Instructions to use greenpolo/langslice-gemma-4-E4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use greenpolo/langslice-gemma-4-E4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="greenpolo/langslice-gemma-4-E4B")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("greenpolo/langslice-gemma-4-E4B")
model = AutoModelForImageTextToText.from_pretrained("greenpolo/langslice-gemma-4-E4B")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use greenpolo/langslice-gemma-4-E4B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="greenpolo/langslice-gemma-4-E4B",
	filename="gguf/langslice-gemma-4-E4B-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

llama.cpp

How to use greenpolo/langslice-gemma-4-E4B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf greenpolo/langslice-gemma-4-E4B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf greenpolo/langslice-gemma-4-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 greenpolo/langslice-gemma-4-E4B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf greenpolo/langslice-gemma-4-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 greenpolo/langslice-gemma-4-E4B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf greenpolo/langslice-gemma-4-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 greenpolo/langslice-gemma-4-E4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf greenpolo/langslice-gemma-4-E4B:Q4_K_M

Use Docker

docker model run hf.co/greenpolo/langslice-gemma-4-E4B:Q4_K_M

LM Studio
Jan

vLLM

How to use greenpolo/langslice-gemma-4-E4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "greenpolo/langslice-gemma-4-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": "greenpolo/langslice-gemma-4-E4B",
		"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/greenpolo/langslice-gemma-4-E4B:Q4_K_M

SGLang

How to use greenpolo/langslice-gemma-4-E4B 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 "greenpolo/langslice-gemma-4-E4B" \
    --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": "greenpolo/langslice-gemma-4-E4B",
		"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 "greenpolo/langslice-gemma-4-E4B" \
        --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": "greenpolo/langslice-gemma-4-E4B",
		"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 greenpolo/langslice-gemma-4-E4B with Ollama:
```
ollama run hf.co/greenpolo/langslice-gemma-4-E4B:Q4_K_M
```

Unsloth Studio new

How to use greenpolo/langslice-gemma-4-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 greenpolo/langslice-gemma-4-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 greenpolo/langslice-gemma-4-E4B to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use greenpolo/langslice-gemma-4-E4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf greenpolo/langslice-gemma-4-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": "greenpolo/langslice-gemma-4-E4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use greenpolo/langslice-gemma-4-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 greenpolo/langslice-gemma-4-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 greenpolo/langslice-gemma-4-E4B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use greenpolo/langslice-gemma-4-E4B with Docker Model Runner:
```
docker model run hf.co/greenpolo/langslice-gemma-4-E4B:Q4_K_M
```

Lemonade

How to use greenpolo/langslice-gemma-4-E4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull greenpolo/langslice-gemma-4-E4B:Q4_K_M

Run and chat with the model

lemonade run user.langslice-gemma-4-E4B-Q4_K_M

List all available models

lemonade list

LangSlice-Gemma-4-E4B v1.0

A Gemma 4 E4B-IT fine-tune that estimates the anterior-posterior (AP) coordinate of a histological brain section in the matching mouse coronal atlas. Trained on adult (Allen mouse 25 µm) and developmental (DeMBA P9–P35, ADMBA P4–P56) coronal sections.

Model	Mean abs. error (mm)	Mean error (% of 13.2 mm AP axis)
Base unsloth/gemma-4-E4B-it	2.375	18.02 %
LangSlice-Gemma-4-E4B v1.0	1.228	9.32 %
Gemini 3 Flash Preview	0.429	3.25 %

SliceBench small coronal, mouse subset (n = 112).

Serve via vLLM

vllm serve greenpolo/langslice-gemma-4-E4B \
  --max-model-len 16384 \
  --enable-auto-tool-choice \
  --tool-call-parser gemma4 \
  --reasoning-parser gemma4

GGUF (Q4_K_M, Q8_0, BF16) under gguf/ for use with llama.cpp llama-mtmd-cli (pair with the bundled mmproj-BF16.gguf for image input).

The model is intentionally scoped to single-slice mouse coronal AP estimation. Sagittal, horizontal, and rat performance are much weaker. Inference must use the bundled chat template.

Full agent loop, multi-slice estimation, and end-to-end registration: LangSlice harness.

License

Gemma 4 Terms of Use governs the weights.

Citation

A Zenodo DOI is forthcoming. Until it's minted, please cite this Hugging Face repository directly:

Baughman, N. and Walsh Lab. LangSlice-Gemma-4-E4B v1.0. Hugging Face, 2026. https://huggingface.co/greenpolo/langslice-gemma-4-E4B

Downloads last month: 409

Safetensors

Model size

8B params

Tensor type

F32

BF16

Model tree for greenpolo/langslice-gemma-4-E4B

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

unsloth/gemma-4-E4B-it

Adapter

(31)

this model

Evaluation results

MAE in millimetres on the adult Allen mouse 13.2 mm AP axis on SliceBench small coronal (mouse subset, n=112)
self-reported

1.228
Mean error (% of plane extent) on SliceBench small coronal (mouse subset, n=112)
self-reported

9.320