Triangle104/Qwen3-30B-A1.5B-High-Speed-Q3_K_S-GGUF

This model was converted to GGUF format from DavidAU/Qwen3-30B-A1.5B-High-Speed 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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This is a simple "finetune" of the Qwen's "Qwen 30B-A3B" (MOE) model, setting the experts in use from 8 to 4 (out of 128 experts).

This method close to doubles the speed of the model and uses 1.5B (of 30B) parameters instead of 3B (of 30B) parameters. Depending on the application you may want to use the regular model ("30B-A3B"), and use this model for simpler use case(s) although I did not notice any loss of function during routine (but not extensive) testing.

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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-A1.5B-High-Speed-Q3_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q3_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q3_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q3_k_s.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-A1.5B-High-Speed-Q3_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q3_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Qwen3-30B-A1.5B-High-Speed-Q3_K_S-GGUF --hf-file qwen3-30b-a1.5b-high-speed-q3_k_s.gguf -c 2048