config.min_length or len(content) > config.max_length:
+                continue
+            
+            if config.clean_text:
+                content = self._clean_text(content)
+            
+            valid_conversation.append({
+                "role": turn["role"],
+                "content": content
+            })
+        
+        if len(valid_conversation) < 2:  # Need at least 2 turns for a conversation
+            return None
+        
+        return {"conversation": valid_conversation}
+
+    def _clean_text(self, text: str) -> str:
+        """Clean and normalize text"""
+        if not isinstance(text, str):
+            return ""
+        
+        # Remove extra whitespace
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text
+
+class CustomInstructDataLoader(BaseInstructDataLoader):
+    """Load custom conversation datasets from local files"""
+    
+    def load(self, config: InstructConfig) -> Dict[str, List[Dict]]:
+        """Load custom conversation dataset from local file and create splits"""
+        if not config.data_path:
+            raise ValueError("Data path is required for custom datasets")
+        
+        file_path = Path(config.data_path)
+        
+        if not file_path.exists():
+            raise FileNotFoundError(f"Data file not found: {file_path}")
+        
+        print(f"Loading custom conversation dataset: {file_path}")
+        
+        if config.data_format == "jsonl":
+            raw_data = self._load_jsonl(file_path, config)
+        elif config.data_format == "json":
+            raw_data = self._load_json(file_path, config)
+        else:
+            raise ValueError(f"Unsupported format: {config.data_format}")
+        
+        if config.max_samples:
+            raw_data = raw_data[:config.max_samples]
+        
+        print(f"Loaded {len(raw_data)} conversation samples from {file_path}")
+        
+        # Create splits from the raw data
+        splits_data = self._create_splits(raw_data, config)
+        
+        return splits_data
+    
+    def _create_splits(self, data: List[Dict], config: InstructConfig) -> Dict[str, List[Dict]]:
+        """Create train/validation/test splits from raw data"""
+        print(f"Creating splits from {len(data)} conversation samples...")
+        
+        # Handle very small datasets
+        if len(data) < 3:
+            print(f"Dataset has only {len(data)} samples. Using all data for training.")
+            return {
+                "train": data,
+                "validation": [],
+                "test": []
+            }
+        
+        # Calculate split sizes
+        total_samples = len(data)
+        
+        # Adjust split ratios if dataset is too small
+        if total_samples < 10:
+            config.train_split = 0.6
+            config.validation_split = 0.2
+            config.test_split = 0.2
+            print(f"Small dataset detected. Adjusted split ratios to: train={config.train_split}, val={config.validation_split}, test={config.test_split}")
+        
+        val_size = max(1, int(total_samples * config.validation_split))
+        test_size = max(1, int(total_samples * config.test_split))
+        train_size = total_samples - val_size - test_size
+        
+        # Ensure train split has at least 1 sample
+        if train_size < 1:
+            if val_size > 1:
+                val_size -= 1
+                train_size += 1
+            elif test_size > 1:
+                test_size -= 1
+                train_size += 1
+        
+        print(f"Split sizes: train={train_size}, validation={val_size}, test={test_size}")
+        
+        # Create splits
+        if val_size == 0 and test_size == 0:
+            splits_data = {
+                "train": data,
+                "validation": [],
+                "test": []
+            }
+        elif val_size == 0:
+            train_data, test_data = train_test_split(data, test_size=test_size, random_state=42)
+            splits_data = {
+                "train": train_data,
+                "validation": [],
+                "test": test_data
+            }
+        elif test_size == 0:
+            train_data, val_data = train_test_split(data, test_size=val_size, random_state=42)
+            splits_data = {
+                "train": train_data,
+                "validation": val_data,
+                "test": []
+            }
+        else:
+            # Full three-way split
+            train_data, temp_data = train_test_split(
+                data,
+                test_size=val_size + test_size,
+                random_state=42
+            )
+            
+            val_data, test_data = train_test_split(
+                temp_data,
+                test_size=test_size,
+                random_state=42
+            )
+            
+            splits_data = {
+                "train": train_data,
+                "validation": val_data,
+                "test": test_data
+            }
+        
+        print(f"Created conversation splits:")
+        print(f"  Train: {len(splits_data['train'])} samples")
+        print(f"  Validation: {len(splits_data['validation'])} samples")
+        print(f"  Test: {len(splits_data['test'])} samples")
+        
+        return splits_data
+    
+    def _load_jsonl(self, file_path: Path, config: InstructConfig) -> List[Dict]:
+        """Load JSONL file"""
+        data = []
+        with open(file_path, 'r', encoding=config.encoding) as f:
+            for line_num, line in enumerate(f, 1):
+                if line.strip():
+                    try:
+                        data.append(json.loads(line))
+                    except json.JSONDecodeError as e:
+                        print(f"Invalid JSON at line {line_num}: {e}")
+        return data
+    
+    def _load_json(self, file_path: Path, config: InstructConfig) -> List[Dict]:
+        """Load JSON file"""
+        with open(file_path, 'r', encoding=config.encoding) as f:
+            data = json.load(f)
+        
+        if isinstance(data, list):
+            return data
+        elif isinstance(data, dict) and "data" in data:
+            return data["data"]
+        else:
+            return [data]
+    
+    def preprocess(self, data: Dict[str, List[Dict]], config: InstructConfig) -> Dict[str, List[Dict]]:
+        """Apply preprocessing steps to all splits separately"""
+        processed_splits = {}
+        
+        print(f"=== PREPROCESSING CUSTOM CONVERSATION DATA ===")
+        
+        for split_name, split_data in data.items():
+            print(f"Processing {split_name} split with {len(split_data)} items...")
+            
+            processed_data = []
+            processed_count = 0
+            skipped_count = 0
+            
+            for i, item in enumerate(split_data):
+                processed_item = self._preprocess_item(item, config)
+                if processed_item is not None:
+                    processed_data.append(processed_item)
+                    processed_count += 1
+                else:
+                    skipped_count += 1
+                    if skipped_count <= 3:  # Log first few skipped items
+                        print(f"Skipped item {i} from {split_name}: {item}")
+            
+            processed_splits[split_name] = processed_data
+            print(f"{split_name} - Preprocessed {processed_count} samples, skipped {skipped_count} samples")
+        
+        return processed_splits
+    
+    def _preprocess_item(self, item: Dict, config: InstructConfig) -> Optional[Dict]:
+        """Preprocess a single conversation item"""
+        conversation = item.get(config.conversation_field, [])
+        
+        if not isinstance(conversation, list) or not conversation:
+            return None
+        
+        # Validate conversation structure
+        valid_conversation = []
+        for turn in conversation:
+            if not isinstance(turn, dict):
+                continue
+            if "role" not in turn or "content" not in turn:
+                continue
+            if turn["role"] not in ["user", "assistant", "system"]:
+                continue
+            
+            content = str(turn["content"]).strip()
+            if len(content) < config.min_length or len(content) > config.max_length:
+                continue
+            
+            if config.clean_text:
+                content = self._clean_text(content)
+            
+            valid_conversation.append({
+                "role": turn["role"],
+                "content": content
+            })
+        
+        if len(valid_conversation) < 2:  # Need at least 2 turns for a conversation
+            return None
+        
+        return {"conversation": valid_conversation}
+
+    def _clean_text(self, text: str) -> str:
+        """Clean and normalize text"""
+        if not isinstance(text, str):
+            return ""
+        
+        # Remove extra whitespace
+        text = re.sub(r'\s+', ' ', text).strip()
+        return text
+
+class InstructDataPipeline:
+    """Main instruction fine-tuning data pipeline"""
+    
+    def __init__(self):
+        self.validator = ConversationValidator()
+        self.hf_loader = HuggingFaceInstructDataLoader()
+        self.custom_loader = CustomInstructDataLoader()
+    
+    def create_config(
+        self,
+        data_source: str,
+        dataset_name: Optional[str] = None,
+        data_path: Optional[str] = None,
+        conversation_field: str = "conversation",
+        **kwargs
+    ) -> InstructConfig:
+        """Create instruction configuration"""
+        return InstructConfig(
+            data_source=data_source,
+            dataset_name=dataset_name,
+            data_path=data_path,
+            conversation_field=conversation_field,
+            **kwargs
+        )
+
+    def load_config_from_yaml(self, yaml_path: str) -> InstructConfig:
+        """Load configuration from YAML file"""
+        try:
+            config_dict = load_yaml_config(yaml_path)
+            
+            # Create configuration object from YAML data
+            config = InstructConfig(
+                data_source=config_dict.get('data_source', 'custom'),
+                dataset_name=config_dict.get('dataset_name'),
+                data_path=config_dict.get('data_path'),
+                data_format=config_dict.get('data_format', 'jsonl'),
+                conversation_field=config_dict.get('conversation_field', 'conversation'),
+                max_samples=config_dict.get('max_samples'),
+                train_split=config_dict.get('train_split', 0.8),
+                validation_split=config_dict.get('validation_split', 0.1),
+                test_split=config_dict.get('test_split', 0.1),
+                clean_text=config_dict.get('clean_text', True),
+                min_length=config_dict.get('min_length', 10),
+                max_length=config_dict.get('max_length', 2048),
+                output_format=config_dict.get('output_format', 'conversation'),
+                output_dir=config_dict.get('output_dir', './data/processed/instruct'),
+                hf_split=config_dict.get('hf_split', 'train'),
+                hf_cache_dir=config_dict.get('hf_cache_dir'),
+                test_split_from=config_dict.get('test_split_from', 'train'),
+                val_split_from=config_dict.get('val_split_from', 'train'),
+                encoding=config_dict.get('encoding', 'utf-8')
+            )
+            
+            print(f"Configuration loaded from YAML: {yaml_path}")
+            print(f"Output directory: {config.output_dir}")
+            print(f"Conversation field: {config.conversation_field}")
+            
+            return config
+            
+        except Exception as e:
+            print(f"Error loading configuration from YAML {yaml_path}: {e}")
+            raise
+    
+    def load_and_preprocess(self, config: InstructConfig) -> Tuple[Dict[str, List[Dict]], Dict[str, Any]]:
+        """Load and preprocess conversation data"""
+        
+        print(f"Starting conversation data loading and preprocessing...")
+        print(f"Data source: {config.data_source}")
+        
+        try:
