Qwen Audio AHA
This repository contains the official LoRA adapter for Qwen2.5-Omni-7B (Thinker), fine-tuned using the AHA (Audio Hallucination Alignment) framework.
Model source
- Kind
- LoRA
- File size
- 154 MB
- Base model
- Qwen/Qwen2.5-Omni-7B
- Version
- vadapter_model
- Updated
- Oct 26, 2026
- Downloads
- 26
- Source
- Hugging Face
Source description
Qwen-Audio-AHA (LoRA Adapter)
This repository contains the official LoRA adapter for Qwen2.5-Omni-7B (Thinker), fine-tuned using the AHA (Audio Hallucination Alignment) framework.
Model Description
AHA is a framework designed to mitigate hallucinations in Large Audio-Language Models (LALMs) by focusing on fine-grained temporal reasoning and counterfactual alignment. By leveraging counterfactual hard negative mining, the pipeline constructs high-quality preference data that forces models to distinguish strict acoustic evidence from linguistically plausible fabrications.
- Paper: AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives
- GitHub Repository: https://github.com/LLM-VLM-GSL/AHA
- Base Model: Qwen/Qwen2.5-Omni-7B
Intended Use
- Primary Task: Audio reasoning and reducing hallucinations in audio-to-text tasks.
- Languages Supported: All languages supported by the base Qwen2.5-Omni-7B model.
Sample Usage
You can load this model using the peft and transformers libraries. Note that librosa is required for audio loading in this example.
import torch
import librosa
from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniProcessor
from peft import PeftModel
device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "Qwen/Qwen2.5-Omni-7B"
adapter_id = "ASU-GSL/Qwen-Audio-AHA"
# Load base model and processor
processor = Qwen2_5OmniProcessor.from_pretrained(model_id)
model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained(
model_id, torch_dtype="auto", device_map="auto"
)
# Load LoRA adapter
model = PeftModel.from_pretrained(model, adapter_id)
# Load Audio
# Replace "example.wav" with the path to your audio file
audio, _ = librosa.load("example.wav", sr=processor.feature_extractor.sampling_rate)
prompt = "<|audio|>
Describe the temporal order of events in this audio."
inputs = processor(text=prompt, audios=audio, return_tensors="pt").to(device)
# Generate
generate_ids = model.generate(**inputs, max_new_tokens=256)
print(processor.batch_decode(generate_ids, skip_special_tokens=True)[0])
Citation
@article{chen2025aha,
title={AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives},
author={Chen, Yanxi and Zhu, Wenhui and Chen, Xiwen and Wang, Zhipeng and Li, Xin and Qiu, Peijie and Wang, Hao and Dong, Xuanzhao and Xiong, Yujian and Schneider, Anderson and others},
journal={arXiv preprint arXiv:2512.24052},
year={2025}
}
Sources
1 source
Sources
1 sourceVerified Oct 26
Model artifacts
1 artifact
Model artifacts
1 artifactadapter_model.safetensors
safetensors · 154 MB · SHA-256 34121a1501a7…9c00 · Hugging Face
Download fileSource excerpts
3 excerpts
Source excerpts
3 excerpts--- base_model: Qwen/Qwen2.5-Omni-7B datasets: - ASU-GSL/AHA library_name: peft license: apache-2.0 pipeline_tag: audio-text-to-text tags: - lora - qwen2.5-omni - multimodal - audio --- # Qwen-Audio-AHA (LoRA Adapter) This repository contains the official LoRA adapter for **Qwen2.5-Omni-7B** (Thinker), fine-tuned using the **AHA (Audio Hallucination Alignment)** framework. ## Model Description AHA is a framework designed to mitigate hallucinations in Large Audio-Language Models (LALMs) by focusing on fine-grained temporal reasoning and counterfactual alignment. By leveraging counterfactual hard negative mining, the pipeline constructs high-quality preference data that forces models to distinguish strict acoustic evidence from linguistically plausible fabrications. - **Paper:** [AHA: Aligning Large Audio-Language Models for Reasoning Hallucinations via Counterfactual Hard Negatives](https://huggingface.co/papers/2512.24052) - **GitHub Repository:** [https://github.com/LLM-VLM-GSL/AHA](https://github.com/LLM-VLM-GSL/AHA) - **Base Model:** [Qwen/Qwen2.5-Omni-7B](https://huggingface.co/Qwen/Qwen2.5-Omni-7B) ## Intended Use - **Primary Task:** Audio reasoning and reducing hallucinations in audio-to-text tasks. - **Languages Supported:** All languages supported by the base Qwen2.5-Omni-7B model. ## Sample Usage You can load this model using the `peft` and `transformers` libraries. Note that `librosa` is required for audio loading in this example. ```python import torch import librosa from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniProcessor from peft import PeftModel device = "cuda" if torch.cuda.is_available() else "cpu" model_id = "Qwen/Qwen2.5-Omni-7B" adapter_id = "ASU-GSL/Qwen-Audio-AHA" # Load base model and processor processor = Qwen2_5OmniProcessor.from_pretrained(model_id) model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained( model_id, torch_dtype="auto", device_map="auto" ) # Load LoRA adapter model = PeftModel.from_pretrained(model, adapter_id) # Load Audio # Replace "example.wav" with the path to your audio file audio, _ = librosa.load("example.wav", sr=processor.feature_extractor.sampling_rate) prompt = "<|audio|> Describe the temporal order of events in this audio." inputs = processor(text=prompt, audios=audio, return_tensors="pt").to(device) # Generate generate_ids = model.generate(**inp...
Source context: 26 downloads · 1 likes · Pipeline audio-text-to-text · Library peft · Repo ASU-GSL/Qwen-Audio-AHA
Source context: 6 downloads · 0 likes · Pipeline audio-text-to-text · Library peft · Repo ASU-GSL/Qwen-Audio-AHA
