Instructions to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF",
	filename="gemma-4-E4B-IQ4_XS.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 Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-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 Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

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 Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

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 Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

Use Docker

docker model run hf.co/Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-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": "Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

Ollama
How to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF with Ollama:
```
ollama run hf.co/Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS
```

Unsloth Studio

How to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-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 Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-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 Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF to start chatting

How to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF with Docker Model Runner:
```
docker model run hf.co/Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS
```

Lemonade

How to use Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Krasnopjorovs/gemma-4-E4B-Imatrix-IQ4_XS-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.gemma-4-E4B-Imatrix-IQ4_XS-GGUF-IQ4_XS

List all available models

lemonade list

gemma-4-E4B-Imatrix-IQ4_XS-GGUF / README.md

Krasnopjorovs

Update README.md

e6a1abc verified 3 months ago

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	---
	license: apache-2.0
	base_model: google/gemma-4-E4B-it
	language:
	- en
	- ru
	- de
	- fr
	- it
	- zh
	- ja
	- ko
	- ar
	pipeline_tag: text-generation
	tags:
	- gemma
	- gemma-4
	- gguf
	- imatrix
	- quantization
	- rag
	- llama-cpp
	---

	# 🧠 Gemma 4 (9B) - Imatrix Quantized (IQ4_XS)

	This is a highly optimized, heavily quantized 4-bit (IQ4_XS) GGUF version of Google's Gemma 4.
	The quantization was performed using a custom Importance Matrix (imatrix) to ensure maximum retention of the model's reasoning capabilities and its massive 262k vocabulary, while aggressively shrinking the file size.

	Optimized and compiled by Krasnopjorovs ([Artjoms](https://artjoms.ai)).

	## 🌍 Multilingual Capabilities
	Thanks to the preserved 262,144 token vocabulary and custom imatrix, this model exhibits exceptional multilingual logic.

	Supported languages include (but are strictly not limited to):
	English, Russian, German, French, Italian, Mandarin Chinese, Japanese, Korean, and Arabic.
	During testing, the model successfully performed highly technical translations involving complex alphabets and provided unprompted transliteration (Pinyin, Romaji) and literal translation breakdowns.

	## 📊 Model Specifications

	* Base Model: Google Gemma 4 (~8-9B parameters)
	* Quantization Format: IQ4_XS (GGUF)
	* Optimization: Custom Imatrix applied
	* File Size: ~4.71 GB
	* Context Size: 131,072 tokens (Native Training Context)
	* Vocabulary Size: 262,144 tokens
	* License: Apache 2.0

	## ⚡ Hardware & Performance

	This specific build is engineered for universal compatibility and maximum throughput. Because of its extremely lightweight footprint (~4.71 GB), it can run efficiently across a massive variety of hardware setups—from edge devices to enterprise AI clusters.

	Deployment Flexibility:
	* Universal GPU Support: Comfortably fits entirely into the VRAM of almost any modern consumer or workstation GPU (8GB+ capacity), ensuring zero offloading bottlenecks.
	* CPU/RAM Fallback: Runs highly efficiently even on CPU-only configurations, provided there is sufficient standard system memory (e.g., DDR4/DDR5 ECC).
	* Professional Workstations: Achieves ultra-high token generation speeds on enterprise-grade architectures and multi-GPU arrays.
	* RAG & Vector Search: Perfect for local Retrieval-Augmented Generation pipelines (e.g., processing massive vector databases) where blazing-fast inference and large context windows (up to 131k) are critical.

	## 🛠️ Usage with `llama.cpp`

	This model is fully compatible with `llama.cpp` and frontends like Open WebUI.

	CLI Example:
	```bash
	./main -m gemma-4-E4B-IQ4_XS.gguf -n 512 -c 8192 --color -i -p "Your prompt here"

	🤝 About the Builder
	This model was compiled to power local, secure AI hardware ecosystems. If you are looking for pre-configured, plug-and-play AI servers and workstations built specifically for running private LLMs, visit artjoms.ai.