+            # Load data
+            if config.data_source == "huggingface":
+                print("Loading HuggingFace conversation dataset...")
+                raw_splits = self.hf_loader.load(config)
+                print("Preprocessing HuggingFace conversation dataset...")
+                processed_splits = self.hf_loader.preprocess(raw_splits, config)
+            elif config.data_source == "custom":
+                print("Loading custom conversation dataset...")
+                raw_splits = self.custom_loader.load(config)
+                print("Preprocessing custom conversation dataset...")
+                processed_splits = self.custom_loader.preprocess(raw_splits, config)
+            else:
+                raise ValueError(f"Unsupported data source: {config.data_source}")
+            
+            print(f"Conversation data loading and preprocessing completed successfully")
+            
+            # Validate processed data
+            print("Validating processed conversation data...")
+            is_valid, errors = self.validator.validate_conversation_data(processed_splits, config, is_processed=True)
+            if not is_valid:
+                print("Conversation data validation failed:")
+                for error in errors:
+                    print(f"  - {error}")
+                raise ValueError("Conversation data validation failed")
+            
+            print("Conversation data validation passed")
+            
+            # Analyze dataset
+            print("Analyzing conversation dataset...")
+            analysis = self.validator.analyze_conversation_dataset(processed_splits, config, is_processed=True)
+            print("Conversation dataset analysis completed")
+            
+            return processed_splits, analysis
+            
+        except Exception as e:
+            print(f"Error in load_and_preprocess: {e}")
+            raise
+
+    def save_data(self, data: Dict[str, List[Dict]], output_dir: str, format: str = "jsonl"):
+        """Save processed conversation data splits to files"""
+        output_path = Path(output_dir)
+        output_path.mkdir(parents=True, exist_ok=True)
+        
+        for split_name, split_data in data.items():
+            if format == "jsonl":
+                output_file = output_path / f"{split_name}.jsonl"
+                with open(output_file, 'w', encoding='utf-8') as f:
+                    for item in split_data:
+                        f.write(json.dumps(item, ensure_ascii=False) + '\n')
+            elif format == "json":
+                output_file = output_path / f"{split_name}.json"
+                with open(output_file, 'w', encoding='utf-8') as f:
+                    json.dump(split_data, f, ensure_ascii=False, indent=2)
+            
+            print(f"Saved {len(split_data)} conversation samples to {output_file}")
+    
+    def run_pipeline(
+        self,
+        config: InstructConfig,
+        save_splits: bool = True
+    ) -> Dict[str, Any]:
+        """Run complete instruction data pipeline"""
+        
+        print("Starting instruction data pipeline...")
+        
+        # Load and preprocess data
+        processed_splits, analysis = self.load_and_preprocess(config)
+        
+        # Save data if requested
+        if save_splits:
+            output_dir = Path(config.output_dir)
+            self.save_data(processed_splits, str(output_dir))
+        
+        # Create result summary
+        result = {
+            "config": config,
+            "analysis": analysis,
+            "splits": {
+                split_name: len(split_data) for split_name, split_data in processed_splits.items()
+            },
+            "output_format": config.output_format,
+            "output_dir": config.output_dir,
+            "data": processed_splits,  # Include the actual processed data
+        }
+        
+        print("Instruction data pipeline completed successfully!")
+        return result
+
+def load_yaml_config(config_path: str) -> Dict[str, Any]:
+    """Load and parse YAML configuration file with proper structure handling"""
+    try:
+        with open(config_path, 'r', encoding='utf-8') as f:
+            yaml_data = yaml.safe_load(f)
+        
+        # Extract configuration from YAML structure
+        config_dict = {}
+        
+        # Handle task section
+        if 'task' in yaml_data:
+            task_data = yaml_data['task']
+            config_dict.update({
+                'task_name': task_data.get('name'),
+                'task_type': task_data.get('type')
+            })
+        
+        # Handle data section
+        if 'data' in yaml_data:
+            data_config = yaml_data['data']
+            config_dict.update({
+                'data_source': data_config.get('source'),
+                'dataset_name': data_config.get('dataset_name'),
+                'data_path': data_config.get('data_path'),
+                'data_format': data_config.get('data_format'),
+                'conversation_field': data_config.get('conversation_field'),
+                'max_samples': data_config.get('max_samples'),
+                'train_split': data_config.get('train_split'),
+                'validation_split': data_config.get('validation_split'),
+                'test_split': data_config.get('test_split'),
+                'clean_text': data_config.get('clean_text'),
+                'min_length': data_config.get('min_length'),
+                'max_length': data_config.get('max_length'),
+                'output_format': data_config.get('output_format'),
+                'output_dir': data_config.get('output_dir'),
+                'encoding': data_config.get('encoding')
+            })
+        
+        print(f"Successfully parsed YAML configuration from: {config_path}")
+        print(f"Extracted {len(config_dict)} configuration parameters")
+        
+        return config_dict
+        
+    except Exception as e:
+        print(f"Error loading YAML config from {config_path}: {e}")
+        raise
+
+def main():
+    """Main function with YAML configuration support"""
+    
+    parser = argparse.ArgumentParser(description="Instruction Data Processing Pipeline")
+    
+    # YAML configuration
+    parser.add_argument("--config", type=str, help="Path to YAML configuration file")
+    
+    # Data source arguments
+    parser.add_argument("--data-source", choices=["huggingface", "custom"], help="Data source")
+    parser.add_argument("--dataset-name", type=str, help="HuggingFace dataset name")
+    parser.add_argument("--data-path", type=str, help="Path to custom data file")
+    parser.add_argument("--data-format", choices=["jsonl", "json"], help="Data format")
+    
+    # Field mapping
+    parser.add_argument("--conversation-field", type=str, help="Conversation field name")
+    
+    # Data processing
+    parser.add_argument("--max-samples", type=int, help="Maximum samples to process")
+    parser.add_argument("--train-split", type=float, help="Training split ratio")
+    parser.add_argument("--validation-split", type=float, help="Validation split ratio")
+    parser.add_argument("--test-split", type=float, help="Test split ratio")
+    
+    # Output configuration
+    parser.add_argument("--output-dir", type=str, help="Output directory")
+    
+    # Logging
+    parser.add_argument("--log-level", choices=["DEBUG", "INFO", "WARNING", "ERROR"], default="INFO", help="Logging level")
+    
+    args = parser.parse_args()
+    
+    # Set up logging
+    # logging.basicConfig(
+    #     level=getattr(logging, args.log_level),
+    #     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    # )
+    
+    # Load configuration
+    config_dict = {}
+    
+    # Load YAML config if provided
+    if args.config:
+        try:
+            config_dict = load_yaml_config(args.config)
+        except Exception as e:
+            print(f"Error loading YAML config: {e}")
+            sys.exit(1)
+    
+    # Override YAML config with CLI arguments (similar to styling pipeline)
+    cli_overrides = {}
+    if args.data_source:
+        cli_overrides['data_source'] = args.data_source
+    if args.dataset_name:
+        cli_overrides['dataset_name'] = args.dataset_name
+    if args.data_path:
+        cli_overrides['data_path'] = args.data_path
+    if args.data_format:
+        cli_overrides['data_format'] = args.data_format
+    if args.conversation_field:
+        cli_overrides['conversation_field'] = args.conversation_field
+    if args.max_samples:
+        cli_overrides['max_samples'] = args.max_samples
+    if args.train_split:
+        cli_overrides['train_split'] = args.train_split
+    if args.validation_split:
+        cli_overrides['validation_split'] = args.validation_split
+    if args.test_split:
+        cli_overrides['test_split'] = args.test_split
+    if args.output_dir:
+        cli_overrides['output_dir'] = args.output_dir
+    
+    # Merge configurations
+    for key, value in cli_overrides.items():
+        if key in config_dict:
+            print(f"Overriding YAML config '{key}' with CLI value: {value}")
+        config_dict[key] = value
+    
+    # Validate required arguments
+    if not config_dict.get('data_source'):
+        parser.error("--data-source is required (either in YAML config or CLI)")
+    
+    if config_dict.get('data_source') == "huggingface" and not config_dict.get('dataset_name'):
+        parser.error("--dataset-name is required for HuggingFace datasets")
+    
+    if config_dict.get('data_source') == "custom" and not config_dict.get('data_path'):
+        parser.error("--data-path is required for custom datasets")
+    
+    # Create configuration object
+    config = InstructConfig(
+        data_source=config_dict.get('data_source', 'custom'),
+        dataset_name=config_dict.get('dataset_name'),
+        data_path=config_dict.get('data_path'),
+        data_format=config_dict.get('data_format', 'jsonl'),
+        conversation_field=config_dict.get('conversation_field', 'conversation'),
+        max_samples=config_dict.get('max_samples'),
+        train_split=config_dict.get('train_split', 0.8),
+        validation_split=config_dict.get('validation_split', 0.1),
+        test_split=config_dict.get('test_split', 0.1),
+        clean_text=config_dict.get('clean_text', True),
+        min_length=config_dict.get('min_length', 10),
+        max_length=config_dict.get('max_length', 2048),
+        output_format=config_dict.get('output_format', 'conversation'),
+        output_dir=config_dict.get('output_dir', './data/processed/instruct'),
+        hf_split=config_dict.get('hf_split', 'train'),
+        hf_cache_dir=config_dict.get('hf_cache_dir'),
+        test_split_from=config_dict.get('test_split_from', 'train'),
+        val_split_from=config_dict.get('val_split_from', 'train'),
+        encoding=config_dict.get('encoding', 'utf-8')
+    )
+    
+    # Initialize pipeline
+    pipeline = InstructDataPipeline()
+    
+    try:
+        print(f"Starting instruction data pipeline with {config.data_source} data source...")
+        if args.config:
+            print(f"Using YAML configuration: {args.config}")
+        print(f"Conversation field: {config.conversation_field}")
+        print()
+        
+        result = pipeline.run_pipeline(config, save_splits=True)
+        
+        print(f"✅ Pipeline completed successfully!")
+        print(f"  Data source: {config.data_source}")
+        if config.data_source == "huggingface":
+            print(f"  Dataset: {config.dataset_name}")
+        else:
+            print(f"  Data file: {config.data_path}")
+        print(f"  Total samples: {result['analysis']['overall']['total_samples']}")
+        print(f"  Split sizes: {result['analysis']['overall']['split_sizes']}")
+        print(f"  Output directory: {config.output_dir}")
+        print(f"  Conversation stats: {result['analysis']['overall']['conversation_stats']}")
+        
+    except Exception as e:
+        print(f"❌ Error running pipeline: {e}")
+        import traceback
+        print("Full error traceback:")
+        traceback.print_exc()
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

