Downloads · 30 days
48
32% of all-time downloads
AudioRubrics
umd-zhou-lab/AudioRubrics
AudioRubrics is a audio-text-to-text model from umd-zhou-lab. Use it for the audio-text-to-text task on the model card, and read the license before you ship it in a product. The card lists the license as other.
The model from Reinforcement Learning with Evolving Rubrics as Rewards for Audio Reasoning: Qwen2.5-Omni-7B post-trained with GRPO using self-evolving, audio-grounded rubric rewards and an overthinking penalty.
All-time downloads
150
Public
Parameters
12.2B
25.3 GB on disk
Likes
3
Public
Repo makeup
Click a slice to open those files.
.safetensors25.3 GB · 100%
Parameter types
How the weights are stored.
BF1611.7B · 96%
Base models
Model card
From the Hugging Face model README
AudioRubrics
The model from Reinforcement Learning with Evolving Rubrics as Rewards for Audio Reasoning: Qwen2.5-Omni-7B post-trained with GRPO using self-evolving, audio-grounded rubric rewards and an overthinking penalty.
- Project page: https://audiorubrics.github.io
- Code: https://github.com/tianyi-lab/AudioRubrics
- Rubric dataset: https://huggingface.co/datasets/umd-zhou-lab/AVQA-Audio-Rubrics
This is the full merged checkpoint (thinker merged back into the complete Omni model) and can be served directly with vLLM:
vllm serve umd-zhou-lab/AudioRubrics --served-model-name omni --trust-remote-code \
--max-model-len 8192 --limit-mm-per-prompt '{"audio":1}'
See the GitHub repository for training and evaluation instructions.
- Task
- Audio-Text-to-Text
- Type
- qwen2_5_omni
- License
- other
- Languages
- en
- Created
- Jul 11, 2026
- Updated
- Aug 5, 2026