--- title: "Qwen2-Audio-7B-GGUF" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/da1958c7-81da-4f11-84d3-9f664173c790" updated: "2026-11-13" --- # Qwen2-Audio-7B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs Qwen2-Audio locally to chat by voice and analyze audio, including speech translation, transcription and sound detection. GGUF copies for Nexa-SDK, for people who want audio AI on their own device. Qwen2-Audio-7B-GGUF is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by NexaAI ## About ### What it is This repository holds GGUF copies of Qwen2-Audio, an audio-and-text model, packaged by NexaAI so it runs locally with Nexa-SDK. It takes audio and text as input, so you can talk to it without a separate speech recognition step. The card says it supports English, Chinese and major European languages. The model has 7.8B parameters. ### Who it suits It suits people who want to talk to an AI or have it analyze audio on their own computer instead of sending files to a service. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Productivity | Everyday users | Ask spoken questions and get answers | Ask a daily question by voice and read the reply | | Media | Editors and producers | Transcribe or summarize recordings | Drop in an audio file and ask for a summary | | Language | Translators | Translate speech | Play a clip and ask for a translation | | Sound work | Audio hobbyists | Analyze music or background noise | Ask what is in a mixed recording | If you only need text answers, you can skip it, because it is built around audio input. ### Where to use it You use it through Nexa-SDK, in a terminal or in a local Streamlit interface, on a Windows, macOS or Linux computer. - A terminal, with Nexa-SDK installed - A local Streamlit interface, started with a flag - Edge devices and laptops that can spare the memory | Use case | Who | What they get back | | --- | --- | --- | | Voice chat | Everyday users | Answers and suggestions to spoken questions | | Audio analysis | Analysts | Summaries, extracted information and transcripts | | Sound detection | Hobbyists | A description of background noise, music and mixed audio | ### How to set it up You need Nexa-SDK, an on-device inference framework that you can install as a Python package or an executable installer. The default q4\_K\_M version needs 4.2 GB of RAM. - Nexa-SDK, installed from the project's install options - About 4.2 GB of RAM for the default version - Windows, macOS or Linux 1. Install Nexa-SDK. 2. Run the command in your terminal. 3. Add your audio file and an optional text prompt. ```bash nexa run qwen2audio ``` ### How to use it In the terminal, drag your audio file into the window, or type the file path on Linux, then add a text prompt to steer the analysis. Leave the prompt empty to send direct voice input. To use a local interface instead, add the flag shown below. ```bash nexa run qwen2audio -st ``` | Tool | What it does | | --- | --- | | nexa run qwen2audio | Runs the model in the terminal with the default q4\_K\_M version | | nexa run qwen2audio -st | Opens a local Streamlit interface instead | | audio file | Dragged in, or entered as a path on Linux | | text prompt | Guides the analysis, or can be left empty | ### Example You drag a recording into the terminal and type "Summarize this audio." The model listens to the clip and answers in text with a summary. Without a prompt, it treats the clip as a voice message and replies to it. ### For newcomers and experts If you are new to this, think of it as an assistant that can hear. A first small step is to install Nexa-SDK, run the default command and speak a short question through an audio file. If you know the field, the model takes audio and text together and answers without an ASR step in front of it. Nexa-SDK serves GGUF quantizations, and the card says the default q4\_K\_M version is the one that runs from the plain command. Other quantizations are listed with their RAM needs on the project's page. Audio file plus optional text prompt → Nexa-SDK loads the GGUF model → Model reads audio and text together → Text answer comes back ### Good to know The card says Qwen2-Audio beats earlier top models and its own predecessor across the tasks it tested, and it shows results in charts that are not reproduced here. To get only the raw GGUF files, Hugging Face lists these commands. ```sh pip install -U huggingface_hub hf download NexaAI/Qwen2-Audio-7B-GGUF ``` Size in GB | Label | Value | | --- | --- | | 4-bit weights, estimate | 3.9 GB | | RAM for the default version | 4.2 GB | - License: Apache-2.0 - Cost to run: no API key or paid service is named, and the default version needs 4.2 GB of RAM - Data it reaches: the download comes from Hugging Face, and the model works on the audio and text you give it on your own device - Not for: work without audio, or devices with less than 4.2 GB of free RAM ## README ````markdown # Qwen2-Audio-7B-GGUF Runs Qwen2-Audio locally to chat by voice and analyze audio, including speech translation, transcription and sound detection. GGUF copies for Nexa-SDK, for people who want audio AI on their own device. Made by NexaAI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This repository holds GGUF copies of Qwen2-Audio, an audio-and-text model, packaged by NexaAI so it runs locally with Nexa-SDK. It takes audio and text as input, so you can talk to it without a separate speech recognition step. The card says it supports English, Chinese and major European languages. The model has 7.8B parameters. Who it suits: It suits people who want to talk to an AI or have it analyze audio on their own computer instead of sending files to a service. ```table