pipelines/instruct/.ipynb_checkpoints/inference-checkpoint.py

@@ -0,0 +1,393 @@
+#!/usr/bin/env python3
+"""
+Instruct Inference Pipeline using Trained Models
+Supports conversational inference with streaming and batch processing
+"""
+
+import os
+import sys
+import json
+import argparse
+from pathlib import Path
+from typing import Dict, Any, Optional, List, Union
+import yaml
+
+# Add the project root to the path
+sys.path.append(str(Path(__file__).parent.parent.parent))
+
+# Inference imports
+import torch
+from datasets import load_from_disk, Dataset
+from unsloth import FastLanguageModel
+from unsloth.chat_templates import get_chat_template
+from transformers import TextStreamer
+
+class InstructInference:
+    """Instruction fine-tuning inference using trained models"""
+    
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+        self.model = None
+        self.tokenizer = None
+        
+        # Set device
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(f"Using device: {self.device}")
+        
+        # Model parameters
+        self.model_output_dir = config.get('model_output_dir', './models/instruct')
+        self.base_model_name = config.get('base_model_name', 'unsloth/Qwen2.5-72B-Instruct')
+        self.max_seq_length = config.get('max_seq_length', 2048)
+        self.dtype = config.get('dtype', None)
+        self.load_in_4bit = config.get('load_in_4bit', True)
+        self.hf_token = config.get('hf_token', None)
+        
+        # Inference parameters
+        self.batch_size = config.get('batch_size', 1)
+        self.max_new_tokens = config.get('max_new_tokens', 128)
+        self.temperature = config.get('temperature', 1.5)
+        self.min_p = config.get('min_p', 0.1)
+        self.use_cache = config.get('use_cache', True)
+        
+        # Chat template
+        self.chat_template = config.get('chat_template', 'qwen-2.5')
+    
+    def load_model_and_tokenizer(self):
+        """Load the trained model and tokenizer"""
+        print("Loading trained instruction model and tokenizer...")
+        
+        try:
+            # Load the saved LoRA model
+            model_path = self.model_output_dir
+            print(f"Loading model from: {model_path}")
+            
+            # Check if the model directory exists
+            if not Path(model_path).exists():
+                raise FileNotFoundError(f"Model directory not found: {model_path}")
+            
+            # Load the model directly from the saved path
+            self.model, self.tokenizer = FastLanguageModel.from_pretrained(
+                model_name=model_path,
+                max_seq_length=self.max_seq_length,
+                dtype=self.dtype,
+                load_in_4bit=self.load_in_4bit,
+            )
+            
+            # Enable native 2x faster inference
+            FastLanguageModel.for_inference(self.model)
+            
+            print(f"✅ Model loaded from: {model_path}")
+            print(f"✅ Tokenizer loaded with vocab size: {self.tokenizer.vocab_size}")
+            
+        except Exception as e:
+            print(f"❌ Error loading model: {e}")
+            raise
+    
+    def setup_chat_template(self):
+        """Setup chat template for conversation formatting"""
+        print("Setting up chat template...")
+        
+        try:
+            self.tokenizer = get_chat_template(
+                self.tokenizer,
+                chat_template=self.chat_template,
+            )
+            
+            print(f"✅ Chat template configured: {self.chat_template}")
+            
+        except Exception as e:
+            print(f"❌ Error setting up chat template: {e}")
+            raise
+    
+    def format_messages(self, messages: List[Dict[str, str]]) -> str:
+        """Format messages using chat template"""
+        try:
+            # Apply chat template to format the conversation
+            formatted_prompt = self.tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,  # Add generation prompt for inference
+            )
+            
+            return formatted_prompt
+            
+        except Exception as e:
+            print(f"❌ Error formatting messages: {e}")
+            raise
+    
+    def generate_response(
+        self, 
+        messages: List[Dict[str, str]], 
+        max_new_tokens: Optional[int] = None,
+        temperature: Optional[float] = None,
+        stream: bool = False
+    ) -> str:
+        """Generate response using the trained instruction model"""
+        try:
+            # Use default values if not provided
+            max_tokens = max_new_tokens or self.max_new_tokens
+            temp = temperature or self.temperature
+            
+            # Format the messages
+            formatted_prompt = self.format_messages(messages)
+            print(f"Formatted prompt: {formatted_prompt[:200]}...")
+            
+            # Tokenize the input
+            inputs = self.tokenizer(
+                [formatted_prompt], 
+                return_tensors="pt"
+            ).to(self.device)
+            
+            if stream:
+                # Streaming generation
+                text_streamer = TextStreamer(self.tokenizer, skip_prompt=True)
+                print("Generating with streaming...")
+                _ = self.model.generate(
+                    input_ids=inputs.input_ids,
+                    streamer=text_streamer,
+                    max_new_tokens=max_tokens,
+                    use_cache=self.use_cache,
+                    temperature=temp,
+                    min_p=self.min_p
+                )
+                return ""  # Streaming output is handled by streamer
+            else:
+                # Non-streaming generation
+                print("Generating response...")
+                outputs = self.model.generate(
+                    input_ids=inputs.input_ids,
+                    max_new_tokens=max_tokens,
+                    use_cache=self.use_cache,
+                    temperature=temp,
+                    min_p=self.min_p,
+                    pad_token_id=self.tokenizer.eos_token_id
+                )
+                
+                # Decode the generated text
+                full_response = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+                
+                # Extract only the new generated response (remove the input prompt)
+                prompt_length = len(formatted_prompt)
+                response = full_response[prompt_length:].strip()
+                
+                return response
+                
+        except Exception as e:
+            print(f"❌ Error generating response: {e}")
+            raise
+    
+    def chat(self, user_input: str, conversation_history: Optional[List[Dict[str, str]]] = None, stream: bool = False) -> str:
+        """Have a chat conversation with the model"""
+        try:
+            # Initialize conversation history if not provided
+            if conversation_history is None:
+                conversation_history = []
+            
+            # Add user input to conversation
+            messages = conversation_history + [{"role": "user", "content": user_input}]
+            
+            print(f"User: {user_input}")
+            
+            if stream:
+                print("Assistant: ", end="", flush=True)
+                self.generate_response(messages, stream=True)
+                return ""
+            else:
+                # Generate response
+                response = self.generate_response(messages, stream=False)
+                print(f"Assistant: {response}")
+                return response
+                
+        except Exception as e:
+            print(f"❌ Error in chat: {e}")
+            raise
+    
+    def batch_inference(
+        self, 
+        conversations: List[List[Dict[str, str]]], 
+        max_new_tokens: Optional[int] = None
+    ) -> List[str]:
+        """Perform batch inference on multiple conversations"""
+        responses = []
+        
+        for i, messages in enumerate(conversations):
+            print(f"Processing conversation {i+1}/{len(conversations)}")
+            response = self.generate_response(messages, max_new_tokens)
+            responses.append(response)
+        
+        return responses
+    
+    def interactive_chat(self):
+        """Start an interactive chat session"""
+        print("🤖 Starting interactive chat session...")
+        print("Type 'quit', 'exit', or 'bye' to end the conversation.")
+        print("Type 'clear' to clear conversation history.")
+        print("Type 'stream on' or 'stream off' to toggle streaming.")
+        print("-" * 50)
+        
+        conversation_history = []
+        streaming = False
+        
+        while True:
+            try:
+                user_input = input("\n👤 You: ").strip()
+                
+                if user_input.lower() in ['quit', 'exit', 'bye']:
+                    print("👋 Goodbye!")
+                    break
+                elif user_input.lower() == 'clear':
+                    conversation_history = []
+                    print("🗑️ Conversation history cleared.")
+                    continue
+                elif user_input.lower() == 'stream on':
+                    streaming = True
+                    print("🔄 Streaming enabled.")
+                    continue
+                elif user_input.lower() == 'stream off':
+                    streaming = False
+                    print("⏸️ Streaming disabled.")
+                    continue
+                elif not user_input:
+                    continue
+                
+                # Generate response
+                if streaming:
+                    print("🤖 Assistant: ", end="", flush=True)
+                    self.chat(user_input, conversation_history, stream=True)
+                    # Add to history (we don't have the actual response text for streaming)
+                    conversation_history.extend([
+                        {"role": "user", "content": user_input},
+                        {"role": "assistant", "content": "[Streamed response]"}
+                    ])
+                else:
+                    response = self.chat(user_input, conversation_history, stream=False)
+                    # Add to history
+                    conversation_history.extend([
+                        {"role": "user", "content": user_input},
+                        {"role": "assistant", "content": response}
+                    ])
+                
+            except KeyboardInterrupt:
+                print("\n👋 Goodbye!")
+                break
+            except Exception as e:
+                print(f"❌ Error: {e}")
+                continue
+
+def load_inference_config(config_path: str) -> Dict[str, Any]:
+    """Load inference configuration from YAML file"""
+    try:
+        with open(config_path, 'r', encoding='utf-8') as f:
+            config = yaml.safe_load(f)
+        
+        # Extract inference configuration
+        inference_config = {}
+        
+        # Model configuration
+        if 'model' in config:
+            model_data = config['model']
+            inference_config.update({
+                'base_model_name': model_data.get('training_model', 'unsloth/Qwen2.5-72B-Instruct'),
+                'max_seq_length': model_data.get('training_max_seq_length', 2048),
+                'dtype': model_data.get('training_dtype'),
+                'load_in_4bit': model_data.get('training_load_in_4bit', True),
+                'hf_token': model_data.get('training_token')
+            })
+        
+        # Training configuration - to get model_output_dir
+        if 'training' in config:
+            training_data = config['training']
+            inference_config.update({
+                'model_output_dir': training_data.get('model_output_dir', './models/instruct')
+            })
+        
+        # Inference configuration
+        if 'inference' in config:
+            inference_data = config['inference']
+            inference_config.update({
+                'batch_size': inference_data.get('batch_size', 1),
+                'max_new_tokens': inference_data.get('max_new_tokens', 128),
+                'temperature': inference_data.get('temperature', 1.5),
+                'min_p': inference_data.get('min_p', 0.1),
+                'use_cache': inference_data.get('use_cache', True)
+            })
+        
+        # Chat template
+        inference_config.update({
+            'chat_template': 'qwen-2.5'  # Use Qwen chat template by default
+        })
+        
+        return inference_config
+        
+    except Exception as e:
+        print(f"Error loading inference config: {e}")
+        raise
+
+def main():
+    """Main inference function"""
+    parser = argparse.ArgumentParser(description="Instruction Inference Pipeline")
+    
+    # Configuration
+    parser.add_argument("--config", type=str, required=True, help="Path to YAML configuration file")
+    parser.add_argument("--interactive", action="store_true", help="Start interactive chat session")
+    parser.add_argument("--message", type=str, help="Single message to send to the model")
+    parser.add_argument("--max-tokens", type=int, help="Maximum new tokens to generate")
+    parser.add_argument("--stream", action="store_true", help="Enable streaming generation")
+    parser.add_argument("--temperature", type=float, help="Sampling temperature")
+    
+    args = parser.parse_args()
+    
+    try:
+        # Load configuration
+        print(f"Loading configuration from: {args.config}")
+        inference_config = load_inference_config(args.config)
+        
+        # Override with CLI arguments
+        if args.max_tokens:
+            inference_config['max_new_tokens'] = args.max_tokens
+        if args.temperature:
+            inference_config['temperature'] = args.temperature
+        
+        print("Inference configuration:")
+        for key, value in inference_config.items():
+            print(f"  {key}: {value}")
+        
+        # Initialize inference
+        inference = InstructInference(inference_config)
+        
+        # Load model and tokenizer
+        inference.load_model_and_tokenizer()
+        
+        # Setup chat template
+        inference.setup_chat_template()
+
+        # Run inference based on mode
+        if args.interactive:
+            # Interactive chat mode
+            inference.interactive_chat()
+        elif args.message:
+            # Single message mode
+            print("Running single message inference...")
+            messages = [{"role": "user", "content": args.message}]
+            
+            if args.stream:
+                print("User:", args.message)
+                print("Assistant: ", end="", flush=True)
+                inference.generate_response(messages, stream=True)
+            else:
+                response = inference.generate_response(messages, stream=False)
+                print(f"User: {args.message}")
+                print(f"Assistant: {response}")
+        else:
+            # Default to interactive mode if no specific mode is chosen
+            print("No specific mode chosen. Starting interactive chat...")
+            inference.interactive_chat()
+        
+    except Exception as e:
+        print(f"Inference failed: {e}")
+        import traceback
+        traceback.print_exc()
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

