Feature Extraction
Transformers
Safetensors
llada
diffusion-language-model
dllm
LLaDA
WINO
WINO-plus
custom_code
Instructions to use QinFFF/WINO-plus-LLaDA-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QinFFF/WINO-plus-LLaDA-8B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="QinFFF/WINO-plus-LLaDA-8B-Instruct", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QinFFF/WINO-plus-LLaDA-8B-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| license: other | |
| library_name: transformers | |
| base_model: GSAI-ML/LLaDA-8B-Instruct | |
| tags: | |
| - diffusion-language-model | |
| - dllm | |
| - LLaDA | |
| - WINO | |
| - WINO-plus | |
| # WINO-plus-LLaDA-8B-Instruct | |
| This repository contains the full merged 8B model weights of WINO+, initialized from LLaDA-8B-Instruct. | |
| WINO+ is a trajectory-injection method for diffusion large language models. It transfers the verified denoising order discovered by WINO into model parameters, enabling faster and higher-quality generation. | |
| The model corresponds to the WINO+ method described in *Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers*. | |