Triangle104/MN-SlushoMix-Q8_0-GGUF

This model was converted to GGUF format from crestf411/MN-SlushoMix 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:

MN-Slush + NemoMix Unleashed.

models:

• model: slush-stage1 parameters: weight: 1 density: 1
• model: slush-stage2 parameters: weight: 0.7 density: 1
• model: MarinaraSpaghetti/NemoMix-Unleashed-12B parameters: weight: 0.9 density: 1
• model: mistralai/Mistral-Nemo-Instruct-2407 parameters: weight: 1 density: 1 merge_method: ties base_model: mistralai/Mistral-Nemo-Base-2407 parameters: weight: 1 density: 1 normalize: true int8_mask: true tokenizer_source: mistralai/Mistral-Nemo-Instruct-2407 dtype: bfloat16

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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/MN-SlushoMix-Q8_0-GGUF --hf-file mn-slushomix-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/MN-SlushoMix-Q8_0-GGUF --hf-file mn-slushomix-q8_0.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/MN-SlushoMix-Q8_0-GGUF --hf-file mn-slushomix-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/MN-SlushoMix-Q8_0-GGUF --hf-file mn-slushomix-q8_0.gguf -c 2048