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---
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`](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-Q6_K-GGUF --hf-file rplament-22b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
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](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-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
```
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