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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
from peft import LoraConfig, get_peft_model
from datasets import load_dataset
import gradio as gr
import spaces
#----------------------------
import time
import gradio as gr
#--------------------
@spaces.GPU
def ana():
# === 1️⃣ MODEL VE TOKENIZER YÜKLEME ===
MODEL_NAME = "mistralai/Mistral-7B-v0.1" # Hugging Face model adı
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
# === 2️⃣ CPU OPTİMİZASYONU ===
torch_dtype = torch.float32 # CPU için uygun dtype
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch_dtype)
# === 3️⃣ LoRA AYARLARI ===
lora_config = LoraConfig(
r=8,
lora_alpha=32,
lora_dropout=0.1,
bias="none",
target_modules=["q_proj", "v_proj"],
)
model = get_peft_model(model, lora_config)
# === 4️⃣ VERİ SETİ ===
dataset = load_dataset("oscar", "unshuffled_deduplicated_tr", trust_remote_code=True) # trust_remote_code=True
subset = dataset["train"].shuffle(seed=42).select(range(10000)) # Küçük subset seçiyoruz (10.000 örnek)
# === 5️⃣ TOKENLEŞTİRME FONKSİYONU ===
@spaces.GPU
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True, max_length=512)
tokenized_datasets = subset.map(tokenize_function, batched=True)
# === 6️⃣ EĞİTİM AYARLARI ===
# Eğitimde kaç adım olduğunu hesaplayalım
train_size = len(tokenized_datasets) # 10,000 örnek
batch_size = 1 # Batch size 1
num_epochs = 1 # 1 epoch eğitimi
max_steps = (train_size // batch_size) * num_epochs # max_steps hesapla
training_args = TrainingArguments(
output_dir="./mistral_lora",
per_device_train_batch_size=1,
gradient_accumulation_steps=16,
learning_rate=5e-4,
num_train_epochs=1,
max_steps=max_steps, # Buraya max_steps parametresini ekliyoruz
save_steps=500,
save_total_limit=2,
logging_dir="./logs",
logging_steps=10,
optim="adamw_torch",
train_dataset=split_dataset["train"],
eval_dataset=split_dataset["test"],
no_cuda=True, # GPU kullanılmıyor
)
train_model()
# === 7️⃣ MODEL EĞİTİMİ ===
@spaces.GPU
def train_model():
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets,
)
trainer.train()
# Eğitimi başlat
demo = gr.ChatInterface(
slow_echo,
type="messages",
flagging_mode="manual",
flagging_options=["Like", "Spam", "Inappropriate", "Other"],
save_history=True,
)
@spaces.GPU
def slow_echo(message, history):
for i in range(len(message)):
ana()
time.sleep(0.05)
yield "You typed: " + message[: i + 1]
demo = gr.ChatInterface(
slow_echo,
type="messages",
flagging_mode="manual",
flagging_options=["Like", "Spam", "Inappropriate", "Other"],
save_history=True,
)
if __name__ == "__main__":
demo.launch(share=True)
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