Poprawka fukcji uzycia

hft.py

@@ -107,12 +107,11 @@ def tokenize_function(examples):
     return tokenized
 
 def custom_collate_fn(batch):
-    input_ids = torch.stack([torch.tensor(b["input_ids"]) for b in batch])
+    device = next(model.parameters()).device
-    attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch])
+    input_ids = torch.stack([torch.tensor(b["input_ids"]) for b in batch]).to(device)
-    labels = torch.stack([torch.tensor(b["labels"]) for b in batch])
+    attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch]).to(device)
-    
+    labels = torch.stack([torch.tensor(b["labels"]) for b in batch]).to(device)
-    source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long)
+    source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long).to(device)
-    #print("source_idx shape:", source_idx.shape)  # Debugowanie
     return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels, "source_idx": source_idx}
 
 class CustomModel(nn.Module):
@@ -127,8 +126,6 @@ class CustomModel(nn.Module):
         
     def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None, **kwargs):
         if source_idx is not None:
-            #print("Max source_idx:", torch.max(source_idx))
-            #print("Num embeddings:", self.source_embedding.num_embeddings)
             source_idx = torch.clamp(source_idx, 0, self.source_embedding.num_embeddings - 1)
             source_embeds = self.source_embedding(source_idx).unsqueeze(1).expand(-1, input_ids.size(1), -1)
             hidden_states = self.base_model.get_input_embeddings()(input_ids) + source_embeds
@@ -146,6 +143,27 @@ class CustomTrainer(Trainer):
         loss = outputs.loss
         return (loss, outputs) if return_outputs else loss
 
+def generate_answer(question, model, tokenizer, source_mapper, max_length=200):
+    inputs = tokenizer(question, return_tensors="pt", truncation=True, max_length=512)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    
+    outputs = model.base_model.generate(
+        **inputs,
+        max_length=max_length,
+        num_return_sequences=1,
+        return_dict_in_generate=True,
+        output_scores=True,
+    )
+    
+    answer = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
+    
+    # Pobierz źródło z ostatniego tokena
+    last_token_id = outputs.sequences[0][-1].item()
+    source_idx = model.source_embedding.weight.shape[0] - 1
+    source = source_mapper.get_source(source_idx)
+    
+    return f"{answer}\n\nŹródło: {source if source else 'Opracowanie własne'}"
+
 # Inicjalizacja komponentów
 source_mapper = SourceMapper()
 model_name = "crumb/nano-mistral"
@@ -160,9 +178,9 @@ tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=8)
 
 # Inicjalizacja modelu
 config = AutoModelForCausalLM.from_pretrained(model_name).config
-#print("Vocabulary size:", config.vocab_size)
 model = CustomModel(model_name, config)
-#model.to("cpu")  # Zmienione na CPU dla debugowania
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
 
 # Konfiguracja treningu
 training_args = TrainingArguments(
@@ -171,7 +189,7 @@ training_args = TrainingArguments(
     per_device_train_batch_size=2,
     gradient_accumulation_steps=4,
     learning_rate=2e-5,
-    fp16=False,  # Wyłączone dla CPU
+    fp16=torch.cuda.is_available(),
     logging_steps=1,
     logging_dir="./logs",
     save_strategy="steps",
@@ -189,40 +207,9 @@ trainer = CustomTrainer(
 )
 trainer.train()
 
-# Funkcja generująca odpowiedź
+# Przykładowe użycie
-def generate_answer(question, model, tokenizer, source_mapper, max_length=200):
+model.eval()
-    inputs = tokenizer(question, return_tensors="pt", truncation=True, max_length=512)
-    
-    outputs = model.base_model.generate(
-        **inputs,
-        max_length=max_length,
-        num_return_sequences=1,
-        return_dict_in_generate=True,
-        output_scores=True,
-    )
-    
-    answer = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
-    
-    # Pobierz źródło z ostatniego tokena
-    last_token_id = outputs.sequences[0][-1].item()
-    source_idx = model.source_embeddi
-
-
-
-
-
-# Po zakończeniu treningu modelu
-
-# Przygotowanie niezbędnych komponentów
-model.eval()  # Przełącz model w tryb ewaluacji
-model = model.to("cuda" if torch.cuda.is_available() else "cpu")  # Przenieś model na GPU, jeśli jest dostępne
-
-# Przykładowe pytanie
 question = "Ile dni urlopu przysługuje pracownikowi?"
-
-# Generowanie odpowiedzi
 answer = generate_answer(question, model, tokenizer, source_mapper)
-
-# Wyświetlenie wyniku
 print("Pytanie:", question)
 print("Odpowiedź:", answer)