Instructions to use OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OwenArli/Gemma-2-2B-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="OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF",
	filename="Gemma-2-2B-ArliAI-RPMax-v1.1-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use OwenArli/Gemma-2-2B-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 OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OwenArli/Gemma-2-2B-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 OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OwenArli/Gemma-2-2B-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 OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OwenArli/Gemma-2-2B-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 OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF with Ollama:
```
ollama run hf.co/OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use OwenArli/Gemma-2-2B-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 OwenArli/Gemma-2-2B-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 OwenArli/Gemma-2-2B-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 OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF to start chatting

Docker Model Runner
How to use OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF with Docker Model Runner:
```
docker model run hf.co/OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M
```

Lemonade

How to use OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OwenArli/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF-Q4_K_M

List all available models

lemonade list

Gemma-2-2B-ArliAI-RPMax-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 models 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

Gemma-2-2B-ArliAI-RPMax-v1.1 is a variant based on gemma-22b-it.

Training Details

Sequence Length: 4096
Training Duration: Approximately less than 1 day 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/Gemma-2-2B-ArliAI-RPMax-v1.1
GPTQ_Q4: https://huggingface.co/ArliAI/Gemma-2-2B-ArliAI-RPMax-v1.1-GPTQ_Q4
GPTQ_Q8: https://huggingface.co/ArliAI/Gemma-2-2B-ArliAI-RPMax-v1.1-GPTQ_Q8
GGUF: https://huggingface.co/ArliAI/Gemma-2-2B-ArliAI-RPMax-v1.1-GGUF

Suggested Prompt Format

Gemma Instruct Prompt Format

Since Gemma does not have system prompts, put the character descriptions in the first turn like on Mistral models.

It is trained with <instructions> and <end_of_instructions> that enclose the system prompt in the first user message.

<bos><start_of_turn>user
<instructions>You are a (character description)<end_of_instructions>\n\nHello!<end_of_turn>
<start_of_turn>model

Downloads last month: 70

GGUF

Model size

3B params

Architecture

gemma2

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

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