Instructions to use oxalis-ink/shamrock-0-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oxalis-ink/shamrock-0-e4b with Transformers:

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

pipe = pipeline("text-generation", model="oxalis-ink/shamrock-0-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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("oxalis-ink/shamrock-0-e4b")
model = AutoModelForMultimodalLM.from_pretrained("oxalis-ink/shamrock-0-e4b", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use oxalis-ink/shamrock-0-e4b 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 oxalis-ink/shamrock-0-e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf oxalis-ink/shamrock-0-e4b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf oxalis-ink/shamrock-0-e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf oxalis-ink/shamrock-0-e4b:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf oxalis-ink/shamrock-0-e4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf oxalis-ink/shamrock-0-e4b:Q4_K_M

Use Docker

docker model run hf.co/oxalis-ink/shamrock-0-e4b:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use oxalis-ink/shamrock-0-e4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oxalis-ink/shamrock-0-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": "oxalis-ink/shamrock-0-e4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/oxalis-ink/shamrock-0-e4b:Q4_K_M

SGLang

How to use oxalis-ink/shamrock-0-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 "oxalis-ink/shamrock-0-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": "oxalis-ink/shamrock-0-e4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "oxalis-ink/shamrock-0-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": "oxalis-ink/shamrock-0-e4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use oxalis-ink/shamrock-0-e4b with Ollama:
```
ollama run hf.co/oxalis-ink/shamrock-0-e4b:Q4_K_M
```
Unsloth Desktop

How to use oxalis-ink/shamrock-0-e4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oxalis-ink/shamrock-0-e4b: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": "oxalis-ink/shamrock-0-e4b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use oxalis-ink/shamrock-0-e4b with Docker Model Runner:
```
docker model run hf.co/oxalis-ink/shamrock-0-e4b:Q4_K_M
```

Lemonade

How to use oxalis-ink/shamrock-0-e4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull oxalis-ink/shamrock-0-e4b:Q4_K_M

Run and chat with the model

lemonade run user.shamrock-0-e4b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use oxalis-ink/shamrock-0-e4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oxalis-ink/shamrock-0-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 oxalis-ink/shamrock-0-e4b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use oxalis-ink/shamrock-0-e4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oxalis-ink/shamrock-0-e4b: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 "oxalis-ink/shamrock-0-e4b: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"

shamrock-0-e4b

Gemma 4 E4B fine-tuned for the Oxalis desktop app's language-learning surfaces (camera translate, text translate, tutor, talk, dictionary) across Japanese, Korean, and Simplified Chinese. The E4B sibling of oxalis-ink/shamrock-0-e2b.

Human Note

As a fair warning... this fine-tune will perform poorly outside of the Oxalis app. Feel free to run it for funzies though :)

Layout

Path	Contents
repo root	Merged fp16 checkpoint
`gguf/`	`shamrock-0-e4b-q4_k_m.gguf` + `mmproj-shamrock-0-e4b-f16.gguf`, what the app's llama.cpp runtime loads
`MTP/`	Multi-token-prediction drafter for speculative decoding: `mtp-shamrock-0-e4b-Q8_0.gguf` (llama.cpp, serve with `--spec-type draft-mtp`) and the same head in HF format under `MTP/assistant/`

License

shamrock-0-e4b is licensed under CC BY-NC 4.0 (see LICENSE). You can use, share and adapt it for non-commercial purposes, including personal projects and research, as long as you give credit. Commercial use, such as selling the weights, serving them in a paid product or API, or bundling them into a commercial app, is not permitted. For commercial licensing, reach out via oxalis.ink.

When you share this model or anything built from it, credit it as:

shamrock-0-e4b by Oxalis (https://oxalis.ink), licensed under CC BY-NC 4.0

and say whether you changed it.

shamrock-0-e4b is a modified version of Gemma 4 E4B by Google DeepMind: its weights were fine-tuned and merged, then converted to GGUF with an added MTP drafter. Gemma 4 itself is licensed under the Apache License 2.0 (see LICENSE-GEMMA and the Gemma 4 license); the CC BY-NC 4.0 terms cover Oxalis's modifications. See NOTICE.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for oxalis-ink/shamrock-0-e4b

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Quantized

(427)

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