Instructions to use nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K
- SGLang
How to use nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K with Docker Model Runner:
docker model run hf.co/nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K
LLaDA-Instruct-JustGRPO
This model is LLaDA-8B-Instruct fine-tuned with JustGRPO on GSM8K.
It was introduced in the paper The Flexibility Trap: Why Arbitrary Order Limits Reasoning Potential in Diffusion Language Models.
Method
JustGRPO is a minimalist RL approach for diffusion language models. Instead of complex diffusion-specific RL adaptations, we simply treat dLLMs as autoregressive models during training and apply standard GRPO. See our paper for details.
- Project Page: https://nzl-thu.github.io/the-flexibility-trap
- Paper: arXiv:2601.15165
- Code: https://github.com/LeapLabTHU/JustGRPO
Performance on GSM8K
| Sequence Length | 128 | 256 | 512 |
|---|---|---|---|
| Accuracy (%) | 83.8 | 89.1 | 89.8 |
Usage
For generation and evaluation, please refer to our GitHub repository.
Citation
@article{ni2026flexibility,
title={The Flexibility Trap: Why Arbitrary Order Limits Reasoning Potential in Diffusion Language Models},
author={Ni, Zanlin and Wang, Shenzhi and Yue, Yang and Yu, Tianyu and Zhao, Weilin and Hua, Yeguo and Chen, Tianyi and Song, Jun and Yu, Cheng and Zheng, Bo and Huang, Gao},
journal={arXiv preprint arXiv:2601.15165},
year={2026}
}
- Downloads last month
- 83
Model tree for nzl-thu/LLaDA-Instruct-JustGRPO-GSM8K
Base model
GSAI-ML/LLaDA-8B-Instruct