pipelines/instruct/.ipynb_checkpoints/train-checkpoint.py

@@ -0,0 +1,518 @@
+#!/usr/bin/env python3
+"""
+Instruct Training Pipeline using Unsloth and SFTTrainer
+Supports instruction fine-tuning with conversational data and LoRA fine-tuning
+"""
+
+import os
+import sys
+import json
+import argparse
+from pathlib import Path
+from typing import Dict, Any, Optional, List
+import yaml
+
+# Add the project root to the path
+sys.path.append(str(Path(__file__).parent.parent.parent))
+
+from utils.config.config_manager import ConfigManager
+
+# Training imports
+import torch
+from datasets import load_from_disk, Dataset
+from unsloth import FastLanguageModel #is_bfloat16_supported
+from unsloth.chat_templates import get_chat_template, standardize_sharegpt, train_on_responses_only
+from trl import SFTTrainer, SFTConfig
+from transformers import DataCollatorForSeq2Seq
+
+class InstructTrainer:
+    """Instruction fine-tuning trainer using Unsloth and SFTTrainer"""
+    
+    def __init__(self, config: Dict[str, Any]):
+        self.config = config
+        self.model = None
+        self.tokenizer = None
+        self.trainer = None
+        
+        # Set device
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        print(f"Using device: {self.device}")
+        
+        # Model parameters
+        self.model_name = config.get('model_name', 'unsloth/Qwen2.5-72B-Instruct')
+        self.max_seq_length = config.get('max_seq_length', 2048)
+        self.dtype = config.get('dtype', None)
+        self.load_in_4bit = config.get('load_in_4bit', True)
+        self.hf_token = config.get('hf_token', None)
+        
+        # LoRA parameters
+        self.lora_r = config.get('lora_r', 32)
+        self.lora_alpha = config.get('lora_alpha', 16)
+        self.lora_dropout = config.get('lora_dropout', 0)
+        self.target_modules = config.get('target_modules', [
+            "q_proj", "k_proj", "v_proj", "o_proj",
+            "gate_proj", "up_proj", "down_proj"
+        ])
+        
+        # Training arguments
+        self.batch_size = config.get('batch_size', 1)
+        self.gradient_accumulation_steps = config.get('gradient_accumulation_steps', 4)
+        self.learning_rate = config.get('learning_rate', 2e-4)
+        self.num_epochs = config.get('num_epochs', 1)
+        self.max_steps = config.get('max_steps', 30)
+        self.warmup_steps = config.get('warmup_steps', 5)
+        self.weight_decay = config.get('weight_decay', 0.01)
+        self.seed = config.get('seed', 3407)
+        
+        # Output paths
+        self.output_dir = config.get('output_dir', './outputs')
+        self.model_output_dir = config.get('model_output_dir', './models/instruct')
+        
+        # Chat template
+        self.chat_template = config.get('chat_template', 'qwen-2.5')
+        
+    def load_model_and_tokenizer(self):
+        """Load the pre-trained model and tokenizer"""
+        print("Loading model and tokenizer...")
+        
+        try:
+            self.model, self.tokenizer = FastLanguageModel.from_pretrained(
+                model_name=self.model_name,
+                max_seq_length=self.max_seq_length,
+                dtype=self.dtype,
+                load_in_4bit=self.load_in_4bit,
+                token=self.hf_token
+            )
+            
+            print(f"✅ Model loaded: {self.model_name}")
+            print(f"✅ Tokenizer loaded with vocab size: {self.tokenizer.vocab_size}")
+            
+        except Exception as e:
+            print(f"❌ Error loading model: {e}")
+            raise
+    
+    def setup_lora(self):
+        """Setup LoRA for efficient fine-tuning"""
+        print("Setting up LoRA configuration...")
+        
+        try:
+            self.model = FastLanguageModel.get_peft_model(
+                self.model,
+                r=self.lora_r,
+                target_modules=self.target_modules,
+                lora_alpha=self.lora_alpha,
+                lora_dropout=self.lora_dropout,
+                bias="none",
+                use_gradient_checkpointing="unsloth",
+                random_state=self.seed,
+                use_rslora=False,
+                loftq_config=None
+            )
+            
+            print(f"✅ LoRA configured with r={self.lora_r}, alpha={self.lora_alpha}")
+            
+        except Exception as e:
+            print(f"❌ Error setting up LoRA: {e}")
+            raise
+    
+    def setup_chat_template(self):
+        """Setup chat template for conversation formatting"""
+        print("Setting up chat template...")
+        
+        try:
+            self.tokenizer = get_chat_template(
+                self.tokenizer,
+                chat_template=self.chat_template,
+            )
+            
+            print(f"✅ Chat template configured: {self.chat_template}")
+            
+        except Exception as e:
+            print(f"❌ Error setting up chat template: {e}")
+            raise
+    
+    def load_dataset(self, dataset_path: str) -> Dataset:
+        """Load the conversation training dataset"""
+        print(f"Loading conversation dataset from: {dataset_path}")
+        
+        try:
+            if Path(dataset_path).exists():
+                # Check if it's a HuggingFace dataset directory
+                if (Path(dataset_path) / "dataset_info.json").exists():
+                    # Load from HuggingFace dataset directory
+                    dataset = load_from_disk(dataset_path)
+                    print(f"Loaded HuggingFace dataset from disk: {len(dataset)} samples")
+                else:
+                    # Load from processed conversation data files (JSONL format)
+                    print("Loading from processed conversation data files...")
+                    from datasets import Dataset
+                    import json
+                    
+                    all_data = []
+                    data_dir = Path(dataset_path)
+                    
+                    # Look for train.jsonl, validation.jsonl, test.jsonl
+                    for split_file in ["train.jsonl", "validation.jsonl", "test.jsonl"]:
+                        file_path = data_dir / split_file
+                        if file_path.exists():
+                            print(f"Loading {split_file}...")
+                            with open(file_path, 'r', encoding='utf-8') as f:
+                                for line in f:
+                                    if line.strip():
+                                        data = json.loads(line)
+                                        all_data.append(data)
+                    
+                    if not all_data:
+                        raise ValueError(f"No conversation data found in {dataset_path}")
+                    
+                    # Create HuggingFace dataset
+                    dataset = Dataset.from_list(all_data)
+                    print(f"Created HuggingFace dataset from {len(all_data)} conversation samples")
+            else:
+                # Try loading from HuggingFace Hub
+                print(f"Attempting to load from HuggingFace Hub: {dataset_path}")
+                dataset = Dataset.load_dataset(dataset_path, split="train")
+                print(f"Loaded from HuggingFace Hub: {len(dataset)} samples")
+            
+            print(f"Dataset loaded: {len(dataset)} samples")
+            print(f"Dataset features: {dataset.features}")
+            
+            # Verify required fields exist for conversation data
+            required_fields = ["conversation"]
+            missing_fields = [field for field in required_fields if field not in dataset.features]
+            if missing_fields:
+                raise ValueError(f"Missing required fields in conversation dataset: {missing_fields}")
+            
+            return dataset
+            
+        except Exception as e:
+            print(f"Error loading conversation dataset: {e}")
+            raise
+    
+    def format_dataset_for_training(self, dataset: Dataset) -> Dataset:
+        """Format conversation dataset for training using standardize_sharegpt and apply_chat_template"""
+        print("Formatting conversation dataset for training...")
+        
+        try:
+            # Standardize the ShareGPT format
+            print("Standardizing ShareGPT format...")
+            dataset = standardize_sharegpt(dataset)
+            
+            # Define the formatting function for chat templates
+            def formatting_prompts_func(examples):
+                convos = examples["conversation"]
+                texts = [
+                    self.tokenizer.apply_chat_template(
+                        convo, 
+                        tokenize=False, 
+                        add_generation_prompt=False
+                    ) for convo in convos
+                ]
+                return {"text": texts}
+            
+            # Apply the formatting function
+            print("Applying chat template formatting...")
+            dataset = dataset.map(formatting_prompts_func, batched=True)
+            
+            print(f"✅ Dataset formatted for training with {len(dataset)} samples")
+            print(f"Sample formatted text: {dataset[0]['text'][:200]}...")
+            
+            return dataset
+            
+        except Exception as e:
+            print(f"❌ Error formatting dataset: {e}")
+            raise
+    
+    def setup_trainer(self, train_dataset: Dataset):
+        """Setup the SFTTrainer for instruction fine-tuning"""
+        print("Setting up SFTTrainer for instruction fine-tuning...")
+        
+        try:
+            # SFT Configuration
+            sft_config = SFTConfig(
+                per_device_train_batch_size=self.batch_size,
+                gradient_accumulation_steps=self.gradient_accumulation_steps,
+                warmup_steps=self.warmup_steps,
+                max_steps=self.max_steps,
+                learning_rate=self.learning_rate,
+                logging_steps=1,
+                optim="paged_adamw_8bit",
+                weight_decay=self.weight_decay,
+                lr_scheduler_type="linear",
+                seed=self.seed,
+                output_dir=self.output_dir,
+                report_to="none",  # Disable wandb for now
+            )
+            
+            print("SFT Configuration:")
+            print(f"  batch_size: {self.batch_size}")
+            print(f"  gradient_accumulation_steps: {self.gradient_accumulation_steps}")
+            print(f"  warmup_steps: {self.warmup_steps}")
+            print(f"  max_steps: {self.max_steps}")
+            print(f"  learning_rate: {self.learning_rate}")
+            
+            # Create SFTTrainer
+            self.trainer = SFTTrainer(
+                model=self.model,
+                tokenizer=self.tokenizer,
+                train_dataset=train_dataset,
+                dataset_text_field="text",
+                max_seq_length=self.max_seq_length,
+                data_collator=DataCollatorForSeq2Seq(tokenizer=self.tokenizer),
+                packing=False,  # Disable packing for conversation data
+                args=sft_config,
+            )
+            
+            print("✅ SFTTrainer configured successfully")
+            
+        except Exception as e:
+            print(f"❌ Error setting up trainer: {e}")
+            import traceback
+            print("Full error traceback:")
+            traceback.print_exc()
+            raise
+    
+    def setup_response_only_training(self):
+        """Setup training to only learn from assistant responses"""
+        print("Setting up response-only training...")
+        
+        try:
+            # Configure trainer to only train on responses
+            self.trainer = train_on_responses_only(
+                self.trainer,
+                instruction_part="<|im_start|>user\n",
+                response_part="<|im_start|>assistant\n",
+            )
+            
+            print("✅ Response-only training configured")
+            
+        except Exception as e:
+            print(f"❌ Error setting up response-only training: {e}")
+            raise
+    
+    def train(self, dataset_path: str):
+        """Run the instruction fine-tuning process"""
+        print("🚀 Starting instruction fine-tuning process...")
+        
+        try:
+            # Load model and tokenizer
+            print("Step 1: Loading model and tokenizer...")
+            self.load_model_and_tokenizer()
+            
+            # Setup LoRA
+            print("Step 2: Setting up LoRA...")
+            self.setup_lora()
+            
+            # Setup chat template
+            print("Step 3: Setting up chat template...")
+            self.setup_chat_template()
+            
+            # Load dataset
+            print(f"Step 4: Loading conversation dataset from: {dataset_path}")
+            train_dataset = self.load_dataset(dataset_path)
+            
+            # Format dataset for training
+            print("Step 5: Formatting dataset for training...")
+            formatted_dataset = self.format_dataset_for_training(train_dataset)
+            
+            # Setup trainer
+            print("Step 6: Setting up trainer...")
+            self.setup_trainer(formatted_dataset)
+            
+            # Setup response-only training (optional but recommended for chat models)
+            print("Step 7: Setting up response-only training...")
+            self.setup_response_only_training()
+            
+            # Start training
+            print("Step 8: Starting training...")
+            trainer_stats = self.trainer.train()
+            
+            print("✅ Instruction fine-tuning completed successfully!")
+            print(f"Training stats: {trainer_stats}")
+            
+            # Save the model
+            self.save_model()
+            
+            return trainer_stats
+            
+        except Exception as e:
+            print(f"❌ Instruction fine-tuning failed: {e}")
+            import traceback
+            print("Full error traceback:")
+            traceback.print_exc()
+            raise
+    
+    def save_model(self):
+        """Save the trained instruction model"""
+        print("Saving trained instruction model...")
+        
+        try:
+            # Create output directory
+            Path(self.model_output_dir).mkdir(parents=True, exist_ok=True)
+            
+            # Save model and tokenizer
+            self.model.save_pretrained(self.model_output_dir)
+            self.tokenizer.save_pretrained(self.model_output_dir)
+            
+            # Save training config
+            config_path = Path(self.model_output_dir) / "training_config.json"
+            with open(config_path, 'w') as f:
+                json.dump(self.config, f, indent=2)
+            
+            print(f"✅ Instruction model saved to: {self.model_output_dir}")
+            print(f"✅ You can now use this model for inference")
+            
+        except Exception as e:
+            print(f"❌ Error saving model: {e}")
+            raise
+    
+    def prepare_for_inference(self):
+        """Prepare model for inference"""
+        print("Preparing model for inference...")
+        
+        try:
+            FastLanguageModel.for_inference(self.model)
+            print("✅ Model prepared for inference")
+            
+        except Exception as e:
+            print(f"❌ Error preparing for inference: {e}")
+            raise
+
+def load_training_config(yaml_path: str) -> Dict[str, Any]:
+    """Load training configuration from YAML file"""
+    try:
+        with open(yaml_path, 'r') as f:
+            config = yaml.safe_load(f)
+        
+        training_config = {}
+        
+        # Model configuration - extract from model section
+        if 'model' in config:
+            model_config = config['model']
+            training_config.update({
+                'model_name': model_config.get('name', 'unsloth/Qwen2.5-72B-Instruct'),
+                'max_seq_length': int(model_config.get('max_seq_length', 2048)),
+                'dtype': model_config.get('dtype', None),
+                'load_in_4bit': model_config.get('load_in_4bit', True),
+                'hf_token': model_config.get('token', None)
+            })
+        
+        # Training configuration - extract from training section
+        if 'training' in config:
+            training_data = config['training']
+            print("Training data from YAML:")
+            print(f"  num_epochs: {training_data.get('num_epochs')} (type: {type(training_data.get('num_epochs'))})")
+            print(f"  batch_size: {training_data.get('batch_size')} (type: {type(training_data.get('batch_size'))})")
+            print(f"  learning_rate: {training_data.get('learning_rate')} (type: {type(training_data.get('learning_rate'))})")
+            print(f"  weight_decay: {training_data.get('weight_decay')} (type: {type(training_data.get('weight_decay'))})")
+            print(f"  warmup_steps: {training_data.get('warmup_steps')} (type: {type(training_data.get('warmup_steps'))})")
+            print(f"  max_steps: {training_data.get('max_steps')} (type: {type(training_data.get('max_steps'))})")
+            print(f"  gradient_accumulation_steps: {training_data.get('gradient_accumulation_steps')} (type: {type(training_data.get('gradient_accumulation_steps'))})")
+            print(f"  seed: {training_data.get('seed')} (type: {type(training_data.get('seed'))})")
+            print(f"  model_output_dir: {training_data.get('model_output_dir')} (type: {type(training_data.get('model_output_dir'))})")
+            
+            training_config.update({
+                'num_epochs': int(training_data.get('num_epochs', 1)),
+                'batch_size': int(training_data.get('batch_size', 1)),
+                'learning_rate': float(training_data.get('learning_rate', 2e-4)),
+                'weight_decay': float(training_data.get('weight_decay', 0.01)),
+                'warmup_steps': int(training_data.get('warmup_steps', 5)),
+                'max_steps': int(training_data.get('max_steps', 30)),
+                'gradient_accumulation_steps': int(training_data.get('gradient_accumulation_steps', 4)),
+                'lr_scheduler_type': training_data.get('lr_scheduler_type', 'linear'),
+                'seed': int(training_data.get('seed', 3407)),
+                'model_output_dir': training_data.get('model_output_dir', './models/instruct'),
+                # LoRA configuration
+                'lora_r': int(training_data.get('lora_r', 32)),
+                'lora_alpha': int(training_data.get('lora_alpha', 16)),
+                'lora_dropout': float(training_data.get('lora_dropout', 0)),
+                'target_modules': training_data.get('target_modules', [
+                    "q_proj", "k_proj", "v_proj", "o_proj",
+                    "gate_proj", "up_proj", "down_proj"
+                ])
+            })
+        
+        # Data configuration - use output_dir from data section
+        if 'data' in config:
+            data_config = config['data']
+            output_dir = data_config.get('output_dir', './data/processed/instruct')
+            training_config.update({
+                'data_output_dir': output_dir,
+                'dataset_path': output_dir,  # Default dataset path is the output_dir
+            })
+        
+        # Output configuration
+        training_config.update({
+            'output_dir': './outputs',
+            'chat_template': 'qwen-2.5'  # Use Qwen chat template by default
+        })
+        
+        print("Final training_config:")
+        for key, value in training_config.items():
+            print(f"  {key}: {value} (type: {type(value)})")
+        
+        return training_config
+        
+    except Exception as e:
+        print(f"Error loading training config: {e}")
+        raise
+
+def main():
+    """Main training function"""
+    parser = argparse.ArgumentParser(description="Instruction Fine-tuning Training Pipeline")
+    
+    # Configuration
+    parser.add_argument("--config", type=str, required=True, help="Path to YAML configuration file")
+    parser.add_argument("--dataset", type=str, help="Path to training dataset (conversation data path)")
+    parser.add_argument("--output-dir", type=str, help="Output directory for model")
+    parser.add_argument("--epochs", type=int, help="Number of training epochs")
+    parser.add_argument("--batch-size", type=int, help="Training batch size")
+    parser.add_argument("--learning-rate", type=float, help="Learning rate")
+    parser.add_argument("--max-steps", type=int, help="Maximum training steps")
+    
+    args = parser.parse_args()
+    
+    # Setup logging replaced with print statements
+    
+    try:
+        # Load configuration
+        print(f"Loading configuration from: {args.config}")
+        training_config = load_training_config(args.config)
+        
+        # Override with CLI arguments
+        if args.output_dir:
+            training_config['model_output_dir'] = args.output_dir
+        if args.epochs:
+            training_config['num_epochs'] = int(args.epochs)
+        if args.batch_size:
+            training_config['batch_size'] = int(args.batch_size)
+        if args.learning_rate:
+            training_config['learning_rate'] = float(args.learning_rate)
+        if args.max_steps:
+            training_config['max_steps'] = int(args.max_steps)
+        
+        # Determine dataset path: CLI argument takes precedence, then YAML config
+        dataset_path = args.dataset or training_config.get('dataset_path')
+        if not dataset_path:
+            print("No dataset path provided. Use --dataset or ensure output_dir is set in YAML config.")
+            sys.exit(1)
+        
+        print("Training configuration:")
+        for key, value in training_config.items():
+            print(f"  {key}: {value}")
+        print(f"  Dataset path: {dataset_path}")
+        
+        # Initialize trainer
+        trainer = InstructTrainer(training_config)
+        
+        # Start training
+        trainer.train(dataset_path)
+        
+        print("Instruction fine-tuning completed successfully!")
+        
+    except Exception as e:
+        print(f"Instruction fine-tuning failed: {e}")
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()

