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fbf3c28 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | #!/usr/bin/env python3
"""
Final training script for Elizabeth - proper tokenization and formatting
"""
import os
os.environ['HF_HOME'] = '/home/x/.cache/huggingface'
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TrainingArguments,
Trainer,
DataCollatorForLanguageModeling
)
from datasets import Dataset
import json
# Configuration
MODEL_NAME = "Qwen/Qwen3-8B"
TRAIN_DATA_PATH = "/home/x/adaptai/aiml/e-train-1/elizabeth_tooluse_minipack_v1.jsonl"
OUTPUT_DIR = "/home/x/adaptai/experiments/qwen3-8b-elizabeth-final"
# YaRN Configuration
YARN_CONFIG = {
"rope_scaling": {
"type": "yarn",
"factor": 8.0,
"original_max_position_embeddings": 16384,
"extrapolation_factor": 1.0,
"attn_factor": 1.0,
"beta_fast": 32.0,
"beta_slow": 1.0
}
}
class FinalTrainer:
def __init__(self):
self.model = None
self.tokenizer = None
def setup_model(self):
"""Load model with YaRN configuration"""
print("🚀 Loading Qwen3-8B with YaRN configuration...")
# Load tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(
MODEL_NAME,
trust_remote_code=True
)
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
# Load model with YaRN
self.model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
**YARN_CONFIG
)
print(f"✅ Model loaded with YaRN configuration")
print(f"📏 Context length: {self.model.config.max_position_embeddings}")
def tokenize_function(self, examples):
"""Tokenize the text examples"""
# Convert messages to text format
texts = []
for messages in examples['messages']:
text = ""
for msg in messages:
if msg['role'] == 'system':
text += f"<|im_start|>system\n{msg['content']}<|im_end|>\n"
elif msg['role'] == 'user':
text += f"<|im_start|>user\n{msg['content']}<|im_end|>\n"
elif msg['role'] == 'assistant':
text += f"<|im_start|>assistant\n{msg['content']}<|im_end|>\n"
elif msg['role'] == 'tool':
text += f"<|im_start|>tool\n{json.dumps(msg['content'])}<|im_end|>\n"
texts.append(text)
# Tokenize
tokenized = self.tokenizer(
texts,
truncation=True,
padding=False,
max_length=4096,
return_tensors=None
)
# Add labels for language modeling
tokenized["labels"] = tokenized["input_ids"].copy()
return tokenized
def load_dataset(self):
"""Load and tokenize dataset"""
print("📊 Loading and tokenizing data...")
# Load raw data
data = []
with open(TRAIN_DATA_PATH, 'r') as f:
for line in f:
data.append(json.loads(line))
# Create dataset
dataset = Dataset.from_list(data)
# Tokenize
tokenized_dataset = dataset.map(
self.tokenize_function,
batched=True,
batch_size=10,
remove_columns=dataset.column_names
)
print(f"✅ Tokenized {len(tokenized_dataset)} examples")
return tokenized_dataset
def train(self):
"""Start training"""
self.setup_model()
dataset = self.load_dataset()
# Training arguments
training_args = TrainingArguments(
output_dir=OUTPUT_DIR,
num_train_epochs=1,
per_device_train_batch_size=1,
gradient_accumulation_steps=16,
learning_rate=2e-5,
warmup_ratio=0.03,
lr_scheduler_type="cosine",
logging_steps=10,
save_steps=100,
bf16=True,
gradient_checkpointing=True,
remove_unused_columns=False,
report_to=[],
)
# Data collator
data_collator = DataCollatorForLanguageModeling(
tokenizer=self.tokenizer,
mlm=False,
)
# Trainer
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=dataset,
data_collator=data_collator,
)
print("🎯 Starting training...")
# Start training
trainer.train()
# Save final model
trainer.save_model()
print(f"✅ Training completed!")
print(f"💾 Model saved to: {OUTPUT_DIR}")
if __name__ == "__main__":
trainer = FinalTrainer()
trainer.train() |