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Triangle104
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MN-12B-Mimicore-WhiteSnake-Q4_K_M-GGUF

Transformers
GGUF
mergekit
Merge
llama-cpp
gguf-my-repo
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MN-12B-Mimicore-WhiteSnake-Q4_K_M-GGUF
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---
license: cc-by-nc-4.0
base_model: DoppelReflEx/MN-12B-Mimicore-WhiteSnake
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---

# Triangle104/MN-12B-Mimicore-WhiteSnake-Q4_K_M-GGUF
This model was converted to GGUF format from [`DoppelReflEx/MN-12B-Mimicore-WhiteSnake`](https://huggingface.co/DoppelReflEx/MN-12B-Mimicore-WhiteSnake) 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/DoppelReflEx/MN-12B-Mimicore-WhiteSnake) for more details on the model.

---
Model details:
-
Better version of GreenSnake, not too much different in OpenLLM 
LeaderBoard scores. Merge with 
cgato/Nemo-12b-Humanize-KTO-Experimental-Latest so this model could 
perform 'human response'. 

This merge model is a gift for Lunar New Year, haha. Enjoy it.

Good for RP, ERP, Story Telling.

PS: It's don't have cgato/Nemo-12b-Humanize-KTO-Experimental-Latest Tokenization issue.

Update: Still have cgato/Nemo-12b-Humanize-KTO-Experimental-Latest 
Tokenization issue, but randomly occur in rare rate. If you are 
experiencing this issue, just press re-generate to reroll other 
message/response.

		Chat Format? ChatML of course!

		Models Merged

The following models were included in the merge:

cgato/Nemo-12b-Humanize-KTO-Experimental-Latest
DoppelReflEx/MN-12B-Mimicore-GreenSnake

		Configuration

The following YAML configuration was used to produce this model:

models:
 - model: cgato/Nemo-12b-Humanize-KTO-Experimental-Latest
   parameters:
     density: 0.9
     weight: 1
 - model: DoppelReflEx/MN-12B-Mimicore-GreenSnake
   parameters:
     density: 0.6
     weight: 0.8
merge_method: dare_ties
base_model: IntervitensInc/Mistral-Nemo-Base-2407-chatml
tokenizer_source: base

---
## 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/MN-12B-Mimicore-WhiteSnake-Q4_K_M-GGUF --hf-file mn-12b-mimicore-whitesnake-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/MN-12B-Mimicore-WhiteSnake-Q4_K_M-GGUF --hf-file mn-12b-mimicore-whitesnake-q4_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/MN-12B-Mimicore-WhiteSnake-Q4_K_M-GGUF --hf-file mn-12b-mimicore-whitesnake-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/MN-12B-Mimicore-WhiteSnake-Q4_K_M-GGUF --hf-file mn-12b-mimicore-whitesnake-q4_k_m.gguf -c 2048
```