pipelines/instruct/data_processor.py

@@ -7,16 +7,12 @@ from datasets import Dataset, load_dataset
 import os
 from dataclasses import dataclass
 from abc import ABC, abstractmethod
-import logging
 from sklearn.model_selection import train_test_split
 import re
 import argparse
 import sys
 import yaml
 
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-
 @dataclass
 class InstructConfig:
     """Configuration for instruction fine-tuning tasks"""
@@ -75,7 +71,7 @@ class ConversationValidator:
             return False, errors
 
         total_samples = sum(len(split_data) for split_data in data.values())
-        logger.info(f"Validating {total_samples} total samples across all splits...")
+        print(f"Validating {total_samples} total samples across all splits...")
 
         # Determine field names based on whether data is processed or not
         conversation_field = "conversation" if not is_processed else "conversation"
@@ -83,10 +79,10 @@ class ConversationValidator:
         # Validate each split
         for split_name, split_data in data.items():
             if not split_data:
-                logger.info(f"Skipping validation for empty {split_name} split")
+                print(f"Skipping validation for empty {split_name} split")
                 continue
                 
-            logger.info(f"Validating {split_name} split with {len(split_data)} samples...")
+            print(f"Validating {split_name} split with {len(split_data)} samples...")
             
             # Check required fields
             missing_conversation_count = 0
@@ -117,19 +113,19 @@ class ConversationValidator:
                             if "role" in turn and turn["role"] not in ["user", "assistant", "system"]:
                                 errors.append(f"Invalid role '{turn['role']}' in conversation turn {j}, {split_name} split, item {i}. Must be 'user', 'assistant', or 'system'")
 
-            logger.info(f"{split_name} - Items missing conversation field: {missing_conversation_count}")
+            print(f"{split_name} - Items missing conversation field: {missing_conversation_count}")
 
             # Show sample of processed data for debugging
             if split_data:
-                logger.info(f"Sample conversation from {split_name}:")
+                print(f"Sample conversation from {split_name}:")
                 for i in range(min(2, len(split_data))):
                     item = split_data[i]
                     conversation = item.get(conversation_field, [])
-                    logger.info(f"  Item {i} conversation length: {len(conversation)} turns")
+                    print(f"  Item {i} conversation length: {len(conversation)} turns")
                     for j, turn in enumerate(conversation[:3]):  # Show first 3 turns
                         role = turn.get("role", "unknown")
                         content = turn.get("content", "")[:100] + "..." if len(turn.get("content", "")) > 100 else turn.get("content", "")
-                        logger.info(f"    Turn {j}: {role} -> '{content}'")
+                        print(f"    Turn {j}: {role} -> '{content}'")
 
         return len(errors) == 0, errors
 
@@ -242,7 +238,7 @@ class HuggingFaceInstructDataLoader(BaseInstructDataLoader):
         if not config.dataset_name:
             raise ValueError("Dataset name is required for Hugging Face datasets")
 
