diff --git a/hft.py b/hft.py
index d1e432a..266ccf3 100644
--- a/hft.py
+++ b/hft.py
@@ -1,8 +1,7 @@
 import os
 import torch
 import torch.nn as nn
-#from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
-from transformers import GPTNeoForCausalLM, Trainer, TrainingArguments, AutoTokenizer, AutoModelForCausalLM # Zmiana importu
+from transformers import GPTNeoForCausalLM, Trainer, TrainingArguments, AutoTokenizer, AutoModelForCausalLM
 from datasets import Dataset
 from PIL import Image
 import re
@@ -14,13 +13,12 @@ from torch.cuda.amp import autocast
 from collections import defaultdict
 from huggingface_hub import login
 
-import torch
 torch.cuda.empty_cache()
 
+# Logowanie do Hugging Face Hub
 login(token="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")
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
-# Nowa klasa do zarządzania źródłami
 class SourceMapper:
     def __init__(self):
         self.source_to_idx = defaultdict(lambda: len(self.source_to_idx))
@@ -78,7 +76,7 @@ def prepare_dataset(directory, catalog_path, source_mapper):
                 
             doc_type = identify_legal_document(file, file_catalog)
             if doc_type != "Opracowanie własne":
-                articles = re.split(r'(Art\.\s+\d+[\.\s])', text)
+                articles = re.split(r'(Art\.?\s+\d+[\.\s])', text)
                 for i in range(1, len(articles), 2):
                     article_number = articles[i].strip()
                     article_content = articles[i+1].strip() if i+1 < len(articles) else ""
@@ -94,7 +92,7 @@ def prepare_dataset(directory, catalog_path, source_mapper):
                 for chunk in chunks:
                     data.append({
                         "text": chunk,
-                        "source_idx": -1  # Brak źródła
+                        "source_idx": -1
                     })
     return data
 
@@ -114,13 +112,10 @@ def custom_collate_fn(batch):
     input_ids = torch.stack([torch.tensor(b["input_ids"]) for b in batch])
     attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch])
     labels = torch.stack([torch.tensor(b["labels"]) for b in batch])
-    
-    # Dodajemy domyślne source_idx, jeśli nie istnieje
     source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long)
-
     return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels, "source_idx": source_idx}
 
-class CustomModel(GPTNeoForCausalLM):  # Zmiana klasy bazowej
+class CustomModel(GPTNeoForCausalLM):
     def __init__(self, config):
         super().__init__(config)
         self.source_embedding = nn.Embedding(
@@ -130,48 +125,42 @@ class CustomModel(GPTNeoForCausalLM):  # Zmiana klasy bazowej
         )
         
     def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None, **kwargs):
-        outputs = super().forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            labels=labels,
-            **kwargs
-        )
-        
-        if source_idx is not None:
-            source_embeds = self.source_embedding(source_idx).unsqueeze(1)
-            outputs.logits += source_embeds
-            
+        with autocast():
+            outputs = super().forward(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                labels=labels,
+                **kwargs
+            )
+            if source_idx is not None:
+                source_embeds = self.source_embedding(source_idx).unsqueeze(1)
+                outputs.logits += source_embeds
         return outputs
 
 class CustomTrainer(Trainer):
     def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
         labels = inputs.pop("labels")
-        source_idx = inputs.pop("source_idx")
-        outputs = model(**inputs, labels=labels, source_idx=source_idx)
+        with autocast():
+            source_idx = inputs.pop("source_idx")
+            outputs = model(**inputs, labels=labels, source_idx=source_idx)
         return (outputs.loss, outputs) if return_outputs else outputs.loss
 
-# Inicjalizacja komponentów
 source_mapper = SourceMapper()
-model_name = "EleutherAI/gpt-neo-2.7B" #"google/gemma-2-2b"
+model_name = "EleutherAI/gpt-neo-2.7B"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 tokenizer.pad_token = tokenizer.eos_token
 
-# Przygotowanie danych
-catalog_path = "file_catalog.json"
-data = prepare_dataset("files", catalog_path, source_mapper)
+data = prepare_dataset("files", "file_catalog.json", source_mapper)
 dataset = Dataset.from_list(data)
 tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=32)
 
-# Inicjalizacja modelu
 config = AutoModelForCausalLM.from_pretrained(model_name).config
-#model = CustomModel.from_pretrained(model_name, config=config)
 model = CustomModel.from_pretrained(model_name)
 model.config.gradient_checkpointing = True
 model.config.use_cache = False
 model.resize_token_embeddings(len(tokenizer))
 model.gradient_checkpointing_enable()
 
-# Konfiguracja treningu
 training_args = TrainingArguments(
     output_dir="./results",
     num_train_epochs=3,
@@ -182,18 +171,16 @@ training_args = TrainingArguments(
     save_strategy="steps",
     save_steps=1000,
     report_to="none",
-    gradient_checkpointing=True,
-    per_device_train_batch_size=4,   # batch size dla treningu
-    per_device_eval_batch_size=4,    # batch size dla ewaluacji
-    logging_dir='./logs'             # folder do logów
+    per_device_train_batch_size=4,
+    per_device_eval_batch_size=4,
+    logging_dir='./logs'
 )
 
-# Trening
 trainer = CustomTrainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_dataset,
-    data_collator=custom_collate_fn  # Użyj niestandardowego collate_fn
+    data_collator=custom_collate_fn
 )
 trainer.train()