Instructions to use yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S",
	filename="gemma-4-E2B-it-UD-Q8_K_XL-tq4_1s.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S 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 yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1
# Run inference directly in the terminal:
llama cli -hf yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1
# Run inference directly in the terminal:
llama cli -hf yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

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 yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1
# Run inference directly in the terminal:
./llama-cli -hf yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

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 yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

Use Docker

docker model run hf.co/yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

LM Studio
Jan
Ollama
How to use yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S with Ollama:
```
ollama run hf.co/yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1
```

Unsloth Studio

How to use yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S 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 yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S 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 yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S to start chatting

How to use yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

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": "yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

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 yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

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 "yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1" \
  --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 yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S with Docker Model Runner:
```
docker model run hf.co/yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1
```

Lemonade

How to use yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S:Q4_1

Run and chat with the model

lemonade run user.gemma-4-E2B-it-GGUF-TQ4_1S-Q4_1

List all available models

lemonade list

yosoyalguien/gemma-4-E2B-it-GGUF-TQ4_1S

TurboQuant (TQ4_1S) quantization of unsloth/gemma-4-E2B-it-GGUF.

Details

Field	Value
Parent model	unsloth/gemma-4-E2B-it-GGUF
Quantization type	TQ4_1S
Quantization tool	turboquant-plus-tqp-v0.2.0
File	`gemma-4-E2B-it-UD-Q8_K_XL-tq4_1s.gguf`

Usage

Use with llama.cpp (TurboQuant fork) or any GGUF-compatible runtime that supports the TQ4_1S type.

llama-server -m gemma-4-E2B-it-UD-Q8_K_XL-tq4_1s.gguf --port 8080

Disclaimer

This model was quantized using TurboQuant, an experimental KV cache compression and quantization method. Quality may differ from the parent model. Refer to the parent model for licensing and usage terms.