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

Libraries

How to use AtomicChat/gemma-4-E4B-it-assistant-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AtomicChat/gemma-4-E4B-it-assistant-GGUF",
	filename="gemma-4-E4B-it-assistant.F16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use AtomicChat/gemma-4-E4B-it-assistant-GGUF with llama.cpp:

Install from brew

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

Use Docker

docker model run hf.co/AtomicChat/gemma-4-E4B-it-assistant-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use AtomicChat/gemma-4-E4B-it-assistant-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/gemma-4-E4B-it-assistant-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AtomicChat/gemma-4-E4B-it-assistant-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AtomicChat/gemma-4-E4B-it-assistant-GGUF:Q4_K_M

Ollama
How to use AtomicChat/gemma-4-E4B-it-assistant-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/gemma-4-E4B-it-assistant-GGUF:Q4_K_M
```

Unsloth Studio

How to use AtomicChat/gemma-4-E4B-it-assistant-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 AtomicChat/gemma-4-E4B-it-assistant-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 AtomicChat/gemma-4-E4B-it-assistant-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AtomicChat/gemma-4-E4B-it-assistant-GGUF to start chatting

Docker Model Runner
How to use AtomicChat/gemma-4-E4B-it-assistant-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/gemma-4-E4B-it-assistant-GGUF:Q4_K_M
```

Lemonade

How to use AtomicChat/gemma-4-E4B-it-assistant-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/gemma-4-E4B-it-assistant-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-E4B-it-assistant-GGUF-Q4_K_M

List all available models

lemonade list

How can I run mmproj + draft acceleration together on the model?

by VaLtEc-BoY - opened 23 days ago

Discussion

VaLtEc-BoY

23 days ago

Hello, how can I run mmproj + draft acceleration together on the model? For example, running GEMMA-4-E4B-gguf + draft assistant + mmproj (vision) + turbo quant Q3. Tks! I like the project atomic-llama-cpp-turboquant !!

Biogenic

Atomic Chat org 19 days ago

Hey, thanks for the kind words! 🙏

Unfortunately, mmproj + speculative decoding cannot run together — llama-server force-disables any draft pipeline as soon as a multimodal projector is loaded (image chunks expand to multiple KV tokens and the draft path doesn't handle them yet). This is an upstream llama.cpp limitation, not specific to this fork.

You can pick any two of three:

Vision + turbo3 KV — drop --mtp-head
Draft + turbo3 KV — drop --mmproj (this is what our scripts/run-gemma4-e4b-mtp-server.sh does)
We're putting mmproj + draft support on the roadmap and will look into it — it needs the draft pipeline to step over mtmd image chunks. Will share updates once we have something to test!

Biogenic

Atomic Chat org 19 days ago

Quick update — turned out simpler than expected, just merged it: https://github.com/AtomicBot-ai/atomic-llama-cpp-turboquant/pull/14

You can now load --mmproj together with --spec-type mtp / nextn / eagle3 on the same llama-server, so your GEMMA-4-E4B + assistant + mmproj + turbo3 combo works out of the box.

Small honest caveat: draft acceleration kicks in on text-only turns. When a turn actually contains an image, that turn falls back to plain target decoding (image still recognised correctly). Lifting that for vision turns is on the follow-up list.

Tested on M4 Max with Qwen 3.6 + NextN and Gemma 4 + MTP — both happy. Enjoy! 🙌

VaLtEc-BoY

19 days ago

Thank you very much for your reply, your project is very good. I've been using Kobold, but I need maximum efficiency. Turbo Quant will help me a lot... I believe that Llama, or any version of it, is important, especially because I don't want anything online, which is what I'm currently aiming for!! Offline agents and chats, your project is perfect for that!TKS!!

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