Instructions to use Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2",
	filename="gemma-3-4b-it-roleplay-tuned-v2.BF16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2: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 Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2: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 Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M

Use Docker

docker model run hf.co/Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M

Ollama
How to use Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 with Ollama:
```
ollama run hf.co/Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M
```

Unsloth Studio

How to use Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 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 Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 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 Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 to start chatting

Atomic Chat new
Docker Model Runner
How to use Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 with Docker Model Runner:
```
docker model run hf.co/Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M
```

Lemonade

How to use Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2:Q4_K_M

Run and chat with the model

lemonade run user.gemma-3-4b-it-roleplay-tuned-v2-Q4_K_M

List all available models

lemonade list

What is it?

This is my further improvement of my previous gemma 3 4b it finetune, trained on way bigger amounts of unique data(17M tokens), which resulted in way better writing, bringing the model closer to how 8b and even 12b models write, at times.

Details:

Advantages:

Way better writing than the base model, less slop and enhanced creativity
keeps good track of the story
Small, runs fast
Better understanding of complex emotional topics

Disadvantages:

4b model has limited logic, this tune forces all of this logic to work in order to provide the best creative writing possible

A word on the quants for this model:

BF16- Mostly overkill, however, highest quality
Q8_0- Amazing quality, near lossless
Q6_K- High quality, fast
Q5_K_M- Mid to high quality, small and fast
Q4_K_M- Mid quality, very small and very fast

Quants are located in the repo, along with safetensors

What I will do next and is v3 possible?

Next I will probably turn my attention back to smaller models, like Qwen 3.5 2b and LFM 1.2b. I also may train a new version on an even bigger dataset, but it might result in overfitting, overall, this model is the peak performance for a 4b model, or at least the best I could do. working with it wasnt easy, but I did my best and feel satisfied with the results.

PS:(Honestly, I wasted too much time on that, there will soon be a WN9 in Limbus so I probably will finetune less)

Downloads last month: 409

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for Indexnusrefather/gemma-3-4b-it-roleplay-tuned-v2

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Adapter

(428)

this model

Adapters

2 models