Instructions to use sphaela/gemma-4-E2B-it-AutoRound-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 sphaela/gemma-4-E2B-it-AutoRound-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 sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sphaela/gemma-4-E2B-it-AutoRound-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 sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sphaela/gemma-4-E2B-it-AutoRound-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 sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sphaela/gemma-4-E2B-it-AutoRound-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 sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use sphaela/gemma-4-E2B-it-AutoRound-GGUF with Ollama:
```
ollama run hf.co/sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M
```
Unsloth Desktop

How to use sphaela/gemma-4-E2B-it-AutoRound-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sphaela/gemma-4-E2B-it-AutoRound-GGUF with Docker Model Runner:
```
docker model run hf.co/sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M
```

Lemonade

How to use sphaela/gemma-4-E2B-it-AutoRound-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-E2B-it-AutoRound-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use sphaela/gemma-4-E2B-it-AutoRound-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 sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M

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 sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sphaela/gemma-4-E2B-it-AutoRound-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M

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 "sphaela/gemma-4-E2B-it-AutoRound-GGUF:Q4_K_M" \
  --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"

gemma-4-E2B-it-AutoRound-GGUF / README.md

sphaela

Update README.md

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	---
	license: apache-2.0
	language:
	- multilingual
	base_model: google/gemma-4-E2B-it
	tags:
	- auto-round
	- intel
	- gguf
	- quantization
	- vlm
	---

	# Gemma-4-E2B-it GGUF (AutoRound Quantized)

	This repository contains GGUF quantized versions of [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it) created using Intel's [AutoRound](https://github.com/intel/auto-round) quantization method.

	## Quantization Details

	The models were quantized using various schemes provided by the `auto-round` tool. For better compatibility and smaller size, we provide unified multimodal projector (`mmproj`) files in F16, BF16, and F32 formats.

	### Files and Sizes

	\| File Name \| Quant Type \| Size \| Description \|
	\|-----------\|------------\|------\|-------------\|
	\| `gemma-4-E2B-it-Q2_K_S.gguf` \| Q2_K_S \| 2.7 GB \| Extremely high compression, significant quality loss. \|
	\| `gemma-4-E2B-it-Q2_K_MIXED.gguf` \| Q2_K_MIXED \| 3.8 GB \| Recommended high-compression option. Uses Q4 for KV cache with good quality. \|
	\| `gemma-4-E2B-it-Q3_K_S.gguf` \| Q3_K_S \| 2.9 GB \| Very high compression, notable quality loss. \|
	\| `gemma-4-E2B-it-Q3_K_M.gguf` \| Q3_K_M \| 2.9 GB \| Balanced 3-bit quantization. \|
	\| `gemma-4-E2B-it-Q3_K_L.gguf` \| Q3_K_L \| 2.9 GB \| High quality 3-bit quantization. \|
	\| `gemma-4-E2B-it-Q4_0.gguf` \| Q4_0 \| 3.2 GB \| Standard 4-bit quantization, good balance. \|
	\| `gemma-4-E2B-it-Q4_1.gguf` \| Q4_1 \| 3.3 GB \| Higher quality 4-bit quantization than Q4_0. \|
	\| `gemma-4-E2B-it-Q4_K_S.gguf` \| Q4_K_S \| 3.2 GB \| Small 4-bit K-quant, good efficiency. \|
	\| `gemma-4-E2B-it-Q4_K_M.gguf` \| Q4_K_M \| 3.2 GB \| Recommended 4-bit K-quant, excellent balance. \|
	\| `gemma-4-E2B-it-Q5_0.gguf` \| Q5_0 \| 3.4 GB \| Standard 5-bit quantization, very high quality. \|
	\| `gemma-4-E2B-it-Q5_1.gguf` \| Q5_1 \| 3.5 GB \| Higher quality 5-bit quantization than Q5_0. \|
	\| `gemma-4-E2B-it-Q5_K_S.gguf` \| Q5_K_S \| 3.4 GB \| Small 5-bit K-quant, very high quality. \|
	\| `gemma-4-E2B-it-Q5_K_M.gguf` \| Q5_K_M \| 3.4 GB \| Recommended 5-bit K-quant, near-lossless. \|
	\| `gemma-4-E2B-it-Q6_K.gguf` \| Q6_K \| 3.6 GB \| 6-bit K-quant, virtually indistinguishable from F16. \|
	\| `gemma-4-E2B-it-Q8_0.gguf` \| Q8_0 \| 4.7 GB \| 8-bit quantization, near-lossless. \|
	\| `mmproj-model-f16.gguf` \| F16 \| 940 MB \| Unified Projector in Float16 format. \|
	\| `mmproj-model-bf16.gguf` \| BF16 \| 942 MB \| Unified Projector in BFloat16 format. \|
	\| `mmproj-model-f32.gguf` \| F32 \| 1.8 GB \| Unified Projector in Float32 format. \|

	## Generate the Model

	The models were generated using Intel's AutoRound with the following command:

	```bash
	auto-round --model google/gemma-4-E2B-it --output_dir ./quantized/ --scheme <SCHEME> --iters 0
	```

	Note: To reproduce this quantization, you need the following Pull Request:
	- [Intel AutoRound PR #1655](https://github.com/intel/auto-round/pull/1655)
	- And the specific fix mentioned in [this comment](https://github.com/intel/auto-round/pull/1655#issuecomment-4259531072).

	## Usage with llama.cpp

	These models can be used with `llama.cpp`. For multimodal usage, you must specify the projector file:

	```bash
	./llama-cli -m gemma-4-E2B-it-Q4_K_M.gguf --mmproj mmproj-model-f16.gguf --image your_image.jpg -p "Describe this image."
	```

	## About AutoRound

	[AutoRound](https://github.com/intel/auto-round) is an advanced quantization technique from Intel that aims to minimize accuracy loss through automated rounding optimization.