instruct model setup
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@@ -20,7 +20,7 @@ from utils.config.config_manager import ConfigManager
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# Training imports
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import torch
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from datasets import load_from_disk, Dataset
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from unsloth import FastLanguageModel #is_bfloat16_supported
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from unsloth import FastLanguageModel, is_bfloat16_supported
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from unsloth.chat_templates import get_chat_template, standardize_sharegpt, train_on_responses_only
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from trl import SFTTrainer, SFTConfig
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from transformers import DataCollatorForSeq2Seq
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@@ -132,56 +132,23 @@ class InstructTrainer:
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raise
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def load_dataset(self, dataset_path: str) -> Dataset:
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"""Load the conversation training dataset"""
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"""Load the conversation training dataset directly from JSONL file"""
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print(f"Loading conversation dataset from: {dataset_path}")
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try:
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if Path(dataset_path).exists():
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# Check if it's a HuggingFace dataset directory
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if (Path(dataset_path) / "dataset_info.json").exists():
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# Load from HuggingFace dataset directory
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dataset = load_from_disk(dataset_path)
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print(f"Loaded HuggingFace dataset from disk: {len(dataset)} samples")
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else:
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# Load from processed conversation data files (JSONL format)
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print("Loading from processed conversation data files...")
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from datasets import Dataset
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import json
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all_data = []
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data_dir = Path(dataset_path)
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# Look for train.jsonl, validation.jsonl, test.jsonl
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for split_file in ["train.jsonl", "validation.jsonl", "test.jsonl"]:
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file_path = data_dir / split_file
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if file_path.exists():
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print(f"Loading {split_file}...")
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with open(file_path, 'r', encoding='utf-8') as f:
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for line in f:
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if line.strip():
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data = json.loads(line)
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all_data.append(data)
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if not all_data:
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raise ValueError(f"No conversation data found in {dataset_path}")
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# Create HuggingFace dataset
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dataset = Dataset.from_list(all_data)
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print(f"Created HuggingFace dataset from {len(all_data)} conversation samples")
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else:
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# Try loading from HuggingFace Hub
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print(f"Attempting to load from HuggingFace Hub: {dataset_path}")
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dataset = Dataset.load_dataset(dataset_path, split="train")
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print(f"Loaded from HuggingFace Hub: {len(dataset)} samples")
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print(f"Dataset loaded: {len(dataset)} samples")
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print(f"Dataset features: {dataset.features}")
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# Verify required fields exist for conversation data
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required_fields = ["conversation"]
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missing_fields = [field for field in required_fields if field not in dataset.features]
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if missing_fields:
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raise ValueError(f"Missing required fields in conversation dataset: {missing_fields}")
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# Load JSONL data exactly as provided
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data = []
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with open(dataset_path, "r", encoding="utf-8") as f:
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for line in f:
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data.append(json.loads(line))
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print(f"Loaded {len(data)} examples")
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# Convert to HuggingFace Dataset
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dataset = Dataset.from_list(data)
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print(dataset)
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print(dataset[0]) # Show first example
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return dataset
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@@ -194,22 +161,16 @@ class InstructTrainer:
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print("Formatting conversation dataset for training...")
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try:
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# Define the formatting function exactly as provided
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def formatting_prompts_func(examples):
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convos = examples["conversation"]
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texts = [self.tokenizer.apply_chat_template(convo, tokenize=False, add_generation_prompt=False) for convo in convos]
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return {"text": texts}
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# Standardize the ShareGPT format
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print("Standardizing ShareGPT format...")
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dataset = standardize_sharegpt(dataset)
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# Define the formatting function for chat templates
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def formatting_prompts_func(examples):
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convos = examples["conversation"]
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texts = [
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self.tokenizer.apply_chat_template(
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convo,
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tokenize=False,
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add_generation_prompt=False
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) for convo in convos
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]
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return {"text": texts}
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# Apply the formatting function
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print("Applying chat template formatting...")
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dataset = dataset.map(formatting_prompts_func, batched=True)
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@@ -277,18 +238,29 @@ class InstructTrainer:
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print("Setting up response-only training...")
