Spaces:
Sleeping
Sleeping
File size: 2,839 Bytes
df4fd98 a30c450 df4fd98 a30c450 df4fd98 a30c450 df4fd98 |
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 |
from unsloth import FastLanguageModel
import torch
from trl import SFTTrainer
from transformers import TrainingArguments
from unsloth import is_bfloat16_supported
def load_model(model_name, max_seq_length):
dtype = None
load_in_4bit = True
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = model_name,
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = ""
)
return model, tokenizer
def get_peft(model, peft, max_seq_length, random_seed):
model = FastLanguageModel.get_peft_model(
model,
r = peft['r',]
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = peft['alpha'],
lora_dropout = peft['dropout'],
bias = peft['bias'],
use_gradient_checkpointing = "unsloth",
random_state = random_seed,
use_rslora = peft['rslora'], # We support rank stabilized LoRA
loftq_config = peft['loftq_config'], # And LoftQ
)
return model
def get_trainer(model, tokenizer, dataset, sft,
data_field, max_seq_length, random_seed,
num_epochs, max_steps):
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
train_dataset = dataset,
dataset_text_field = data_field,
max_seq_length = max_seq_length,
dataset_num_proc = 2,
packing = False,
args = TrainingArguments(
per_device_train_batch_size = sft['per_device_train_batch_size'],
gradient_accumulation_steps = sft['gradient_accumulation_steps'],
warmup_steps = sft['warmup_steps'],
num_train_epochs = num_epochs,
max_steps = max_steps,
learning_rate = sft['learning_rate'],
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
logging_steps = sft['logging_steps'],
optim = sft['optim'],
weight_decay = sft['weight_decay'],
lr_scheduler_type = sft['lr_scheduler_type'],
seed = random_seed,
output_dir = "outputs",
),
)
return trainer
def prepare_trainer(model_name, max_seq_length, random_seed,
num_epochs, max_steps,
peft, sft, dataset, data_field):
print("Loading Model")
model, tokenizer = load_model(model_name, max_seq_length)
print("Preparing for PEFT")
model = get_peft(model, peft, max_seq_length, random_seed)
print("Getting Trainer Model")
trainer = get_trainer(model, tokenizer, dataset, data_field, max_seq_length, random_seed,
num_epochs, max_steps)
return trainer
if __name__ == "__main__":
trainer = prepare_trainer()
|