Instructions to use redche7/qwen3-4b-p300-text-correction-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use redche7/qwen3-4b-p300-text-correction-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "redche7/qwen3-4b-p300-text-correction-lora") - Notebooks
- Google Colab
- Kaggle
File size: 718 Bytes
30940dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | Recover the most likely original Russian phrase from the final user message. The input is either unchanged or was corrupted only by independent character-level noise. Individual characters may have been substituted, duplicated directly beside themselves, or deleted completely. Multiple errors may occur in the same phrase. The corruption does not reorder the surviving characters. Restore the exact original wording and punctuation. Do not paraphrase, replace words with synonyms, or rewrite the phrase. Preserve informal language, slang, and source errors not caused by the described corruption. Return exactly one corrected Russian phrase and nothing else. If the phrase is already correct, return it unchanged. |