README.md

metadata

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 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card 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)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

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:

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 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