File size: 4,236 Bytes
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 | #!/usr/bin/env python3
"""
Simple training script for Elizabeth - focuses on core identity without complex 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-simple"
# 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 SimpleTrainer:
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 load_simple_dataset(self):
"""Load only the simple text examples"""
print("📊 Loading simple training data...")
texts = []
# Load only examples with simple text (no tool calls)
with open(TRAIN_DATA_PATH, 'r') as f:
for line in f:
data = json.loads(line)
# Skip complex tool call examples for now
has_tool_call = any('tool_call' in str(msg) for msg in data.get('messages', []))
if not has_tool_call:
# Convert to simple text
conversation = ""
for msg in data.get('messages', []):
if 'content' in msg:
conversation += f"{msg['role']}: {msg['content']}\n"
texts.append(conversation)
print(f"✅ Loaded {len(texts)} simple examples")
# Create dataset
dataset = Dataset.from_dict({"text": texts})
return dataset
def train(self):
"""Start simple training"""
self.setup_model()
dataset = self.load_simple_dataset()
# Simple 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,
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,
tokenizer=self.tokenizer,
)
print("🎯 Starting simple training...")
# Start training
trainer.train()
# Save final model
trainer.save_model()
print(f"✅ Simple training completed!")
if __name__ == "__main__":
trainer = SimpleTrainer()
trainer.train() |