mod

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  # Zmiana importu
+from transformers import AutoTokenizer, GPTNeoForCausalLM  # Poprawiono importy
 from datasets import Dataset
 from PIL import Image
 import re
@@ -151,7 +150,7 @@ class CustomTrainer(Trainer):
 
 # 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
 
@@ -162,9 +161,8 @@ 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)
+config = GPTNeoForCausalLM.from_pretrained(model_name).config
+model = CustomModel.from_pretrained(model_name, config=config)
 model.resize_token_embeddings(len(tokenizer))
 model.gradient_checkpointing_enable()
 
@@ -210,7 +208,7 @@ def generate_answer(question, model, tokenizer, source_mapper, max_length=200):
     
     # Pobierz źródło z ostatniego tokena
     last_token_id = outputs.sequences[0][-1].item()
-    source_idx = model.source_embedding.weight.shape[0] - 1  # Tymczasowe rozwiązanie
+    source_idx = last_token_id % 1000  # Zaktualizuj sposób określania źródła
     source = source_mapper.get_source(source_idx)
     
     return f"{answer}\n\nŹródło: {source if source else 'Opracowanie własne'}"