Triangle104/Yomiel-22B-Q5_K_M-GGUF

This model was converted to GGUF format from Silvelter/Yomiel-22B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Merge Method

This model was merged using the della_linear merge method using ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1 as a base.

Models Merged

The following models were included in the merge:

nbeerbower/Mistral-Small-Drummer-22B
gghfez/SeminalRP-22b
TheDrummer/Cydonia-22B-v1.1
anthracite-org/magnum-v4-22b

Configuration

The following YAML configuration was used to produce this model:

base_model: ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1 parameters: epsilon: 0.04 lambda: 1.05 int8_mask: true rescale: true normalize: false dtype: bfloat16 tokenizer_source: base merge_method: della_linear models:

• model: ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1 parameters: weight: [0.2, 0.3, 0.2, 0.3, 0.2] density: [0.45, 0.55, 0.45, 0.55, 0.45]
• model: gghfez/SeminalRP-22b parameters: weight: [0.01768, -0.01675, 0.01285, -0.01696, 0.01421] density: [0.6, 0.4, 0.5, 0.4, 0.6]
• model: anthracite-org/magnum-v4-22b parameters: weight: [0.208, 0.139, 0.139, 0.139, 0.208] density: [0.7]
• model: TheDrummer/Cydonia-22B-v1.1 parameters: weight: [0.208, 0.139, 0.139, 0.139, 0.208] density: [0.7]
• model: nbeerbower/Mistral-Small-Drummer-22B parameters: weight: [0.33] density: [0.45, 0.55, 0.45, 0.55, 0.45]

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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/Yomiel-22B-Q5_K_M-GGUF --hf-file yomiel-22b-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Yomiel-22B-Q5_K_M-GGUF --hf-file yomiel-22b-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/Yomiel-22B-Q5_K_M-GGUF --hf-file yomiel-22b-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Yomiel-22B-Q5_K_M-GGUF --hf-file yomiel-22b-q5_k_m.gguf -c 2048