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
| 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. | |