File size: 5,754 Bytes
fde73f3 | 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 | """Train a base model on the unified Mel corpus with LoRA.
Designed for cloud GPU deployment. Loads base model in fp16/bf16, applies
LoRA adapters, trains on the prepared JSONL data.
Usage:
python train.py --model EleutherAI/pythia-1.4b --data train.jsonl --output mel-pythia-1.4b
For 4-bit quantization (fits on smaller GPUs):
python train.py --model EleutherAI/pythia-2.8b --data train.jsonl --output mel-pythia-2.8b --use-4bit
"""
import argparse
import json
import os
import torch
from datasets import Dataset
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
TrainingArguments,
Trainer,
DataCollatorForLanguageModeling,
BitsAndBytesConfig,
)
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, TaskType
def load_jsonl(path):
"""Load JSONL into a HF Dataset."""
examples = []
with open(path) as f:
for line in f:
examples.append(json.loads(line))
return Dataset.from_list(examples)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='EleutherAI/pythia-1.4b',
help='Base model. Use uncontaminated base models, not -Instruct/-Chat variants.')
parser.add_argument('--data', default='train.jsonl')
parser.add_argument('--output', default='mel-pythia-1.4b')
parser.add_argument('--epochs', type=int, default=3)
parser.add_argument('--batch-size', type=int, default=1)
parser.add_argument('--gradient-accumulation', type=int, default=8)
parser.add_argument('--learning-rate', type=float, default=2e-4)
parser.add_argument('--lora-rank', type=int, default=16)
parser.add_argument('--lora-alpha', type=int, default=32)
parser.add_argument('--use-4bit', action='store_true', help='4-bit quantization for memory efficiency')
parser.add_argument('--use-8bit', action='store_true')
parser.add_argument('--max-length', type=int, default=2048)
parser.add_argument('--hf-repo', default=None, help='HuggingFace repo to push trained adapter to')
args = parser.parse_args()
print(f"=== Training {args.model} on {args.data} ===")
print(f"Output: {args.output}")
print(f"Epochs: {args.epochs}, batch: {args.batch_size}, accum: {args.gradient_accumulation}")
print(f"LoRA rank: {args.lora_rank}, alpha: {args.lora_alpha}")
# Quantization config
bnb_config = None
if args.use_4bit:
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
)
elif args.use_8bit:
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(args.model)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Load model
print(f"Loading model...")
model = AutoModelForCausalLM.from_pretrained(
args.model,
quantization_config=bnb_config,
torch_dtype=torch.bfloat16 if not bnb_config else None,
device_map='auto',
)
if bnb_config:
model = prepare_model_for_kbit_training(model)
# Apply LoRA
# Target modules vary by model architecture
target_modules = {
'pythia': ['query_key_value', 'dense', 'dense_h_to_4h', 'dense_4h_to_h'],
'llama': ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'],
'qwen': ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'],
'phi': ['q_proj', 'k_proj', 'v_proj', 'dense', 'fc1', 'fc2'],
}
model_family = 'pythia'
for key in target_modules:
if key in args.model.lower():
model_family = key
break
lora_config = LoraConfig(
r=args.lora_rank,
lora_alpha=args.lora_alpha,
target_modules=target_modules[model_family],
lora_dropout=0.05,
bias='none',
task_type=TaskType.CAUSAL_LM,
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# Load and tokenize data
print(f"Loading data: {args.data}")
dataset = load_jsonl(args.data)
print(f"Examples: {len(dataset)}")
def tokenize_fn(examples):
return tokenizer(
examples['text'],
truncation=True,
max_length=args.max_length,
padding=False,
)
dataset = dataset.map(tokenize_fn, batched=True, remove_columns=dataset.column_names)
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
# Training args
training_args = TrainingArguments(
output_dir=args.output,
num_train_epochs=args.epochs,
per_device_train_batch_size=args.batch_size,
gradient_accumulation_steps=args.gradient_accumulation,
learning_rate=args.learning_rate,
warmup_steps=100,
logging_steps=10,
save_steps=500,
save_total_limit=3,
bf16=True,
gradient_checkpointing=True,
optim='paged_adamw_8bit' if bnb_config else 'adamw_torch',
report_to='none',
push_to_hub=args.hf_repo is not None,
hub_model_id=args.hf_repo,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
data_collator=data_collator,
)
print("Starting training...")
trainer.train()
print("Saving final model...")
trainer.save_model(args.output)
if args.hf_repo:
trainer.push_to_hub()
print(f"Done. Saved to {args.output}")
if __name__ == '__main__':
main()
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