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try:
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# For Qwen models, we need to use the correct chat template tokens
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# Qwen uses different tokens than Llama
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if "qwen" in self.model_name.lower():
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instruction_part = "<|im_start|>user\n"
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response_part = "<|im_start|>assistant\n"
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else:
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# Default for other models
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instruction_part = "<|start_header_id|>user<|end_header_id|>\n\n"
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response_part = "<|start_header_id|>assistant<|end_header_id|>\n\n"
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# Configure trainer to only train on responses
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self.trainer = train_on_responses_only(
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self.trainer,
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instruction_part="<|im_start|>user\n",
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response_part="<|im_start|>assistant\n",
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instruction_part=instruction_part,
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response_part=response_part,
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)
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print("✅ Response-only training configured")
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except Exception as e:
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print(f"❌ Error setting up response-only training: {e}")
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raise
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print("Skipping response-only training and proceeding with full training...")
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# Don't raise the exception, just continue with regular training
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def train(self, dataset_path: str):
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"""Run the instruction fine-tuning process"""
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@@ -321,7 +293,11 @@ class InstructTrainer:
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# Setup response-only training (optional but recommended for chat models)
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print("Step 7: Setting up response-only training...")
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self.setup_response_only_training()
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try:
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self.setup_response_only_training()
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except Exception as e:
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print(f"⚠️ Response-only training failed: {e}")
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print("Continuing with full training (will train on all tokens)...")
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# Start training
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print("Step 8: Starting training...")
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@@ -432,13 +408,12 @@ def load_training_config(yaml_path: str) -> Dict[str, Any]:
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])
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})
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# Data configuration - use output_dir from data section
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# Data configuration - use data_path from data section
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if 'data' in config:
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data_config = config['data']
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output_dir = data_config.get('output_dir', './data/processed/instruct')
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data_path = data_config.get('data_path', './data/raw/instruct/code_reasoning.jsonl')
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training_config.update({
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'data_output_dir': output_dir,
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'dataset_path': output_dir, # Default dataset path is the output_dir
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'dataset_path': data_path, # Use data_path directly for JSONL file
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})
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# Output configuration
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+43
-68
@@ -20,7 +20,7 @@ from utils.config.config_manager import ConfigManager
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# Training imports
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import torch
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from datasets import load_from_disk, Dataset
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from unsloth import FastLanguageModel #is_bfloat16_supported
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from unsloth import FastLanguageModel, is_bfloat16_supported
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from unsloth.chat_templates import get_chat_template, standardize_sharegpt, train_on_responses_only
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from trl import SFTTrainer, SFTConfig
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from transformers import DataCollatorForSeq2Seq
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@@ -132,56 +132,23 @@ class InstructTrainer:
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raise
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def load_dataset(self, dataset_path: str) -> Dataset:
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"""Load the conversation training dataset"""
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"""Load the conversation training dataset directly from JSONL file"""
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print(f"Loading conversation dataset from: {dataset_path}")
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try:
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if Path(dataset_path).exists():
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# Check if it's a HuggingFace dataset directory
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if (Path(dataset_path) / "dataset_info.json").exists():
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# Load from HuggingFace dataset directory
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dataset = load_from_disk(dataset_path)
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print(f"Loaded HuggingFace dataset from disk: {len(dataset)} samples")
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else:
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# Load from processed conversation data files (JSONL format)
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print("Loading from processed conversation data files...")
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from datasets import Dataset
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import json
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all_data = []
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data_dir = Path(dataset_path)
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# Look for train.jsonl, validation.jsonl, test.jsonl
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for split_file in ["train.jsonl", "validation.jsonl", "test.jsonl"]:
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file_path = data_dir / split_file
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if file_path.exists():
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print(f"Loading {split_file}...")
