Triangle104/Gemma-3-Glitter-27B-Q5_K_M-GGUF

This model was converted to GGUF format from allura-org/Gemma-3-Glitter-27B 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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A creative writing model based on Gemma 3 27B.

Columbidae/gemma-3-27b-half, a 50/50 merge of 27B IT and 27B PT, was used as the base model. (This was done because of the success of Starshine, a 50/50 IT and PT merge.)

The inclusion of PT model does weaken the instruct, but it also weakens the censorship/hesitancy to participate in certain fictional stories. The prose also becomes more natural with less of the IT model included.

This model does better with short and to-the-point prompts. Long, detailed system prompts will often confuse it. (Tested with 1000-2000 token system prompts to lackluster results compared to 100-500 token prompts).

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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/Gemma-3-Glitter-27B-Q5_K_M-GGUF --hf-file gemma-3-glitter-27b-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Gemma-3-Glitter-27B-Q5_K_M-GGUF --hf-file gemma-3-glitter-27b-q5_k_m.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/Gemma-3-Glitter-27B-Q5_K_M-GGUF --hf-file gemma-3-glitter-27b-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Gemma-3-Glitter-27B-Q5_K_M-GGUF --hf-file gemma-3-glitter-27b-q5_k_m.gguf -c 2048