Instructions to use unsloth/gemma-4-31B-it-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use unsloth/gemma-4-31B-it-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 unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

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 unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

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 unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/gemma-4-31B-it-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/gemma-4-31B-it-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": "unsloth/gemma-4-31B-it-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/unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

Ollama
How to use unsloth/gemma-4-31B-it-GGUF with Ollama:
```
ollama run hf.co/unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use unsloth/gemma-4-31B-it-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 unsloth/gemma-4-31B-it-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 unsloth/gemma-4-31B-it-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/gemma-4-31B-it-GGUF to start chatting

How to use unsloth/gemma-4-31B-it-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

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": "unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use unsloth/gemma-4-31B-it-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

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 "unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL" \
  --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 unsloth/gemma-4-31B-it-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/gemma-4-31B-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.gemma-4-31B-it-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use unsloth/gemma-4-31B-it-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 unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

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 unsloth/gemma-4-31B-it-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Atomic Chat

Token accuracy issues

by apex-3d - opened Apr 2

Discussion

apex-3d

Apr 2

Hello! Thanks for the quick release.

I'm getting a few weird quirks where the model incorrectly thinks certain words are misspelled when asked to proofread or give feedback on text content.

For example, I asked it to write me a story about a cat and a dog, and then fed that input back to it to proofread. It consistently generates responses like this:

1. Technical Corrections (Proofreading)
You have a few minor typos that likely happened during the flow of writing. Cleaning these up will make the prose seamless:

"attempt" → attempt
"pounce" → (Actually, you spelled this correctly, but check the surrounding flow).
"undeterred" → undeterred
"wagged" → wagged (This appears twice).
"vibrated" → vibrated
"Minerva" → Minerva (You switched the 'i' and 'e' in one instance toward the end).

Tested with UD-Q8_K_XL and UD-Q6_K_XL in llama.cpp b8637 and b8639.

Not sure if it's a problem with the quant, base model, or inference engine, but figured I'd open a discussion here to track the issue.

fizzacles

Apr 2

•

edited Apr 2

I'm not sure if something is wrong with the chat template. Having some issues with the model getting fixated on a singular token and repeating that forever, Q6_K_XL with "neutral" samplers.

quasar-of-mikus

Apr 2

•

edited Apr 2

bartowski's quants are broken too. Likely related: https://github.com/ggml-org/llama.cpp/issues/21321

@fizzacles Same here. Happens when formatting it manually per google's instructions.

apex-3d

Apr 2

•

edited Apr 3

Yup, confirmed bartowski's quants are also broken (even the full bf16 GGUF).

Haven't been able to compare with the raw base model yet since I was waiting for VLLM to merge support for this model. Will post here when I get a chance unless someone beats me to it.

EDIT: Was able to run the original model in BF16 successfully with VLLM v0.19.0, so this is a llama.cpp issue. Not sure if all the fixes have been merged in yet.

quasar-of-mikus

Apr 3

•

edited Apr 3

Try adding <bos> at the start of context manually. Seems to fix it for me (?)

Edit: No. It helped because I misconfigured the model. Also, https://github.com/ggml-org/llama.cpp/pull/21343

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