Instructions to use prithivMLmods/gemma-4-E4B-it-F32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/gemma-4-E4B-it-F32-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/gemma-4-E4B-it-F32-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/gemma-4-E4B-it-F32-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/gemma-4-E4B-it-F32-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/gemma-4-E4B-it-F32-GGUF",
	filename="GGUF/gemma-4-E4B-it.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/gemma-4-E4B-it-F32-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 prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

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 prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

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 prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

Use Docker

docker model run hf.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

LM Studio
Jan

vLLM

How to use prithivMLmods/gemma-4-E4B-it-F32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/gemma-4-E4B-it-F32-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": "prithivMLmods/gemma-4-E4B-it-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

SGLang

How to use prithivMLmods/gemma-4-E4B-it-F32-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/gemma-4-E4B-it-F32-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/gemma-4-E4B-it-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/gemma-4-E4B-it-F32-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/gemma-4-E4B-it-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/gemma-4-E4B-it-F32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16
```

Unsloth Studio

How to use prithivMLmods/gemma-4-E4B-it-F32-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 prithivMLmods/gemma-4-E4B-it-F32-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 prithivMLmods/gemma-4-E4B-it-F32-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/gemma-4-E4B-it-F32-GGUF to start chatting

How to use prithivMLmods/gemma-4-E4B-it-F32-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

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": "prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/gemma-4-E4B-it-F32-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 prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

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 prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use prithivMLmods/gemma-4-E4B-it-F32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16
```

Lemonade

How to use prithivMLmods/gemma-4-E4B-it-F32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/gemma-4-E4B-it-F32-GGUF:BF16

Run and chat with the model

lemonade run user.gemma-4-E4B-it-F32-GGUF-BF16

List all available models

lemonade list

gemma-4-E4B-it-F32-GGUF

File size: 3,229 Bytes

---
license: apache-2.0
language:
- en
base_model:
- google/gemma-4-E4B-it
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- llama.cpp
---

# **gemma-4-E4B-it-F32-GGUF**

> Gemma-4-E4B-it from Google is a 4.5B effective parameter (8B total with Per-Layer Embeddings) multimodal dense model in the Gemma 4 family, optimized for edge deployment on laptops, high-end smartphones, and consumer GPUs with native support for text, images (variable aspect ratio/resolution), audio processing, and configurable thinking modes for step-by-step reasoning. Featuring 42 layers, 512-token sliding window, 128K context length, and 262K vocabulary, it delivers frontier-level performance in agentic workflows, multilingual OCR/handwriting recognition, document/PDF parsing, UI/screen analysis, chart interpretation, object detection with pointing, coding assistance, and low-latency speech-to-text understanding—rivaling models 10-20x larger while maintaining Google's production-grade safety alignments. The instruction-tuned variant excels at on-device autonomous agents via Android AICore/Qualcomm optimizations, with open weights enabling local-first inference (MediaTek/ARM CPUs, NVIDIA RTX) for privacy-focused applications like mobile IDEs, real-time document processing, and structured data extraction in resource-constrained environments.

## Quick start with llama.cpp

```
llama-server -hf prithivMLmods/gemma-4-E4B-it-F32-GGUF:F32
```

## Model Files

   File Name | Quant Type | File Size | File Link |
 | - | - | - | - |
 | gemma-4-E4B-it.BF16.gguf | BF16 | 15.1 GB | [Download](https://huggingface.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF/blob/main/GGUF/gemma-4-E4B-it.BF16.gguf) |
 | gemma-4-E4B-it.F16.gguf | F16 | 15.1 GB | [Download](https://huggingface.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF/blob/main/GGUF/gemma-4-E4B-it.F16.gguf) |
 | gemma-4-E4B-it.F32.gguf | F32 | 30.1 GB | [Download](https://huggingface.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF/blob/main/GGUF/gemma-4-E4B-it.F32.gguf) |
 | gemma-4-E4B-it.Q8_0.gguf | Q8_0 | 8.01 GB | [Download](https://huggingface.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF/blob/main/GGUF/gemma-4-E4B-it.Q8_0.gguf) |
 | gemma-4-E4B-it.mmproj-bf16.gguf | mmproj-bf16 | 992 MB | [Download](https://huggingface.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF/blob/main/GGUF/gemma-4-E4B-it.mmproj-bf16.gguf) |
 | gemma-4-E4B-it.mmproj-f16.gguf | mmproj-f16 | 992 MB | [Download](https://huggingface.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF/blob/main/GGUF/gemma-4-E4B-it.mmproj-f16.gguf) |
 | gemma-4-E4B-it.mmproj-f32.gguf | mmproj-f32 | 1.91 GB | [Download](https://huggingface.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF/blob/main/GGUF/gemma-4-E4B-it.mmproj-f32.gguf) |
 | gemma-4-E4B-it.mmproj-q8_0.gguf | mmproj-q8_0 | 560 MB | [Download](https://huggingface.co/prithivMLmods/gemma-4-E4B-it-F32-GGUF/blob/main/GGUF/gemma-4-E4B-it.mmproj-q8_0.gguf) |

## Quants Usage 

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)