POKASH.PL
/
ably.do
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

mod

This commit is contained in:
l.gabrysiak 2025-02-25 19:19:17 +01:00
parent 9e3ff6db7f
commit e068a27261
1 changed files with 23 additions and 26 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

49
hft.py
View File

@ -145,28 +145,6 @@ class CustomTrainer(Trainer):
loss = outputs.loss loss = outputs.loss
return (loss, outputs) if return_outputs else loss return (loss, outputs) if return_outputs else loss
def generate_answer(question, model, tokenizer, source_mapper, max_length=200):
device = next(model.parameters()).device
inputs = tokenizer(question, return_tensors="pt", truncation=True, max_length=512)
inputs = {k: v.to(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 # Inicjalizacja komponentów
source_mapper = SourceMapper() source_mapper = SourceMapper()
model_name = "crumb/nano-mistral" model_name = "crumb/nano-mistral"
@ -210,9 +188,28 @@ trainer = CustomTrainer(
) )
trainer.train() trainer.train()
# Przykładowe użycie # Funkcja generująca odpowiedź
model.eval() def generate_answer(question, model, tokenizer, source_mapper, max_length=200):
inputs = tokenizer(question, return_tensors="pt", truncation=True, max_length=512)
outputs = 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 # Tymczasowe rozwiązanie
source = source_mapper.get_source(source_idx)
return f"{answer}\n\nŹródło: {source if source else 'Opracowanie własne'}"
# Przykład użycia
question = "Ile dni urlopu przysługuje pracownikowi?" question = "Ile dni urlopu przysługuje pracownikowi?"
answer = generate_answer(question, model, tokenizer, source_mapper) answer = generate_answer(question, model, tokenizer, source_mapper)
print("Pytanie:", question) print(answer)
print("Odpowiedź:", answer)