-        logger.info(f"Loading Hugging Face conversation dataset: {config.dataset_name}")
+        print(f"Loading Hugging Face conversation dataset: {config.dataset_name}")
 
         try:
             dataset = load_dataset(
@@ -251,7 +247,7 @@ class HuggingFaceInstructDataLoader(BaseInstructDataLoader):
             )
 
             available_splits = list(dataset.keys())
-            logger.info(f"Available splits in dataset: {available_splits}")
+            print(f"Available splits in dataset: {available_splits}")
 
             splits_data = {
                 "train": [],
@@ -262,10 +258,10 @@ class HuggingFaceInstructDataLoader(BaseInstructDataLoader):
             # Handle train split
             if "train" in available_splits:
                 train_dataset = dataset["train"]
-                logger.info(f"Using 'train' split with {len(train_dataset)} samples")
+                print(f"Using 'train' split with {len(train_dataset)} samples")
                 splits_data["train"] = list(train_dataset)
             else:
-                logger.error("No 'train' split found in dataset!")
+                print("No 'train' split found in dataset!")
                 raise ValueError(f"Dataset {config.dataset_name} does not have a 'train' split")
 
             # Handle validation and test splits (similar logic to styling pipeline)
@@ -277,23 +273,23 @@ class HuggingFaceInstructDataLoader(BaseInstructDataLoader):
                     if splits_data[split_name]:
                         original_size = len(splits_data[split_name])
                         splits_data[split_name] = splits_data[split_name][:config.max_samples]
-                        logger.info(f"Limited {split_name} split from {original_size} to {len(splits_data[split_name])} samples")
+                        print(f"Limited {split_name} split from {original_size} to {len(splits_data[split_name])} samples")
 
-            logger.info(f"Successfully loaded dataset {config.dataset_name}")
+            print(f"Successfully loaded dataset {config.dataset_name}")
             return splits_data
 
         except Exception as e:
-            logger.error(f"Error loading dataset {config.dataset_name}: {e}")
+            print(f"Error loading dataset {config.dataset_name}: {e}")
             raise
 
     def preprocess(self, data: Dict[str, List[Dict]], config: InstructConfig) -> Dict[str, List[Dict]]:
         """Apply preprocessing steps to all splits separately"""
         processed_splits = {}
 
-        logger.info(f"=== PREPROCESSING CONVERSATION DATA ===")
+        print(f"=== PREPROCESSING CONVERSATION DATA ===")
         
         for split_name, split_data in data.items():
-            logger.info(f"Processing {split_name} split with {len(split_data)} items...")
+            print(f"Processing {split_name} split with {len(split_data)} items...")
             
             processed_data = []
             processed_count = 0
@@ -308,7 +304,7 @@ class HuggingFaceInstructDataLoader(BaseInstructDataLoader):
                     skipped_count += 1
 
             processed_splits[split_name] = processed_data
-            logger.info(f"{split_name} - Preprocessed {processed_count} samples, skipped {skipped_count} samples")
+            print(f"{split_name} - Preprocessed {processed_count} samples, skipped {skipped_count} samples")
 
         return processed_splits
 
@@ -368,7 +364,7 @@ class CustomInstructDataLoader(BaseInstructDataLoader):
         if not file_path.exists():
             raise FileNotFoundError(f"Data file not found: {file_path}")
         
-        logger.info(f"Loading custom conversation dataset: {file_path}")
+        print(f"Loading custom conversation dataset: {file_path}")
         
         if config.data_format == "jsonl":
             raw_data = self._load_jsonl(file_path, config)
@@ -380,7 +376,7 @@ class CustomInstructDataLoader(BaseInstructDataLoader):
         if config.max_samples:
             raw_data = raw_data[:config.max_samples]
         
-        logger.info(f"Loaded {len(raw_data)} conversation samples from {file_path}")
+        print(f"Loaded {len(raw_data)} conversation samples from {file_path}")
         
         # Create splits from the raw data
         splits_data = self._create_splits(raw_data, config)
@@ -389,11 +385,11 @@ class CustomInstructDataLoader(BaseInstructDataLoader):
     
     def _create_splits(self, data: List[Dict], config: InstructConfig) -> Dict[str, List[Dict]]:
         """Create train/validation/test splits from raw data"""
-        logger.info(f"Creating splits from {len(data)} conversation samples...")
+        print(f"Creating splits from {len(data)} conversation samples...")
         
         # Handle very small datasets
         if len(data) < 3:
-            logger.warning(f"Dataset has only {len(data)} samples. Using all data for training.")
+            print(f"Dataset has only {len(data)} samples. Using all data for training.")
             return {
                 "train": data,
                 "validation": [],
@@ -408,7 +404,7 @@ class CustomInstructDataLoader(BaseInstructDataLoader):
             config.train_split = 0.6
             config.validation_split = 0.2
             config.test_split = 0.2
-            logger.info(f"Small dataset detected. Adjusted split ratios to: train={config.train_split}, val={config.validation_split}, test={config.test_split}")
+            print(f"Small dataset detected. Adjusted split ratios to: train={config.train_split}, val={config.validation_split}, test={config.test_split}")
         
         val_size = max(1, int(total_samples * config.validation_split))
         test_size = max(1, int(total_samples * config.test_split))
@@ -423,7 +419,7 @@ class CustomInstructDataLoader(BaseInstructDataLoader):
                 test_size -= 1
                 train_size += 1
         
-        logger.info(f"Split sizes: train={train_size}, validation={val_size}, test={test_size}")
+        print(f"Split sizes: train={train_size}, validation={val_size}, test={test_size}")
         
         # Create splits
         if val_size == 0 and test_size == 0:
@@ -466,10 +462,10 @@ class CustomInstructDataLoader(BaseInstructDataLoader):
                 "test": test_data
             }
         
-        logger.info(f"Created conversation splits:")
-        logger.info(f"  Train: {len(splits_data['train'])} samples")
-        logger.info(f"  Validation: {len(splits_data['validation'])} samples")
-        logger.info(f"  Test: {len(splits_data['test'])} samples")
+        print(f"Created conversation splits:")
+        print(f"  Train: {len(splits_data['train'])} samples")
+        print(f"  Validation: {len(splits_data['validation'])} samples")
+        print(f"  Test: {len(splits_data['test'])} samples")
         
         return splits_data
     
@@ -482,7 +478,7 @@ class CustomInstructDataLoader(BaseInstructDataLoader):
                     try:
                         data.append(json.loads(line))
                     except json.JSONDecodeError as e:
-                        logger.warning(f"Invalid JSON at line {line_num}: {e}")
+                        print(f"Invalid JSON at line {line_num}: {e}")
         return data
     
     def _load_json(self, file_path: Path, config: InstructConfig) -> List[Dict]:
@@ -501,10 +497,10 @@ class CustomInstructDataLoader(BaseInstructDataLoader):
         """Apply preprocessing steps to all splits separately"""
         processed_splits = {}
         
-        logger.info(f"=== PREPROCESSING CUSTOM CONVERSATION DATA ===")
+        print(f"=== PREPROCESSING CUSTOM CONVERSATION DATA ===")
         
         for split_name, split_data in data.items():
-            logger.info(f"Processing {split_name} split with {len(split_data)} items...")
+            print(f"Processing {split_name} split with {len(split_data)} items...")
             
             processed_data = []
             processed_count = 0
@@ -518,10 +514,10 @@ class CustomInstructDataLoader(BaseInstructDataLoader):
                 else:
                     skipped_count += 1
                     if skipped_count <= 3:  # Log first few skipped items
-                        logger.info(f"Skipped item {i} from {split_name}: {item}")
+                        print(f"Skipped item {i} from {split_name}: {item}")
             
             processed_splits[split_name] = processed_data
-            logger.info(f"{split_name} - Preprocessed {processed_count} samples, skipped {skipped_count} samples")
+            print(f"{split_name} - Preprocessed {processed_count} samples, skipped {skipped_count} samples")
         
         return processed_splits
     
@@ -621,59 +617,59 @@ class InstructDataPipeline:
                 encoding=config_dict.get('encoding', 'utf-8')
             )
             
-            logger.info(f"Configuration loaded from YAML: {yaml_path}")
-            logger.info(f"Output directory: {config.output_dir}")
-            logger.info(f"Conversation field: {config.conversation_field}")
+            print(f"Configuration loaded from YAML: {yaml_path}")
+            print(f"Output directory: {config.output_dir}")
+            print(f"Conversation field: {config.conversation_field}")
             
             return config
             
         except Exception as e:
-            logger.error(f"Error loading configuration from YAML {yaml_path}: {e}")
+            print(f"Error loading configuration from YAML {yaml_path}: {e}")
             raise
     
     def load_and_preprocess(self, config: InstructConfig) -> Tuple[Dict[str, List[Dict]], Dict[str, Any]]:
         """Load and preprocess conversation data"""
         
-        logger.info(f"Starting conversation data loading and preprocessing...")
-        logger.info(f"Data source: {config.data_source}")
+        print(f"Starting conversation data loading and preprocessing...")
+        print(f"Data source: {config.data_source}")
         
         try:
             # Load data
             if config.data_source == "huggingface":
-                logger.info("Loading HuggingFace conversation dataset...")
+                print("Loading HuggingFace conversation dataset...")
                 raw_splits = self.hf_loader.load(config)
-                logger.info("Preprocessing HuggingFace conversation dataset...")
+                print("Preprocessing HuggingFace conversation dataset...")
                 processed_splits = self.hf_loader.preprocess(raw_splits, config)
             elif config.data_source == "custom":
-                logger.info("Loading custom conversation dataset...")
+                print("Loading custom conversation dataset...")
                 raw_splits = self.custom_loader.load(config)
-                logger.info("Preprocessing custom conversation dataset...")
+                print("Preprocessing custom conversation dataset...")
                 processed_splits = self.custom_loader.preprocess(raw_splits, config)
             else:
                 raise ValueError(f"Unsupported data source: {config.data_source}")
             
-            logger.info(f"Conversation data loading and preprocessing completed successfully")
+            print(f"Conversation data loading and preprocessing completed successfully")
             
             # Validate processed data
-            logger.info("Validating processed conversation data...")
+            print("Validating processed conversation data...")
             is_valid, errors = self.validator.validate_conversation_data(processed_splits, config, is_processed=True)
             if not is_valid:
-                logger.error("Conversation data validation failed:")
+                print("Conversation data validation failed:")
                 for error in errors:
-                    logger.error(f"  - {error}")
+                    print(f"  - {error}")
                 raise ValueError("Conversation data validation failed")
             
-            logger.info("Conversation data validation passed")
+            print("Conversation data validation passed")
             
             # Analyze dataset
-            logger.info("Analyzing conversation dataset...")
+            print("Analyzing conversation dataset...")
             analysis = self.validator.analyze_conversation_dataset(processed_splits, config, is_processed=True)
-            logger.info("Conversation dataset analysis completed")
+            print("Conversation dataset analysis completed")
             
             return processed_splits, analysis
             
         except Exception as e:
-            logger.error(f"Error in load_and_preprocess: {e}")
+            print(f"Error in load_and_preprocess: {e}")
             raise
 
     def save_data(self, data: Dict[str, List[Dict]], output_dir: str, format: str = "jsonl"):
@@ -692,7 +688,7 @@ class InstructDataPipeline:
                 with open(output_file, 'w', encoding='utf-8') as f:
                     json.dump(split_data, f, ensure_ascii=False, indent=2)
             
-            logger.info(f"Saved {len(split_data)} conversation samples to {output_file}")
+            print(f"Saved {len(split_data)} conversation samples to {output_file}")
     
     def run_pipeline(
         self,
@@ -701,7 +697,7 @@ class InstructDataPipeline:
     ) -> Dict[str, Any]:
         """Run complete instruction data pipeline"""
         
-        logger.info("Starting instruction data pipeline...")
+        print("Starting instruction data pipeline...")
         
