Triangle104/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.

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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.

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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 Triangle104/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 Triangle104/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 Triangle104/Qwen3-30B-A3B-Q6_K-GGUF --hf-file qwen3-30b-a3b-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Qwen3-30B-A3B-Q6_K-GGUF --hf-file qwen3-30b-a3b-q6_k.gguf -c 2048