Any-to-Any
Transformers
English
Polish
transformer
hardbanai, hardbanrecords, audio-generation, text-generation, image-generation, ai-publishing, creative-ai, emotional-style, multimodal, polish-language, musicgen
Instructions to use HardbanRecordsLab/HardbanAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HardbanRecordsLab/HardbanAI with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HardbanRecordsLab/HardbanAI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,152 Bytes
6a9d6aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import json
# Inicjalizacja modelu i tokenizera (ładuj tylko raz, przy starcie handlera)
model_path = "./" # ścieżka do folderu z modelem
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
model.eval()
def handle(event, context):
"""
event: dict, z kluczem 'text' zawierającym tekst do przetworzenia
context: obiekt kontekstowy (zależny od platformy, nieużywany tu)
Zwraca dict z wygenerowanym tekstem.
"""
# Wczytaj input
try:
if isinstance(event, str):
event = json.loads(event)
text = event.get("text", "")
except Exception as e:
return {"error": f"Błąd parsowania danych wejściowych: {str(e)}"}
if not text:
return {"error": "Brak tekstu do przetworzenia."}
# Tokenizacja i generacja
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(**inputs)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
return {"generated_text": result}
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