         # Load and preprocess data
         processed_splits, analysis = self.load_and_preprocess(config)
@@ -723,7 +719,7 @@ class InstructDataPipeline:
             "data": processed_splits,  # Include the actual processed data
         }
         
-        logger.info("Instruction data pipeline completed successfully!")
+        print("Instruction data pipeline completed successfully!")
         return result
 
 def load_yaml_config(config_path: str) -> Dict[str, Any]:
@@ -764,13 +760,13 @@ def load_yaml_config(config_path: str) -> Dict[str, Any]:
                 'encoding': data_config.get('encoding')
             })
         
-        logger.info(f"Successfully parsed YAML configuration from: {config_path}")
-        logger.info(f"Extracted {len(config_dict)} configuration parameters")
+        print(f"Successfully parsed YAML configuration from: {config_path}")
+        print(f"Extracted {len(config_dict)} configuration parameters")
         
         return config_dict
         
     except Exception as e:
-        logger.error(f"Error loading YAML config from {config_path}: {e}")
+        print(f"Error loading YAML config from {config_path}: {e}")
         raise
 
 def main():
@@ -805,10 +801,10 @@ def main():
     args = parser.parse_args()
     
     # Set up logging
-    logging.basicConfig(
-        level=getattr(logging, args.log_level),
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-    )
+    # logging.basicConfig(
+    #     level=getattr(logging, args.log_level),
+    #     format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    # )
     
     # Load configuration
     config_dict = {}
@@ -818,7 +814,7 @@ def main():
         try:
             config_dict = load_yaml_config(args.config)
         except Exception as e:
-            logger.error(f"Error loading YAML config: {e}")
+            print(f"Error loading YAML config: {e}")
             sys.exit(1)
     
     # Override YAML config with CLI arguments (similar to styling pipeline)
@@ -847,7 +843,7 @@ def main():
     # Merge configurations
     for key, value in cli_overrides.items():
         if key in config_dict:
-            logger.info(f"Overriding YAML config '{key}' with CLI value: {value}")
+            print(f"Overriding YAML config '{key}' with CLI value: {value}")
         config_dict[key] = value
     
     # Validate required arguments

pipelines/instruct/train.py

@@ -7,7 +7,6 @@ Supports instruction fine-tuning with conversational data and LoRA fine-tuning
 import os
 import sys
 import json
-import logging
 import argparse
 from pathlib import Path
 from typing import Dict, Any, Optional, List
@@ -21,13 +20,11 @@ from utils.config.config_manager import ConfigManager
 # Training imports
 import torch
 from datasets import load_from_disk, Dataset
-from unsloth import FastLanguageModel, is_bfloat16_supported
+from unsloth import FastLanguageModel #is_bfloat16_supported
 from unsloth.chat_templates import get_chat_template, standardize_sharegpt, train_on_responses_only
 from trl import SFTTrainer, SFTConfig
 from transformers import DataCollatorForSeq2Seq
 
-logger = logging.getLogger(__name__)
-
 class InstructTrainer:
     """Instruction fine-tuning trainer using Unsloth and SFTTrainer"""
     
@@ -39,7 +36,7 @@ class InstructTrainer:
         
         # Set device
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        logger.info(f"Using device: {self.device}")
+        print(f"Using device: {self.device}")
         
         # Model parameters
         self.model_name = config.get('model_name', 'unsloth/Qwen2.5-72B-Instruct')
@@ -76,7 +73,7 @@ class InstructTrainer:
         
     def load_model_and_tokenizer(self):
         """Load the pre-trained model and tokenizer"""
-        logger.info("Loading model and tokenizer...")
+        print("Loading model and tokenizer...")
         
         try:
             self.model, self.tokenizer = FastLanguageModel.from_pretrained(
@@ -87,16 +84,16 @@ class InstructTrainer:
                 token=self.hf_token
             )
             
-            logger.info(f"✅ Model loaded: {self.model_name}")
-            logger.info(f"✅ Tokenizer loaded with vocab size: {self.tokenizer.vocab_size}")
+            print(f"✅ Model loaded: {self.model_name}")
+            print(f"✅ Tokenizer loaded with vocab size: {self.tokenizer.vocab_size}")
             
         except Exception as e:
-            logger.error(f"❌ Error loading model: {e}")
+            print(f"❌ Error loading model: {e}")
             raise
     
     def setup_lora(self):
         """Setup LoRA for efficient fine-tuning"""
-        logger.info("Setting up LoRA configuration...")
+        print("Setting up LoRA configuration...")
         
         try:
             self.model = FastLanguageModel.get_peft_model(
@@ -112,15 +109,15 @@ class InstructTrainer:
                 loftq_config=None
             )
             
-            logger.info(f"✅ LoRA configured with r={self.lora_r}, alpha={self.lora_alpha}")
+            print(f"✅ LoRA configured with r={self.lora_r}, alpha={self.lora_alpha}")
             
         except Exception as e:
-            logger.error(f"❌ Error setting up LoRA: {e}")
+            print(f"❌ Error setting up LoRA: {e}")
             raise
     
     def setup_chat_template(self):
         """Setup chat template for conversation formatting"""
-        logger.info("Setting up chat template...")
+        print("Setting up chat template...")
         
         try:
             self.tokenizer = get_chat_template(
@@ -128,15 +125,15 @@ class InstructTrainer:
                 chat_template=self.chat_template,
             )
             
-            logger.info(f"✅ Chat template configured: {self.chat_template}")
+            print(f"✅ Chat template configured: {self.chat_template}")
             
         except Exception as e:
-            logger.error(f"❌ Error setting up chat template: {e}")
+            print(f"❌ Error setting up chat template: {e}")
             raise
     
     def load_dataset(self, dataset_path: str) -> Dataset:
         """Load the conversation training dataset"""
-        logger.info(f"Loading conversation dataset from: {dataset_path}")
+        print(f"Loading conversation dataset from: {dataset_path}")
         
         try:
             if Path(dataset_path).exists():
@@ -144,10 +141,10 @@ class InstructTrainer:
                 if (Path(dataset_path) / "dataset_info.json").exists():
                     # Load from HuggingFace dataset directory
                     dataset = load_from_disk(dataset_path)
-                    logger.info(f"Loaded HuggingFace dataset from disk: {len(dataset)} samples")
+                    print(f"Loaded HuggingFace dataset from disk: {len(dataset)} samples")
                 else:
                     # Load from processed conversation data files (JSONL format)
-                    logger.info("Loading from processed conversation data files...")
+                    print("Loading from processed conversation data files...")
                     from datasets import Dataset
                     import json
                     
@@ -158,7 +155,7 @@ class InstructTrainer:
                     for split_file in ["train.jsonl", "validation.jsonl", "test.jsonl"]:
                         file_path = data_dir / split_file
                         if file_path.exists():
-                            logger.info(f"Loading {split_file}...")
+                            print(f"Loading {split_file}...")
                             with open(file_path, 'r', encoding='utf-8') as f:
                                 for line in f:
                                     if line.strip():
@@ -170,15 +167,15 @@ class InstructTrainer:
                     
                     # Create HuggingFace dataset
                     dataset = Dataset.from_list(all_data)
-                    logger.info(f"Created HuggingFace dataset from {len(all_data)} conversation samples")
+                    print(f"Created HuggingFace dataset from {len(all_data)} conversation samples")
             else:
                 # Try loading from HuggingFace Hub
-                logger.info(f"Attempting to load from HuggingFace Hub: {dataset_path}")
+                print(f"Attempting to load from HuggingFace Hub: {dataset_path}")
                 dataset = Dataset.load_dataset(dataset_path, split="train")
-                logger.info(f"Loaded from HuggingFace Hub: {len(dataset)} samples")
+                print(f"Loaded from HuggingFace Hub: {len(dataset)} samples")
             
-            logger.info(f"Dataset loaded: {len(dataset)} samples")
-            logger.info(f"Dataset features: {dataset.features}")
+            print(f"Dataset loaded: {len(dataset)} samples")
+            print(f"Dataset features: {dataset.features}")
             
             # Verify required fields exist for conversation data
             required_fields = ["conversation"]
@@ -189,16 +186,16 @@ class InstructTrainer:
             return dataset
             
         except Exception as e:
-            logger.error(f"Error loading conversation dataset: {e}")
+            print(f"Error loading conversation dataset: {e}")
             raise
     
     def format_dataset_for_training(self, dataset: Dataset) -> Dataset:
         """Format conversation dataset for training using standardize_sharegpt and apply_chat_template"""
-        logger.info("Formatting conversation dataset for training...")
+        print("Formatting conversation dataset for training...")
         
         try:
             # Standardize the ShareGPT format
-            logger.info("Standardizing ShareGPT format...")
+            print("Standardizing ShareGPT format...")
             dataset = standardize_sharegpt(dataset)
             
             # Define the formatting function for chat templates
@@ -214,21 +211,21 @@ class InstructTrainer:
                 return {"text": texts}
             
             # Apply the formatting function
-            logger.info("Applying chat template formatting...")
+            print("Applying chat template formatting...")
             dataset = dataset.map(formatting_prompts_func, batched=True)
             
-            logger.info(f"✅ Dataset formatted for training with {len(dataset)} samples")
-            logger.info(f"Sample formatted text: {dataset[0]['text'][:200]}...")
+            print(f"✅ Dataset formatted for training with {len(dataset)} samples")
+            print(f"Sample formatted text: {dataset[0]['text'][:200]}...")
             
             return dataset
             
         except Exception as e:
-            logger.error(f"❌ Error formatting dataset: {e}")
+            print(f"❌ Error formatting dataset: {e}")
             raise
     
     def setup_trainer(self, train_dataset: Dataset):
         """Setup the SFTTrainer for instruction fine-tuning"""
-        logger.info("Setting up SFTTrainer for instruction fine-tuning...")
+        print("Setting up SFTTrainer for instruction fine-tuning...")
         
         try:
             # SFT Configuration
@@ -247,12 +244,12 @@ class InstructTrainer:
                 report_to="none",  # Disable wandb for now
             )
             
-            logger.info("SFT Configuration:")
-            logger.info(f"  batch_size: {self.batch_size}")
-            logger.info(f"  gradient_accumulation_steps: {self.gradient_accumulation_steps}")
-            logger.info(f"  warmup_steps: {self.warmup_steps}")
-            logger.info(f"  max_steps: {self.max_steps}")
-            logger.info(f"  learning_rate: {self.learning_rate}")
+            print("SFT Configuration:")
+            print(f"  batch_size: {self.batch_size}")
+            print(f"  gradient_accumulation_steps: {self.gradient_accumulation_steps}")
+            print(f"  warmup_steps: {self.warmup_steps}")
+            print(f"  max_steps: {self.max_steps}")
+            print(f"  learning_rate: {self.learning_rate}")
             
             # Create SFTTrainer
             self.trainer = SFTTrainer(
@@ -266,18 +263,18 @@ class InstructTrainer:
                 args=sft_config,
             )
             