Industry | Who uses it | How they use it | Example Productivity | Everyday users | Ask spoken questions and get answers | Ask a daily question by voice and read the reply Media | Editors and producers | Transcribe or summarize recordings | Drop in an audio file and ask for a summary Language | Translators | Translate speech | Play a clip and ask for a translation Sound work | Audio hobbyists | Analyze music or background noise | Ask what is in a mixed recording ``` If you only need text answers, you can skip it, because it is built around audio input. Where to use it: You use it through Nexa-SDK, in a terminal or in a local Streamlit interface, on a Windows, macOS or Linux computer. - A terminal, with Nexa-SDK installed - A local Streamlit interface, started with a flag - Edge devices and laptops that can spare the memory ```table Use case | Who | What they get back Voice chat | Everyday users | Answers and suggestions to spoken questions Audio analysis | Analysts | Summaries, extracted information and transcripts Sound detection | Hobbyists | A description of background noise, music and mixed audio ``` How to set it up: You need Nexa-SDK, an on-device inference framework that you can install as a Python package or an executable installer. The default q4_K_M version needs 4.2 GB of RAM. - Nexa-SDK, installed from the project's install options - About 4.2 GB of RAM for the default version - Windows, macOS or Linux 1. Install Nexa-SDK. 2. Run the command in your terminal. 3. Add your audio file and an optional text prompt. ```bash nexa run qwen2audio ``` How to use it: In the terminal, drag your audio file into the window, or type the file path on Linux, then add a text prompt to steer the analysis. Leave the prompt empty to send direct voice input. To use a local interface instead, add the flag shown below. ```bash nexa run qwen2audio -st ``` ```table Tool | What it does nexa run qwen2audio | Runs the model in the terminal with the default q4_K_M version nexa run qwen2audio -st | Opens a local Streamlit interface instead audio file | Dragged in, or entered as a path on Linux text prompt | Guides the analysis, or can be left empty ``` Example: You drag a recording into the terminal and type "Summarize this audio." The model listens to the clip and answers in text with a summary. Without a prompt, it treats the clip as a voice message and replies to it. For newcomers and experts: If you are new to this, think of it as an assistant that can hear. A first small step is to install Nexa-SDK, run the default command and speak a short question through an audio file. If you know the field, the model takes audio and text together and answers without an ASR step in front of it. Nexa-SDK serves GGUF quantizations, and the card says the default q4_K_M version is the one that runs from the plain command. Other quantizations are listed with their RAM needs on the project's page. ```flow Audio file plus optional text prompt Nexa-SDK loads the GGUF model Model reads audio and text together Text answer comes back ``` Good to know: The card says Qwen2-Audio beats earlier top models and its own predecessor across the tasks it tested, and it shows results in charts that are not reproduced here. To get only the raw GGUF files, Hugging Face lists these commands. ```sh pip install -U huggingface_hub hf download NexaAI/Qwen2-Audio-7B-GGUF ``` ```chart Title: Size in GB 4-bit weights, estimate: 3.9 GB RAM for the default version: 4.2 GB ``` - License: Apache-2.0 - Cost to run: no API key or paid service is named, and the default version needs 4.2 GB of RAM - Data it reaches: the download comes from Hugging Face, and the model works on the audio and text you give it on your own device - Not for: work without audio, or devices with less than 4.2 GB of free RAM ## On Hugging Face, 13 November 2026 - Task: audio text to text - Parameters: 7.8B - Weights: about 3.9 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 2,522 - Likes: 177 - License: apache-2.0 - Languages: en - Tags: gguf, audio-text-to-text, chat, audio, conversational ## Links - Model: https://huggingface.co/NexaAI/Qwen2-Audio-7B-GGUF - Files: https://huggingface.co/NexaAI/Qwen2-Audio-7B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download NexaAI/Qwen2-Audio-7B-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [NexaAI/Qwen2-Audio-7B-GGUF on Hugging Face](https://huggingface.co/NexaAI/Qwen2-Audio-7B-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Qwen2-Audio-7B-GGUF (this page) | 2,522 | 177 | 7.8B | apache-2.0 | 2024-11-25 | | [Surya 2.5 GGUF](https://mindket.com/p/0d08b30b-fd57-4e61-a8a1-da92d56f2592) | 126,232 | 8 | 589M | openrail | 2026-10-29 | | [MiniCPM-OCR 1.0 GGUF](https://mindket.com/p/7ac4f5b0-b475-4cdd-b31c-2ea3e524a0be) | 58,162 | 133 | 752M | apache-2.0 | 2026-11-09 | | [GigaChat3.1-10B-A1.8B-GGUF](https://mindket.com/p/3e03c635-d97c-4ad9-9c9e-80af78adeb40) | 19,408 | 95 | 10.7B | mit | 2026-03-25 | | [LFM2.5-350M-home-assistant-sft](https://mindket.com/p/017243ad-59ae-4d9a-aa08-3283bdb44d99) | 2,229 | 8 | 353M | apache-2.0 | 2026-06-28 | | [parakeet-ultra-gguf](https://mindket.com/p/0f756bf9-7825-4294-af8b-c884a8d3877d) | 1,375,499 | 8 | 627M | cc-by-4.0 | 2026-10-16 | Numbers as Mindket last read them, on 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-11-25 - First published: 2024-10-23 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open NexaAI/Qwen2-Audio-7B-GGUF on Hugging Face](https://huggingface.co/NexaAI/Qwen2-Audio-7B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/da1958c7-81da-4f11-84d3-9f664173c790) - [Its page on Mindket](https://mindket.com/p/da1958c7-81da-4f11-84d3-9f664173c790)