README.md

---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-30B-A3B
tags:
- TensorBlock
- GGUF
---

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## Qwen/Qwen3-30B-A3B - GGUF

This repo contains GGUF format model files for [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5214](https://github.com/ggml-org/llama.cpp/commit/eaea3253244dc4bbe07f6cd81325847ccc6cf93e).

## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
  <th style="font-size: 25px;">Awesome MCP Servers</th>
  <th style="font-size: 25px;">TensorBlock Studio</th>
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    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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    ">👀 See what we built 👀</a>
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## Prompt template

```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Qwen3-30B-A3B-Q2_K.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q2_K.gguf) | Q2_K | 11.259 GB | smallest, significant quality loss - not recommended for most purposes |
| [Qwen3-30B-A3B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q3_K_S.gguf) | Q3_K_S | 13.292 GB | very small, high quality loss |
| [Qwen3-30B-A3B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q3_K_M.gguf) | Q3_K_M | 14.712 GB | very small, high quality loss |
| [Qwen3-30B-A3B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q3_K_L.gguf) | Q3_K_L | 15.901 GB | small, substantial quality loss |
| [Qwen3-30B-A3B-Q4_0.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q4_0.gguf) | Q4_0 | 17.304 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Qwen3-30B-A3B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q4_K_S.gguf) | Q4_K_S | 17.456 GB | small, greater quality loss |
| [Qwen3-30B-A3B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q4_K_M.gguf) | Q4_K_M | 18.557 GB | medium, balanced quality - recommended |
| [Qwen3-30B-A3B-Q5_0.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q5_0.gguf) | Q5_0 | 21.081 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Qwen3-30B-A3B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q5_K_S.gguf) | Q5_K_S | 21.081 GB | large, low quality loss - recommended |
| [Qwen3-30B-A3B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q5_K_M.gguf) | Q5_K_M | 21.726 GB | large, very low quality loss - recommended |
| [Qwen3-30B-A3B-Q6_K.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q6_K.gguf) | Q6_K | 25.093 GB | very large, extremely low quality loss |
| [Qwen3-30B-A3B-Q8_0.gguf](https://huggingface.co/tensorblock/Qwen_Qwen3-30B-A3B-GGUF/blob/main/Qwen3-30B-A3B-Q8_0.gguf) | Q8_0 | 32.484 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/Qwen_Qwen3-30B-A3B-GGUF --include "Qwen3-30B-A3B-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/Qwen_Qwen3-30B-A3B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```