---
library_name: transformers
tags:
- mergekit
- moe
- mixture of experts
- merge
- llama-cpp
- gguf-my-repo
base_model: DavidAU/L3-MOE-4X8B-Grand-Horror-25B
---

# Triangle104/L3-MOE-4X8B-Grand-Horror-25B-Q3_K_L-GGUF
This model was converted to GGUF format from [`DavidAU/L3-MOE-4X8B-Grand-Horror-25B`](https://huggingface.co/DavidAU/L3-MOE-4X8B-Grand-Horror-25B) 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/DavidAU/L3-MOE-4X8B-Grand-Horror-25B) for more details on the model.

---
It is a LLama3 model, max context of 8192 (or 32k+ with rope) using mixture of experts to combine Dark/Horror models models of 8B each into one massive powerhouse at 25B parameters (equal to 32B - 4 X 8 B).

This model's instruction following, and output generation for creative writing, prose, fiction and role play are exceptional.

It excels at description, dialog, imagery, metaphors, and prose - and shows great variations in sentence / paragraph size, length, and composition.

It is also not afraid, and will not pull its punches.

And it has a sense of humor too.

It can do horror just as easily as it can do romance.

Most notably dialog is very "un-ai" like, combined with prose (short, and terse at times).

(lots of different examples below, including 2, 3 and 4 experts and different genres)

And it is fast: 34 t/s (2 experts) on a low end 16GB card, Q3KS.

Double this speed for standard/mid-range video cards.

Model can be used also for all genres (examples below showing this).

This model has been designed to be relatively bullet proof and operates with all parameters, including temp settings from 0 to 5.

It is an extraordinary compressed model, with a very low perplexity level (lower than Meta Llama3 Instruct).

It is for any writing, fiction or roleplay activity.

It requires Llama3 template and/or "Command-R" template.

---
## 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/L3-MOE-4X8B-Grand-Horror-25B-Q3_K_L-GGUF --hf-file l3-moe-4x8b-grand-horror-25b-q3_k_l.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/L3-MOE-4X8B-Grand-Horror-25B-Q3_K_L-GGUF --hf-file l3-moe-4x8b-grand-horror-25b-q3_k_l.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/L3-MOE-4X8B-Grand-Horror-25B-Q3_K_L-GGUF --hf-file l3-moe-4x8b-grand-horror-25b-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/L3-MOE-4X8B-Grand-Horror-25B-Q3_K_L-GGUF --hf-file l3-moe-4x8b-grand-horror-25b-q3_k_l.gguf -c 2048
```