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# Promt Format
```
alpaca_prompt = """Sen bir doktorsun. Sorular谋 buna g枚re cevapla.
### <|reserved_special_token_0|>:
{}
### <|reserved_special_token_1|>:
{}"""
```
# Training args
```
batch_size = 128
gradient_accumulation_steps = 32
num_train_epochs = 2
per_device_batch_size = int(batch_size / gradient_accumulation_steps)
training_args = TrainingArguments(
per_device_train_batch_size = per_device_batch_size,
per_device_eval_batch_size = per_device_batch_size,
gradient_accumulation_steps = gradient_accumulation_steps,
save_total_limit = 1,
warmup_steps = int(2000 / batch_size),
num_train_epochs = num_train_epochs,
learning_rate = 1e-4,
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = output_dir,
save_strategy = "steps",
eval_strategy = "steps",
logging_strategy = "steps",
save_steps = int(5000 / batch_size * num_train_epochs),
eval_steps = int(28900 / batch_size * num_train_epochs),
logging_steps = int(28900 / batch_size * num_train_epochs),
)
```
# Trainer args
```
max_seq_length = 8192
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
train_dataset = train_dataset,
eval_dataset = eval_dataset,
dataset_text_field = "text",
max_seq_length = max_seq_length,
dataset_num_proc = 1,
packing = False, # Can make training 5x faster for short sequences.
args = training_args
)
```
# From pretrained args
```
from unsloth import FastLanguageModel
dtype = None
load_in_4bit = False
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = output_dir,
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
```
# Peft model args
```
model = FastLanguageModel.get_peft_model(
model,
r = 8,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 16,
lora_dropout = 0,
bias = "none",
use_gradient_checkpointing = True
random_state = 3407,
use_rslora = False,
loftq_config = None,
)
``` |