Instructions to use timothydillan/gemma4-e2b-balinese-mt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timothydillan/gemma4-e2b-balinese-mt with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="timothydillan/gemma4-e2b-balinese-mt",
	filename="gemma4-e2b-idban-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "\"Меня зовут Вольфганг и я живу в Берлине\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use timothydillan/gemma4-e2b-balinese-mt 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 timothydillan/gemma4-e2b-balinese-mt:Q8_0
# Run inference directly in the terminal:
llama cli -hf timothydillan/gemma4-e2b-balinese-mt:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf timothydillan/gemma4-e2b-balinese-mt:Q8_0
# Run inference directly in the terminal:
llama cli -hf timothydillan/gemma4-e2b-balinese-mt:Q8_0

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 timothydillan/gemma4-e2b-balinese-mt:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf timothydillan/gemma4-e2b-balinese-mt:Q8_0

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 timothydillan/gemma4-e2b-balinese-mt:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf timothydillan/gemma4-e2b-balinese-mt:Q8_0

Use Docker

docker model run hf.co/timothydillan/gemma4-e2b-balinese-mt:Q8_0

LM Studio
Jan
Ollama
How to use timothydillan/gemma4-e2b-balinese-mt with Ollama:
```
ollama run hf.co/timothydillan/gemma4-e2b-balinese-mt:Q8_0
```

Unsloth Studio

How to use timothydillan/gemma4-e2b-balinese-mt 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 timothydillan/gemma4-e2b-balinese-mt 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 timothydillan/gemma4-e2b-balinese-mt to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for timothydillan/gemma4-e2b-balinese-mt to start chatting

Atomic Chat new
Docker Model Runner
How to use timothydillan/gemma4-e2b-balinese-mt with Docker Model Runner:
```
docker model run hf.co/timothydillan/gemma4-e2b-balinese-mt:Q8_0
```

Lemonade

How to use timothydillan/gemma4-e2b-balinese-mt with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull timothydillan/gemma4-e2b-balinese-mt:Q8_0

Run and chat with the model

lemonade run user.gemma4-e2b-balinese-mt-Q8_0

List all available models

lemonade list

gemma4-e2b-balinese-mt / Modelfile

timothydillan

Upload Modelfile with huggingface_hub

29b3cf8 verified 12 days ago

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222 Bytes

	FROM ./gemma4-e2b-idban-Q8_0.gguf
	TEMPLATE """{{ if .System }}<\|turn>system
	{{ .System }}<turn\|>
	{{ end }}<\|turn>user
	{{ .Prompt }}<turn\|>
	<\|turn>model
	{{ .Response }}"""
	PARAMETER stop "<turn\|>"
	PARAMETER temperature 0.3

	FROM ./gemma4-e2b-idban-Q8_0.gguf
	TEMPLATE """{{ if .System }}<\|turn>system
	{{ .System }}<turn\|>
	{{ end }}<\|turn>user
	{{ .Prompt }}<turn\|>
	<\|turn>model
	{{ .Response }}"""
	PARAMETER stop "<turn\|>"
	PARAMETER temperature 0.3