Triangle104/Gemma2-Gutenberg-Doppel-9B-Q4_K_M-GGUF

This model was converted to GGUF format from nbeerbower/Gemma2-Gutenberg-Doppel-9B 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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Model details:

Gemma2-Gutenberg-Doppel-9B

UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3 finetuned on jondurbin/gutenberg-dpo-v0.1 and nbeerbower/gutenberg2-dpo. Method

ORPO finetuned using 2x A40 for 3 epochs. Open LLM Leaderboard Evaluation Results

Detailed results can be found here Metric Value Avg. 29.82 IFEval (0-Shot) 71.71 BBH (3-Shot) 41.08 MATH Lvl 5 (4-Shot) 3.47 GPQA (0-shot) 10.63 MuSR (0-shot) 17.30 MMLU-PRO (5-shot) 34.75

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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/Gemma2-Gutenberg-Doppel-9B-Q4_K_M-GGUF --hf-file gemma2-gutenberg-doppel-9b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Gemma2-Gutenberg-Doppel-9B-Q4_K_M-GGUF --hf-file gemma2-gutenberg-doppel-9b-q4_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/Gemma2-Gutenberg-Doppel-9B-Q4_K_M-GGUF --hf-file gemma2-gutenberg-doppel-9b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Gemma2-Gutenberg-Doppel-9B-Q4_K_M-GGUF --hf-file gemma2-gutenberg-doppel-9b-q4_k_m.gguf -c 2048