---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
tags:
- llama-cpp
- text-generation-inference
language:
- en
---

# **Qwen3-Coder-30B-A3B-Instruct-GGUF**

> Qwen3-Coder-30B-A3B-Instruct is a state-of-the-art large language model from the Qwen series, specifically optimized for advanced agentic coding, browser-based automation, and foundational programming tasks. Featuring 30.5 billion total parameters with 3.3 billion activated in a Mixture-of-Experts (MoE) architecture, it delivers strong performance and efficiency for complex code and tool-use scenarios. Its standout long-context capability natively processes up to 262,144 tokens—expandable to 1 million with Yarn—making it ideal for repository-scale code understanding and generation. 

> The model supports agentic coding with advanced function-call handling, and is compatible with popular local inference platforms like Ollama, LMStudio, and llama.cpp. Designed for both pretraining and post-training stages, Qwen3-Coder-30B-A3B-Instruct runs exclusively in non-thinking mode, ensuring fast, high-quality outputs for coding and automation workflows without requiring explicit configuration for thinking blocks

## 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 prithivMLmods/Qwen3-Coder-30B-A3B-Instruct-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo prithivMLmods/Qwen3-Coder-30B-A3B-Instruct-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q4_k_m.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 prithivMLmods/Qwen3-Coder-30B-A3B-Instruct-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo prithivMLmods/Qwen3-Coder-30B-A3B-Instruct-GGUF --hf-file qwen3-coder-30b-a3b-instruct-q4_k_m.gguf -c 2048
```