Instructions to use helenk/gemma-4-E4B-finetune-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use helenk/gemma-4-E4B-finetune-GGUF 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 helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf helenk/gemma-4-E4B-finetune-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 helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf helenk/gemma-4-E4B-finetune-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 helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M

Use Docker

docker model run hf.co/helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use helenk/gemma-4-E4B-finetune-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "helenk/gemma-4-E4B-finetune-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "helenk/gemma-4-E4B-finetune-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M

Ollama
How to use helenk/gemma-4-E4B-finetune-GGUF with Ollama:
```
ollama run hf.co/helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use helenk/gemma-4-E4B-finetune-GGUF with Docker Model Runner:
```
docker model run hf.co/helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M
```

Lemonade

How to use helenk/gemma-4-E4B-finetune-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull helenk/gemma-4-E4B-finetune-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-E4B-finetune-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

gemma-4-E4B-finetune-GGUF / README.md

helenk

Add README

cd23c5b verified 5 months ago

1.13 kB

tags:
  - gguf
  - llama.cpp
  - unsloth
  - vision-language-model

gemma_4_finetune : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

For text only LLMs: llama-cli -hf helenk/gemma_4_finetune --jinja
For multimodal models: llama-mtmd-cli -hf helenk/gemma_4_finetune --jinja

Available Model files:

gemma-4-e4b-it.Q4_K_M.gguf
gemma-4-e4b-it.F16-mmproj.gguf

⚠️ Ollama Note for Vision Models

Important: Ollama currently does not support separate mmproj files for vision models.

To create an Ollama model from this vision model:

Place the Modelfile in the same directory as the finetuned bf16 merged model
Run: ollama create model_name -f ./Modelfile (Replace model_name with your desired name)

This will create a unified bf16 model that Ollama can use. This was trained 2x faster with Unsloth