--- base_model: SvdH/RPLament-22B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo license: other license_name: mrl license_link: https://mistral.ai/licenses/MRL-0.1.md --- # Triangle104/RPLament-22B-Q5_K_M-GGUF This model was converted to GGUF format from [`SvdH/RPLament-22B`](https://huggingface.co/SvdH/RPLament-22B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/SvdH/RPLament-22B) for more details on the model. --- Model details: - This is a merge of pre-trained language models created using mergekit. Merge Method - This model was merged using the DARE TIES merge method using ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1 as a base. Models Merged The following models were included in the merge: allura-org/MS-Meadowlark-22B Gryphe/Pantheon-RP-1.6.2-22b-Small rAIfle/Acolyte-22B anthracite-org/magnum-v4-22b Configuration - The following YAML configuration was used to produce this model: merge_method: dare_ties base_model: ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1 parameters: int8_mask: true dtype: bfloat16 models: - model: ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1 parameters: weight: 0.30 density: 0.78 - model: anthracite-org/magnum-v4-22b parameters: weight: 0.25 density: 0.66 - model: allura-org/MS-Meadowlark-22B parameters: weight: 0.20 density: 0.54 - model: rAIfle/Acolyte-22B parameters: weight: 0.15 density: 0.42 - model: Gryphe/Pantheon-RP-1.6.2-22b-Small parameters: weight: 0.10 density: 0.42 --- ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Triangle104/RPLament-22B-Q5_K_M-GGUF --hf-file rplament-22b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Triangle104/RPLament-22B-Q5_K_M-GGUF --hf-file rplament-22b-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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/RPLament-22B-Q5_K_M-GGUF --hf-file rplament-22b-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Triangle104/RPLament-22B-Q5_K_M-GGUF --hf-file rplament-22b-q5_k_m.gguf -c 2048 ```