Instructions to use QuantFactory/MN-Violet-Lotus-12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/MN-Violet-Lotus-12B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/MN-Violet-Lotus-12B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/MN-Violet-Lotus-12B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/MN-Violet-Lotus-12B-GGUF",
	filename="MN-Violet-Lotus-12B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/MN-Violet-Lotus-12B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/MN-Violet-Lotus-12B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/MN-Violet-Lotus-12B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/MN-Violet-Lotus-12B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/MN-Violet-Lotus-12B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/MN-Violet-Lotus-12B-GGUF to start chatting

Pi new

How to use QuantFactory/MN-Violet-Lotus-12B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/MN-Violet-Lotus-12B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/MN-Violet-Lotus-12B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/MN-Violet-Lotus-12B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/MN-Violet-Lotus-12B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MN-Violet-Lotus-12B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/MN-Violet-Lotus-12B-GGUF

This is quantized version of FallenMerick/MN-Violet-Lotus-12B created using llama.cpp

Original Model Card

MN-Violet-Lotus-12B

This is the model I was trying to create when Chunky-Lotus emerged. Not only does this model score higher on my local EQ benchmarks (80.00 w/ 100% parsed @ 8-bit), but it does an even better job at roleplaying and producing creative outputs while still adhering to wide ranges of character personalities. The high levels of emotional intelligence are really quite noticeable as well.

Once again, models tend to score higher on my local tests when compared to their posted scores, but this has become the new high score for models I've personally tested.

I really like the way this model writes, and I hope you'll enjoy using it as well!

GGUF Quants:

Recommended ST Settings

Special thanks to @Zeldazachman for these amazing ST settings that I now wholeheartedly recommend!

Merge Details

This is a merge of pre-trained language models created using mergekit.

Merge Method

This model was merged using the Model Stock merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: FallenMerick/MN-Twilight-Maid-SLERP-12B #(unreleased)
  - model: Sao10K/MN-12B-Lyra-v4
  - model: flammenai/Mahou-1.5-mistral-nemo-12B
merge_method: model_stock
base_model: mistralai/Mistral-Nemo-Instruct-2407
parameters:
  normalize: true
dtype: bfloat16

In this recipe, Violet Twilight and Lumimaid were first blended using the SLERP method to create a strong roleplaying foundation. Lyra v4 is then added to the mix for its great creativity and roleplaying performance, along with Mahou to once again curtail the outputs and prevent the resulting model from becoming too wordy. Model Stock was used for the final merge in order to really push the resulting weights in the proper direction while using Nemo Instruct as a strong anchor point.