Triangle104/Flammades-Mistral-Nemo-12B-Q4_K_M-GGUF

This model was converted to GGUF format from flammenai/Flammades-Mistral-Nemo-12B 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:

nbeerbower/Mistral-Nemo-Gutenberg-Doppel-12B-v2 finetuned on flammenai/Date-DPO-NoAsterisks and jondurbin/truthy-dpo-v0.1. Method

ORPO tuned with 2x RTX 3090 for 3 epochs. Open LLM Leaderboard Evaluation Results

Detailed results can be found here Metric Value Avg. 22.34 IFEval (0-Shot) 38.42 BBH (3-Shot) 32.39 MATH Lvl 5 (4-Shot) 6.19 GPQA (0-shot) 7.16 MuSR (0-shot) 20.31 MMLU-PRO (5-shot) 29.57

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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/Flammades-Mistral-Nemo-12B-Q4_K_M-GGUF --hf-file flammades-mistral-nemo-12b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Flammades-Mistral-Nemo-12B-Q4_K_M-GGUF --hf-file flammades-mistral-nemo-12b-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/Flammades-Mistral-Nemo-12B-Q4_K_M-GGUF --hf-file flammades-mistral-nemo-12b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Flammades-Mistral-Nemo-12B-Q4_K_M-GGUF --hf-file flammades-mistral-nemo-12b-q4_k_m.gguf -c 2048