strixAE is an audio-language reasoning agent obtained by applying Group Relative Policy Optimization (GRPO) to Audio-Reasoner and merging the trained weights into a standalone checkpoint. It uses the Qwen2AudioForConditionalGeneration architecture and is intended for audio understanding, structured reasoning, and audio-restoration pipeline decisions. The model is designed for research involving: - audio understanding across speech, music, and environmental sounds; - audio-quality and degradation analysis; - deciding whether candidate restoration operations should be applied or skipped; - selecting and ordering an audio-restoration pipeline; - structured audio reasoning and question…
Qwen2-Audio-7B-Instruct is an open-weight model for audio text to text from Qwen, released under Apache License 2.0. It has 8.4B parameters and a 8,192-token context. At 16-bit it needs about 20.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 323.7k downloads a month.
Qwen2-Audio is the new series of Qwen large audio-language models. Qwen2-Audio is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions.
Parameters8.4B
Context8,192
Weights16.8 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads323.7k
Runs On
What it takes to serve Qwen2-Audio-7B-Instruct (8.4B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 16.8 GB | 20.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 8.4 GB | 10.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 4.2 GB | 5.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 16, 2026.
Qwen2-Audio-7B-Instruct on every accelerator the SAVRN Index prices, at every precision
SAVRN's Notes on Qwen2-Audio-7B-Instruct
Audio in, text out, nothing else. Qwen2-Audio-7B-Instruct is the chat-tuned half of a pair; the publisher released a pretrained Qwen2-Audio-7B beside it. It carries 8.4 billion parameters and 16.8 GB of bfloat16 weights in 15 files. In use it needs 20.2 GB at 16-bit, 10.1 GB at 8-bit and 5.0 GB at 4-bit, so the whole thing lands on a single mid-size card, and the MI300X at $1.85 an hour that heads our cheapest-setup table is more machine than it needs.
Apache 2.0 allows commercial use, modification and redistribution, with the duty to keep the license, copyright and NOTICE files and state significant changes, plus a patent grant, so an audio analysis service built on it owes the publisher nothing further. The number to check is context: 8,192 tokens, which caps how much audio and conversation fit in one turn, even though the configuration also lists a 32,768 token sliding window. Released July 31, 2024, last touched January 12, 2025; the papers are arXiv:2407.10759 and arXiv:2311.07919.
Model Card
By Qwen, published under apache-2.0, revision 0a095220c30b.
Introduction
Qwen2-Audio is the new series of Qwen large audio-language models. Qwen2-Audio is capable of accepting various audio signal inputs and performing audio analysis or direct textual responses with regard to speech instructions. We introduce two distinct audio interaction modes:
-
voice chat: users can freely engage in voice interactions with Qwen2-Audio without text input;
-
audio analysis: users could provide audio and text instructions for analysis during the interaction;
We release Qwen2-Audio-7B and Qwen2-Audio-7B-Instruct, which are pretrained model and chat model respectively.
For more details, please refer to our Blog, GitHub, and Report.
Requirements
The code of Qwen2-Audio has been in the latest Hugging face transformers and we advise you to build from source with command pip install git+https://github.com/huggingface/transformers, or you might encounter the following error:
KeyError: 'qwen2-audio'
Quickstart
Configuration
- Architecture
- Qwen2AudioForConditionalGeneration
- Context length (tokens)
- 8,192
- Feed-forward size
- 11,008
- Vocabulary size
- 156,032
- Sliding window (tokens)
- 32,768
- RoPE base
- 10,000
- Stored precision
- bfloat16
- Model type
- qwen2_audio
Identity and Version
- Repository
- Qwen/Qwen2-Audio-7B-Instruct
- Publisher
- Qwen
- Task
- Audio text to text
- Modality
- Other
- Library
- transformers
- Parameters
- 8.4B parameters
- Languages
- en
- Revision
- 0a095220c30b7b31434169c3086508ef3ea5bf0a
- First published
- 2024-07-31
- Last updated
- 2025-01-12
Files and Weights
15 files, 16.8 GB in total. The weights are 5 files totalling 16.8 GB in safetensors.
Weights5 files · 16.8 GB
Configuration4 files · 80.4 KB
Tokenizer4 files · 12.1 MB
Documentation1 file · 8.7 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model-00001-of-00005.safetensors | Weights | 3.9 GB | 383de5b5b06f |
| model-00002-of-00005.safetensors | Weights | 4.0 GB | 610e59a23cdf |
| model-00003-of-00005.safetensors | Weights | 4.0 GB | b9ea76e97226 |
| model-00004-of-00005.safetensors | Weights | 3.6 GB | d68dee591619 |
| model-00005-of-00005.safetensors | Weights | 1.3 GB | a447dab3e72f |
| config.json | Configuration | 853 B | — |
| generation_config.json | Configuration | 230 B | — |
| model.safetensors.index.json | Configuration | 79.0 KB | — |
| preprocessor_config.json | Configuration | 342 B | — |
| README.md | Documentation | 8.7 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| merges.txt | Tokenizer | 1.7 MB | — |
| tokenizer.json | Tokenizer | 7.0 MB | — |
| tokenizer_config.json | Tokenizer | 638.3 KB | — |
| vocab.json | Tokenizer | 2.8 MB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 16.8 GB
Download from Qwen
Released by Qwen through ModelScope. Read the license.
Built From
- Described by arXiv:2311.07919
- Described by arXiv:2407.10759
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 16.8 GB |
| 16-bit | 16.8 GB |
| 8-bit | 8.4 GB |
| 4-bit | 4.2 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Qwen2-Audio-7B-Instruct
How much GPU memory does Qwen2-Audio-7B-Instruct need?
About 20.2 GB at 16-bit and 5 GB at 4-bit: the weights (8.4B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Qwen2-Audio-7B-Instruct on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use Qwen2-Audio-7B-Instruct commercially?
Yes. Qwen2-Audio-7B-Instruct is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is Qwen2-Audio-7B-Instruct's context length?
8,192 tokens, from the maximum position embeddings in its published configuration.
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