-            logger.info("✅ SFTTrainer configured successfully")
+            print("✅ SFTTrainer configured successfully")
             
         except Exception as e:
-            logger.error(f"❌ Error setting up trainer: {e}")
+            print(f"❌ Error setting up trainer: {e}")
             import traceback
-            logger.error("Full error traceback:")
+            print("Full error traceback:")
             traceback.print_exc()
             raise
     
     def setup_response_only_training(self):
         """Setup training to only learn from assistant responses"""
-        logger.info("Setting up response-only training...")
+        print("Setting up response-only training...")
         
         try:
             # Configure trainer to only train on responses
@@ -287,51 +284,51 @@ class InstructTrainer:
                 response_part="<|im_start|>assistant\n",
             )
             
-            logger.info("✅ Response-only training configured")
+            print("✅ Response-only training configured")
             
         except Exception as e:
-            logger.error(f"❌ Error setting up response-only training: {e}")
+            print(f"❌ Error setting up response-only training: {e}")
             raise
     
     def train(self, dataset_path: str):
         """Run the instruction fine-tuning process"""
-        logger.info("🚀 Starting instruction fine-tuning process...")
+        print("🚀 Starting instruction fine-tuning process...")
         
         try:
             # Load model and tokenizer
-            logger.info("Step 1: Loading model and tokenizer...")
+            print("Step 1: Loading model and tokenizer...")
             self.load_model_and_tokenizer()
             
             # Setup LoRA
-            logger.info("Step 2: Setting up LoRA...")
+            print("Step 2: Setting up LoRA...")
             self.setup_lora()
             
             # Setup chat template
-            logger.info("Step 3: Setting up chat template...")
+            print("Step 3: Setting up chat template...")
             self.setup_chat_template()
             
             # Load dataset
-            logger.info(f"Step 4: Loading conversation dataset from: {dataset_path}")
+            print(f"Step 4: Loading conversation dataset from: {dataset_path}")
             train_dataset = self.load_dataset(dataset_path)
             
             # Format dataset for training
-            logger.info("Step 5: Formatting dataset for training...")
+            print("Step 5: Formatting dataset for training...")
             formatted_dataset = self.format_dataset_for_training(train_dataset)
             
             # Setup trainer
-            logger.info("Step 6: Setting up trainer...")
+            print("Step 6: Setting up trainer...")
             self.setup_trainer(formatted_dataset)
             
             # Setup response-only training (optional but recommended for chat models)
-            logger.info("Step 7: Setting up response-only training...")
+            print("Step 7: Setting up response-only training...")
             self.setup_response_only_training()
             
             # Start training
-            logger.info("Step 8: Starting training...")
+            print("Step 8: Starting training...")
             trainer_stats = self.trainer.train()
             
-            logger.info("✅ Instruction fine-tuning completed successfully!")
-            logger.info(f"Training stats: {trainer_stats}")
+            print("✅ Instruction fine-tuning completed successfully!")
+            print(f"Training stats: {trainer_stats}")
             
             # Save the model
             self.save_model()
@@ -339,15 +336,15 @@ class InstructTrainer:
             return trainer_stats
             
         except Exception as e:
-            logger.error(f"❌ Instruction fine-tuning failed: {e}")
+            print(f"❌ Instruction fine-tuning failed: {e}")
             import traceback
-            logger.error("Full error traceback:")
+            print("Full error traceback:")
             traceback.print_exc()
             raise
     
     def save_model(self):
         """Save the trained instruction model"""
-        logger.info("Saving trained instruction model...")
+        print("Saving trained instruction model...")
         
         try:
             # Create output directory
@@ -362,23 +359,23 @@ class InstructTrainer:
             with open(config_path, 'w') as f:
                 json.dump(self.config, f, indent=2)
             
-            logger.info(f"✅ Instruction model saved to: {self.model_output_dir}")
-            logger.info(f"✅ You can now use this model for inference")
+            print(f"✅ Instruction model saved to: {self.model_output_dir}")
+            print(f"✅ You can now use this model for inference")
             
         except Exception as e:
-            logger.error(f"❌ Error saving model: {e}")
+            print(f"❌ Error saving model: {e}")
             raise
     
     def prepare_for_inference(self):
         """Prepare model for inference"""
-        logger.info("Preparing model for inference...")
+        print("Preparing model for inference...")
         
         try:
             FastLanguageModel.for_inference(self.model)
-            logger.info("✅ Model prepared for inference")
+            print("✅ Model prepared for inference")
             
         except Exception as e:
-            logger.error(f"❌ Error preparing for inference: {e}")
+            print(f"❌ Error preparing for inference: {e}")
             raise
 
 def load_training_config(yaml_path: str) -> Dict[str, Any]:
@@ -403,16 +400,16 @@ def load_training_config(yaml_path: str) -> Dict[str, Any]:
         # Training configuration - extract from training section
         if 'training' in config:
             training_data = config['training']
-            logger.info("Training data from YAML:")
-            logger.info(f"  num_epochs: {training_data.get('num_epochs')} (type: {type(training_data.get('num_epochs'))})")
-            logger.info(f"  batch_size: {training_data.get('batch_size')} (type: {type(training_data.get('batch_size'))})")
-            logger.info(f"  learning_rate: {training_data.get('learning_rate')} (type: {type(training_data.get('learning_rate'))})")
-            logger.info(f"  weight_decay: {training_data.get('weight_decay')} (type: {type(training_data.get('weight_decay'))})")
-            logger.info(f"  warmup_steps: {training_data.get('warmup_steps')} (type: {type(training_data.get('warmup_steps'))})")
-            logger.info(f"  max_steps: {training_data.get('max_steps')} (type: {type(training_data.get('max_steps'))})")
-            logger.info(f"  gradient_accumulation_steps: {training_data.get('gradient_accumulation_steps')} (type: {type(training_data.get('gradient_accumulation_steps'))})")
-            logger.info(f"  seed: {training_data.get('seed')} (type: {type(training_data.get('seed'))})")
-            logger.info(f"  model_output_dir: {training_data.get('model_output_dir')} (type: {type(training_data.get('model_output_dir'))})")
+            print("Training data from YAML:")
+            print(f"  num_epochs: {training_data.get('num_epochs')} (type: {type(training_data.get('num_epochs'))})")
+            print(f"  batch_size: {training_data.get('batch_size')} (type: {type(training_data.get('batch_size'))})")
+            print(f"  learning_rate: {training_data.get('learning_rate')} (type: {type(training_data.get('learning_rate'))})")
+            print(f"  weight_decay: {training_data.get('weight_decay')} (type: {type(training_data.get('weight_decay'))})")
+            print(f"  warmup_steps: {training_data.get('warmup_steps')} (type: {type(training_data.get('warmup_steps'))})")
+            print(f"  max_steps: {training_data.get('max_steps')} (type: {type(training_data.get('max_steps'))})")
+            print(f"  gradient_accumulation_steps: {training_data.get('gradient_accumulation_steps')} (type: {type(training_data.get('gradient_accumulation_steps'))})")
+            print(f"  seed: {training_data.get('seed')} (type: {type(training_data.get('seed'))})")
+            print(f"  model_output_dir: {training_data.get('model_output_dir')} (type: {type(training_data.get('model_output_dir'))})")
             
             training_config.update({
                 'num_epochs': int(training_data.get('num_epochs', 1)),
@@ -450,14 +447,14 @@ def load_training_config(yaml_path: str) -> Dict[str, Any]:
             'chat_template': 'qwen-2.5'  # Use Qwen chat template by default
         })
         
-        logger.info("Final training_config:")
+        print("Final training_config:")
         for key, value in training_config.items():
-            logger.info(f"  {key}: {value} (type: {type(value)})")
+            print(f"  {key}: {value} (type: {type(value)})")
         
         return training_config
         
     except Exception as e:
-        logger.error(f"Error loading training config: {e}")
+        print(f"Error loading training config: {e}")
         raise
 
 def main():
@@ -475,15 +472,11 @@ def main():
     
     args = parser.parse_args()
     
-    # Setup logging
-    logging.basicConfig(
-        level=logging.INFO,
-        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-    )
+    # Setup logging replaced with print statements
     
     try:
         # Load configuration
-        logger.info(f"Loading configuration from: {args.config}")
+        print(f"Loading configuration from: {args.config}")
         training_config = load_training_config(args.config)
         
         # Override with CLI arguments
@@ -501,13 +494,13 @@ def main():
         # Determine dataset path: CLI argument takes precedence, then YAML config
         dataset_path = args.dataset or training_config.get('dataset_path')
         if not dataset_path:
-            logger.error("No dataset path provided. Use --dataset or ensure output_dir is set in YAML config.")
+            print("No dataset path provided. Use --dataset or ensure output_dir is set in YAML config.")
             sys.exit(1)
         
-        logger.info("Training configuration:")
+        print("Training configuration:")
         for key, value in training_config.items():
-            logger.info(f"  {key}: {value}")
-        logger.info(f"  Dataset path: {dataset_path}")
+            print(f"  {key}: {value}")
+        print(f"  Dataset path: {dataset_path}")
         
         # Initialize trainer
         trainer = InstructTrainer(training_config)
@@ -515,10 +508,10 @@ def main():
         # Start training
         trainer.train(dataset_path)
         
-        logger.info("Instruction fine-tuning completed successfully!")
+        print("Instruction fine-tuning completed successfully!")
         
     except Exception as e:
-        logger.error(f"Instruction fine-tuning failed: {e}")
+        print(f"Instruction fine-tuning failed: {e}")
         sys.exit(1)
 
 if __name__ == "__main__":

requirements.txt

@@ -9,3 +9,4 @@ numpy>=1.24.0
 scikit-learn>=1.3.0
 pyyaml>=6.0
 huggingface-hub>=0.15.0
+unsloth

setup.sh

@@ -0,0 +1,46 @@
+#!/usr/bin/env bash
+set -e  # exit on error
+
+# Step 1: Download Miniconda installer
+echo ">>> Downloading Miniconda..."
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
+
+# Step 2: Run the installer
+echo ">>> Installing Miniconda..."
+bash ~/miniconda.sh -b -p $HOME/miniconda
+
+# Step 3: Initialize conda for bash
+echo ">>> Initializing Conda..."
+eval "$($HOME/miniconda/bin/conda shell.bash hook)"
+
+# Step 4: Add conda to PATH permanently
+if ! grep -q "miniconda/bin" ~/.bashrc; then
+    echo "export PATH=\$HOME/miniconda/bin:\$PATH" >> ~/.bashrc
+fi
+source ~/.bashrc
+
+# Step 5: Verify installation
+echo ">>> Conda version:"
+conda --version
+
+# Step 6: Create environment
+echo ">>> Creating environment: llm-env (python=3.10)..."
+conda create -n llm-env python=3.10 -y
+
+# Step 7: Activate environment
+echo ">>> Activating environment..."
+conda activate llm-env
+
+# Step 8: Install dependencies
+echo ">>> Installing scikit-learn..."
+conda install scikit-learn -y
+
+if [ -f requirements.txt ]; then
+    echo ">>> Installing Python requirements..."
+    pip install -r requirements.txt
+else
+    echo ">>> No requirements.txt found, skipping pip install."
+fi
+
+echo ">>> Setup complete. To activate your environment run:"
+echo "conda activate llm-env"