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with open(file_path, 'r', encoding='utf-8') as f:
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for line in f:
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if line.strip():
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data = json.loads(line)
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all_data.append(data)
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if not all_data:
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raise ValueError(f"No conversation data found in {dataset_path}")
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# Create HuggingFace dataset
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dataset = Dataset.from_list(all_data)
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print(f"Created HuggingFace dataset from {len(all_data)} conversation samples")
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else:
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# Try loading from HuggingFace Hub
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print(f"Attempting to load from HuggingFace Hub: {dataset_path}")
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dataset = Dataset.load_dataset(dataset_path, split="train")
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print(f"Loaded from HuggingFace Hub: {len(dataset)} samples")
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print(f"Dataset loaded: {len(dataset)} samples")
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print(f"Dataset features: {dataset.features}")
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# Verify required fields exist for conversation data
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required_fields = ["conversation"]
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missing_fields = [field for field in required_fields if field not in dataset.features]
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if missing_fields:
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raise ValueError(f"Missing required fields in conversation dataset: {missing_fields}")
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# Load JSONL data exactly as provided
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data = []
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with open(dataset_path, "r", encoding="utf-8") as f:
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for line in f:
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data.append(json.loads(line))
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print(f"Loaded {len(data)} examples")
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# Convert to HuggingFace Dataset
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dataset = Dataset.from_list(data)
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print(dataset)
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print(dataset[0]) # Show first example
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return dataset
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@@ -194,22 +161,16 @@ class InstructTrainer:
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print("Formatting conversation dataset for training...")
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try:
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# Define the formatting function exactly as provided
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def formatting_prompts_func(examples):
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convos = examples["conversation"]
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texts = [self.tokenizer.apply_chat_template(convo, tokenize=False, add_generation_prompt=False) for convo in convos]
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return {"text": texts}
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# Standardize the ShareGPT format
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print("Standardizing ShareGPT format...")
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dataset = standardize_sharegpt(dataset)
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# Define the formatting function for chat templates
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def formatting_prompts_func(examples):
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convos = examples["conversation"]
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texts = [
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self.tokenizer.apply_chat_template(
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convo,
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tokenize=False,
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add_generation_prompt=False
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) for convo in convos
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]
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return {"text": texts}
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# Apply the formatting function
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print("Applying chat template formatting...")
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dataset = dataset.map(formatting_prompts_func, batched=True)
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@@ -277,18 +238,29 @@ class InstructTrainer:
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print("Setting up response-only training...")
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try:
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# For Qwen models, we need to use the correct chat template tokens
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# Qwen uses different tokens than Llama
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if "qwen" in self.model_name.lower():
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instruction_part = "<|im_start|>user\n"
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response_part = "<|im_start|>assistant\n"
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else:
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# Default for other models
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instruction_part = "<|start_header_id|>user<|end_header_id|>\n\n"
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response_part = "<|start_header_id|>assistant<|end_header_id|>\n\n"
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# Configure trainer to only train on responses
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self.trainer = train_on_responses_only(
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self.trainer,
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instruction_part="<|im_start|>user\n",
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response_part="<|im_start|>assistant\n",
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instruction_part=instruction_part,
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response_part=response_part,
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)
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print("✅ Response-only training configured")
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except Exception as e:
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print(f"❌ Error setting up response-only training: {e}")
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raise
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print("Skipping response-only training and proceeding with full training...")
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# Don't raise the exception, just continue with regular training
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def train(self, dataset_path: str):
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"""Run the instruction fine-tuning process"""
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@@ -321,7 +293,11 @@ class InstructTrainer:
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# Setup response-only training (optional but recommended for chat models)
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print("Step 7: Setting up response-only training...")
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self.setup_response_only_training()
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try:
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self.setup_response_only_training()
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except Exception as e:
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print(f"⚠️ Response-only training failed: {e}")
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print("Continuing with full training (will train on all tokens)...")
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# Start training
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print("Step 8: Starting training...")
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@@ -432,13 +408,12 @@ def load_training_config(yaml_path: str) -> Dict[str, Any]:
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])
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})
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# Data configuration - use output_dir from data section
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# Data configuration - use data_path from data section
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if 'data' in config:
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data_config = config['data']
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output_dir = data_config.get('output_dir', './data/processed/instruct')
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data_path = data_config.get('data_path', './data/raw/instruct/code_reasoning.jsonl')
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training_config.update({
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'data_output_dir': output_dir,
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'dataset_path': output_dir, # Default dataset path is the output_dir
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'dataset_path': data_path, # Use data_path directly for JSONL file
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})
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# Output configuration
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