metadata

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:
  - llama-cpp
  - gguf-my-repo

spacematt/Qwen3-30B-A3B-Q6_K-GGUF

This model was converted to GGUF format from Qwen/Qwen3-30B-A3B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

/think Parameters

temperature: 0.6
top_p: 0.95
top_k: 20
min_p: 0
presence_penalty: 1.5

/nothink Parameters

temperature: 0.7
top_p: 0.8
top_k: 20
min_p: 0
presence_penalty: 1.5

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo spacematt/Qwen3-30B-A3B-Q6_K-GGUF --hf-file qwen3-30b-a3b-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo spacematt/Qwen3-30B-A3B-Q6_K-GGUF --hf-file qwen3-30b-a3b-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps 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 spacematt/Qwen3-30B-A3B-Q6_K-GGUF --hf-file qwen3-30b-a3b-q6_k.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo spacematt/Qwen3-30B-A3B-Q6_K-GGUF --hf-file qwen3-30b-a3b-q6_k.gguf -c 2048