Instructions to use ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF",
	filename="ArliAI-RPMax-12B-v1.1-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 ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-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 ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-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 ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-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 ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF with Ollama:
```
ollama run hf.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-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 ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-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 ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF to start chatting

Pi new

How to use ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-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": "ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-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 ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-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 ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF with Docker Model Runner:
```
docker model run hf.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
```

Lemonade

How to use ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF-Q4_K_M

List all available models

lemonade list

ArliAI-RPMax-12B-v1.1

=====================================

RPMax Series Overview

| 2B | 3.8B | 8B | 9B | 12B | 20B | 22B | 70B |

RPMax is a series of models that are trained on a diverse set of curated creative writing and RP datasets with a focus on variety and deduplication. This model is designed to be highly creative and non-repetitive by making sure no two entries in the dataset have repeated characters or situations, which makes sure the model does not latch on to a certain personality and be capable of understanding and acting appropriately to any characters or situations.

Early tests by users mentioned that these models does not feel like any other RP models, having a different style and generally doesn't feel in-bred.

You can access the model at https://arliai.com and ask questions at https://www.reddit.com/r/ArliAI/

We also have a models ranking page at https://www.arliai.com/models-ranking

Ask questions in our new Discord Server! https://discord.com/invite/t75KbPgwhk

Model Description

ArliAI-RPMax-12B-v1.1 is a variant based on Mistral Nemo 12B Instruct 2407.

This is arguably the most successful RPMax model due to how Mistral is already very uncensored in the first place.

Training Details

Sequence Length: 8192
Training Duration: Approximately 2 days on 2x3090Ti
Epochs: 1 epoch training for minimized repetition sickness
QLORA: 64-rank 128-alpha, resulting in ~2% trainable weights
Learning Rate: 0.00001
Gradient accumulation: Very low 32 for better learning.

Quantization

The model is available in quantized formats:

FP16: https://huggingface.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1
GPTQ_Q4: https://huggingface.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GPTQ_Q4
GPTQ_Q8: https://huggingface.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GPTQ_Q8
GGUF: https://huggingface.co/ArliAI/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-GGUF

Suggested Prompt Format

Mistral Instruct Prompt Format

Downloads last month: 4,511

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

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