---
base_model: Qwen/Qwen3-30B-A3B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-30B-A3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen3-30B-A3B) for more details on the model.

---
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

    Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios.
    Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
    Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
    Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
    Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.

Model Overview
-
Qwen3-30B-A3B has the following features:

    Type: Causal Language Models
    Training Stage: Pretraining & Post-training
    Number of Parameters: 30.5B in total and 3.3B activated
    Number of Paramaters (Non-Embedding): 29.9B
    Number of Layers: 48
    Number of Attention Heads (GQA): 32 for Q and 4 for KV
    Number of Experts: 128
    Number of Activated Experts: 8
    Context Length: 32,768 natively and 131,072 tokens with YaRN.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF --hf-file qwen3-30b-a3b-q3_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF --hf-file qwen3-30b-a3b-q3_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF --hf-file qwen3-30b-a3b-q3_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Qwen3-30B-A3B-Q3_K_S-GGUF --hf-file qwen3-30b-a3b-q3_k_s.gguf -c 2048
```