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-Q6_K-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-Q6_K-GGUF --hf-file rplament-22b-q6_k.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Triangle104/RPLament-22B-Q6_K-GGUF --hf-file rplament-22b-q6_k.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-Q6_K-GGUF --hf-file rplament-22b-q6_k.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Triangle104/RPLament-22B-Q6_K-GGUF --hf-file rplament-22b-q6_k.gguf -c 2048