powrót do gemma2
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l.gabrysiak
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7179a2de95
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eb1f2229f0
109
hft.py
109
hft.py
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@ -1,7 +1,7 @@
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import os
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import torch
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import torch.nn as nn
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from transformers import GPTNeoForCausalLM, Trainer, TrainingArguments, AutoTokenizer, AutoModelForCausalLM
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from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer
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from datasets import Dataset
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from PIL import Image
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import re
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@ -9,36 +9,28 @@ import pytesseract
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import docx2txt
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import PyPDF2
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import json
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from torch.cuda.amp import autocast
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from collections import defaultdict
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from huggingface_hub import login
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import torch
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torch.cuda.empty_cache()
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# Logowanie do Hugging Face Hub
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login(token="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")
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
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def free_memory():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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# Nowa klasa do zarządzania źródłami
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class SourceMapper:
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def __init__(self):
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self.source_to_idx = defaultdict(lambda: 0) # Domyślnie 0 dla nieznanych
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self.idx_to_source = {0: "Unknown"}
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self.next_idx = 1 # Indeksy od 1 dla znanych źródeł
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self.source_to_idx = defaultdict(lambda: len(self.source_to_idx))
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self.idx_to_source = {}
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def add_source(self, source):
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if source and source not in self.source_to_idx:
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idx = self.next_idx
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self.source_to_idx[source] = idx
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idx = self.source_to_idx[source]
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self.idx_to_source[idx] = source
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self.next_idx += 1
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def get_idx(self, source):
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return self.source_to_idx.get(source, 0)
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return self.source_to_idx[source] if source else -1
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def get_source(self, idx):
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return self.idx_to_source.get(idx, "Unknown")
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@ -62,7 +54,7 @@ def extract_text_from_file(file_path):
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with open(file_path, 'rb') as file:
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reader = PyPDF2.PdfReader(file)
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for page in reader.pages:
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text += page.extract_text() or ""
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text += page.extract_text()
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return text
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elif ext in ['.doc', '.docx']:
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return docx2txt.process(file_path)
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@ -84,7 +76,7 @@ def prepare_dataset(directory, catalog_path, source_mapper):
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doc_type = identify_legal_document(file, file_catalog)
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if doc_type != "Opracowanie własne":
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articles = re.split(r'(Art\.\s+\d+\.)', text)
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articles = re.split(r'(Art\.\s+\d+[\.\s])', text)
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for i in range(1, len(articles), 2):
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article_number = articles[i].strip()
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article_content = articles[i+1].strip() if i+1 < len(articles) else ""
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@ -100,7 +92,7 @@ def prepare_dataset(directory, catalog_path, source_mapper):
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for chunk in chunks:
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data.append({
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"text": chunk,
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"source_idx": 0
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"source_idx": -1 # Brak źródła
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})
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return data
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@ -120,74 +112,85 @@ def custom_collate_fn(batch):
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input_ids = torch.stack([torch.tensor(b["input_ids"]) for b in batch])
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attention_mask = torch.stack([torch.tensor(b["attention_mask"]) for b in batch])
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labels = torch.stack([torch.tensor(b["labels"]) for b in batch])
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# Dodajemy domyślne source_idx, jeśli nie istnieje
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source_idx = torch.tensor([b.get("source_idx", -1) for b in batch], dtype=torch.long)
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return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels, "source_idx": source_idx}
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class CustomModel(GPTNeoForCausalLM):
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class CustomModel(AutoModelForCausalLM):
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def __init__(self, config):
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super().__init__(config)
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self.source_embedding = nn.Embedding(
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num_embeddings=1000,
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num_embeddings=1000, # Maksymalna liczba unikalnych źródeł
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embedding_dim=config.hidden_size,
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padding_idx=0 # Poprawiony padding_idx
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padding_idx=-1
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)
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self.source_proj = nn.Linear(config.hidden_size, config.vocab_size)
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def forward(self, input_ids=None, attention_mask=None, labels=None, source_idx=None, **kwargs):
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with autocast():
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outputs = super().forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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labels=labels,
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**kwargs
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)
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if source_idx is not None:
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source_embeds = self.source_embedding(source_idx)
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source_projected = self.source_proj(source_embeds)
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outputs.logits += source_projected.unsqueeze(1)
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outputs = super().forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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labels=labels,
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**kwargs
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)
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if source_idx is not None:
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# Dodajemy embedding źródła do hidden states
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source_embeds = self.source_embedding(source_idx).unsqueeze(1)
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outputs.logits += source_embeds
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return outputs
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class CustomTrainer(Trainer):
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def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
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labels = inputs.pop("labels")
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source_idx = inputs.pop("source_idx")
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outputs = model(**inputs, labels=labels, source_idx=source_idx)
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return (outputs.loss, outputs) if return_outputs else outputs.loss
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# Inicjalizacja komponentów
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source_mapper = SourceMapper()
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model_name = "EleutherAI/gpt-neo-1.3B"
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model_name = "google/gemma-2-2b"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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tokenizer.pad_token = tokenizer.eos_token
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data = prepare_dataset("files", "file_catalog.json", source_mapper)
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# Przygotowanie danych
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catalog_path = "file_catalog.json"
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data = prepare_dataset("files", catalog_path, source_mapper)
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dataset = Dataset.from_list(data)
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tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=16)
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tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=32)
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# Inicjalizacja modelu
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config = AutoModelForCausalLM.from_pretrained(model_name).config
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model = CustomModel.from_pretrained(model_name)
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model.config.gradient_checkpointing = True
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model.config.use_cache = False
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model.resize_token_embeddings(len(tokenizer))
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model = CustomModel.from_pretrained(model_name, config=config)
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model.gradient_checkpointing_enable()
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# Konfiguracja treningu
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training_args = TrainingArguments(
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output_dir="./results",
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num_train_epochs=3,
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gradient_accumulation_steps=8,
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per_device_train_batch_size=2,
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gradient_accumulation_steps=4,
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learning_rate=2e-5,
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fp16=True,
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logging_steps=50,
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logging_steps=100,
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save_strategy="steps",
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save_steps=500,
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per_device_train_batch_size=2,
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per_device_eval_batch_size=2,
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logging_dir='./logs'
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save_steps=1000,
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report_to="none",
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gradient_checkpointing=True
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)
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trainer = Trainer(
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# Trening
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trainer = CustomTrainer(
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model=model,
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args=training_args,
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train_dataset=tokenized_dataset,
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data_collator=custom_collate_fn
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data_collator=custom_collate_fn, # Użyj niestandardowego collate_fn
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batch_size=8 # zmniejszenie rozmiaru batcha
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)
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trainer.train()
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free_memory()
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# Funkcja generująca odpowiedź
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def generate_answer(question, model, tokenizer, source_mapper, max_length=200):
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inputs = tokenizer(question, return_tensors="pt", truncation=True, max_length=512)
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@ -203,8 +206,8 @@ def generate_answer(question, model, tokenizer, source_mapper, max_length=200):
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answer = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
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# Pobierz źródło z ostatniego tokena
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last_token_logits = outputs.scores[-1]
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source_idx = torch.argmax(last_token_logits, dim=-1)[-1].item()
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last_token_id = outputs.sequences[0][-1].item()
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source_idx = model.source_embedding.weight.shape[0] - 1 # Tymczasowe rozwiązanie
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source = source_mapper.get_source(source_idx)
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return f"{answer}\n\nŹródło: {source if source else 'Opracowanie własne'}"
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