Instructions to use U2DIA/gemma4-particle-edu-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use U2DIA/gemma4-particle-edu-e4b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="U2DIA/gemma4-particle-edu-e4b",
	filename="gemma4-physics-edu-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use U2DIA/gemma4-particle-edu-e4b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf U2DIA/gemma4-particle-edu-e4b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf U2DIA/gemma4-particle-edu-e4b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf U2DIA/gemma4-particle-edu-e4b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf U2DIA/gemma4-particle-edu-e4b: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 U2DIA/gemma4-particle-edu-e4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf U2DIA/gemma4-particle-edu-e4b: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 U2DIA/gemma4-particle-edu-e4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf U2DIA/gemma4-particle-edu-e4b:Q4_K_M

Use Docker

docker model run hf.co/U2DIA/gemma4-particle-edu-e4b:Q4_K_M

LM Studio
Jan

vLLM

How to use U2DIA/gemma4-particle-edu-e4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "U2DIA/gemma4-particle-edu-e4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "U2DIA/gemma4-particle-edu-e4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/U2DIA/gemma4-particle-edu-e4b:Q4_K_M

Ollama
How to use U2DIA/gemma4-particle-edu-e4b with Ollama:
```
ollama run hf.co/U2DIA/gemma4-particle-edu-e4b:Q4_K_M
```

Unsloth Studio new

How to use U2DIA/gemma4-particle-edu-e4b 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 U2DIA/gemma4-particle-edu-e4b 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 U2DIA/gemma4-particle-edu-e4b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for U2DIA/gemma4-particle-edu-e4b to start chatting

Pi new

How to use U2DIA/gemma4-particle-edu-e4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf U2DIA/gemma4-particle-edu-e4b:Q4_K_M

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": "U2DIA/gemma4-particle-edu-e4b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use U2DIA/gemma4-particle-edu-e4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf U2DIA/gemma4-particle-edu-e4b: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 U2DIA/gemma4-particle-edu-e4b:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use U2DIA/gemma4-particle-edu-e4b with Docker Model Runner:
```
docker model run hf.co/U2DIA/gemma4-particle-edu-e4b:Q4_K_M
```

Lemonade

How to use U2DIA/gemma4-particle-edu-e4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull U2DIA/gemma4-particle-edu-e4b:Q4_K_M

Run and chat with the model

lemonade run user.gemma4-particle-edu-e4b-Q4_K_M

List all available models

lemonade list

Gemma 4 Particle Edu — E4B Fine-tuned (Q4_K_M GGUF)

Fine-tuned Gemma 4 E4B (4.5B active) for physics simulation parameter generation. Part of the Gemma 4 Particle Edu Kaggle Good Hackathon submission.

What this model does

Given a natural language physics scenario (e.g., "DNA double helix at body temperature"), this model outputs a JSON simulation specification with SI-unit physics parameters:

{
  "simulation": {
    "prompt": "dna",
    "title": "DNA Double Helix",
    "domain": "biology",
    "physics": {
      "gravity": 0,
      "damping": 0.99,
      "springStiffness": 30,
      "particleCount": 22000,
      "temperature": 310,
      "density": 1700
    }
  }
}

Training details

Method: Unsloth QLoRA (r=16)
Base: Gemma 4 E4B (4.5B active parameters)
Dataset: 907 Alpaca-format physics simulation pairs
Hardware: Lambda A10 (24GB)
Cost: $0.55
Quantization: llama.cpp Q4_K_M (CPU-only conversion)

Benchmark vs other Gemma 4 sizes

All 4 sizes fine-tuned on the same 907-pair dataset:

Model	Type	JSON parse	Physics	Time	Cost
Base Gemma 4 9B	Dense	30%	0%	12.7s	-
E4B FT (this model)	QLoRA r=16	70%	77%	8.9s	$0.55
Base Gemma 4 26B MoE	MoE	95%	22%	9.3s	-
26B FT	QLoRA r=8	90%	31%	9.3s	$2.40
Base Gemma 4 31B	Dense	100%	21%	20.6s	-
31B shallow FT	r=8, 1ep	100%	18%	21.1s	$2.55
31B deep FT	r=64, 3ep	100%	18%	20.0s	$2.55

Finding: E4B QLoRA is cost-optimal — $0.55 delivers +40%p JSON success and +77%p physics accuracy over the 9B base. Larger bases (26B/31B) already achieve 95-100% JSON parsing, so the 907-pair dataset cannot move them further.

How to use

Ollama (recommended)

# Pull this repo and register with Ollama
huggingface-cli download U2DIA/gemma4-particle-edu-e4b --local-dir ./gemma4-e4b
cd gemma4-e4b
ollama create gemma4-physics-edu -f Modelfile
ollama run gemma4-physics-edu

llama.cpp

./llama-cli -m gemma4-physics-edu-Q4_K_M.gguf -p "Simulate a DNA double helix"

Files

File	Size	Description
`gemma4-physics-edu-Q4_K_M.gguf`	5.3 GB	Merged Q4_K_M quantized weights
`config.json`	6 KB	Hugging Face model config
`tokenizer.json`	31 MB	Tokenizer
`Modelfile`	241 B	Ollama Modelfile

Related resources

GitHub: https://github.com/U2SY26/gemma4-particle-edu
Live Demo: https://gemma4-particle-edu.vercel.app
Kaggle Writeup: https://www.kaggle.com/competitions/gemma-4-good-hackathon/writeups/gemma-4-particle-edu-free-3d-physics-simulation-v
Kaggle Benchmark Dataset: https://www.kaggle.com/datasets/syu21125/gemma4-particle-edu-benchmark-300
Kaggle Ollama Live Demo: https://www.kaggle.com/code/syu21125/gemma-4-particle-edu-ollama-live-demo
3dweb (production app, 8,470 installs): https://play.google.com/store/apps/details?id=com.sciencelab.science_lab_flutter

Limitations

70% JSON parse rate means ~30% of outputs need retry or fallback
Physics accuracy was measured on 20 scenarios; full 300-scenario benchmark requires the 31B model
Fine-tuned on English prompts; Korean prompts fall back to the base model's multilingual capability
Not suitable for production medical, safety-critical, or regulatory-compliant simulations

Competition

Submitted to Kaggle Gemma 4 Good Hackathon (2026-05-18 deadline).

Tracks: Impact (Education) + Special Technology (Ollama + Unsloth)

Citation

@misc{gemma4-particle-edu-e4b,
  author = {Yun (U2DIA)},
  title = {Gemma 4 Particle Edu — E4B Fine-tuned},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/U2DIA/gemma4-particle-edu-e4b}
}

Downloads last month: 57

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

Model tree for U2DIA/gemma4-particle-edu-e4b

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Adapter

(339)

this model

Evaluation results

JSON parse rate (%)
self-reported

70.000
Physics accuracy (%)
self-reported

77.000