Instructions to use unsloth/gemma-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-it-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-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 unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-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 unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M

Use Docker

docker model run hf.co/unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use unsloth/gemma-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-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-4-26B-A4B-it-GGUF:UD-Q4_K_M

Ollama
How to use unsloth/gemma-4-26B-A4B-it-GGUF with Ollama:
```
ollama run hf.co/unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
```
Unsloth Desktop

How to use unsloth/gemma-4-26B-A4B-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-4-26B-A4B-it-GGUF:UD-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": "unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use unsloth/gemma-4-26B-A4B-it-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M
```

Lemonade

How to use unsloth/gemma-4-26B-A4B-it-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-26B-A4B-it-GGUF-UD-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use unsloth/gemma-4-26B-A4B-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-4-26B-A4B-it-GGUF:UD-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 unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use unsloth/gemma-4-26B-A4B-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-4-26B-A4B-it-GGUF:UD-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 "unsloth/gemma-4-26B-A4B-it-GGUF:UD-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"

Gemma 4 seems to work best with high temperature for coding

#21

by Reverger - opened Apr 9

Discussion

Reverger

Apr 9

•

edited Apr 9

@danielhanchen
Just reposting these findings from reddit.
https://www.reddit.com/r/LocalLLaMA/comments/1sg8r4l/gemma_4_seems_to_work_best_with_high_temperature/

This is insane. I used temp 1.5 and they're passing the carwash test easily now.
Gemma 4 seems to work best with high temperature for coding
I've been playing with Gemma 4 31B for coding tasks since it came out and been genuinely impressed with how capable it is. With the benchmarks putting it a little behind Qwen3.5 I didn't have high expectations, but it's honestly been performing better with what I've thrown at it so far

This has all been at the recommended parameters (temp 1.0, top-k 65 and top-p 0.95). With the general consensus being that for coding tasks you want a lower temperature I began repeating some of my tests with lower values (0.8, 0.6 and 0.3) but found if anything each step down made it worse

I just tested it on 26B A3B and 31B.
This is insane. I used temp 1.5 and they're passing the carwash test easily now.

danielhanchen

Unsloth AI org Apr 9

Interesting thanks for sharing your findings!

mikelowski

Apr 9

Not my experience with Q4_K_M:

urtuuuu

Apr 9

gemma-4-26B-A4B-it-UD-IQ4_XS.gguf
temp 1, top-k 64, min_p 0

It depends on your logic:

**1. The Practical Logic:**
If you want to wash the **car**, you have to **drive the car** to the car wash. If you walk, you will arrive at the car wash, but your car will still be at home, dirty!

**2. The Efficiency Logic:**
50 meters is roughly 60 steps.
* **Driving:** You have to get in, start the engine, shift into gear, drive, brake, and park.
* **Walking:** You just start walking.

For a distance of only 50 meters, **walking is actually faster** in terms of total time spent, but since you need the vehicle to perform the task, **driving is the only option that works.**

**Verdict: Drive the car.**

mikelowski

Apr 9

The thing is you can ask it several times in new chat sessions and sometimes will get it right and sometimes will fail. Still pretty impressive, E4B consistency is to say walk.

KeyboardMasher

Apr 9

The order of samplers application matters a lot.
Everyone supplies parameters but nobody supplies correct sampler chain, trusting llama.cpp's default one.

The right way to do it:
--samplers "temperature;top_p;top_k" --temperature 1 --top_p 0.95 --top_k 64

mikelowski

Apr 9

Reverger

Apr 10

@mikelowski
Please share your parameters.

mikelowski

Apr 10

•

edited Apr 10

@mikelowski
Please share your parameters.

There you go:
"--n-gpu-layers", "999",
"-ot", ".ffn_.*_exps.=CPU",
"--batch-size", "512",
"--ubatch-size", "256",
"--flash-attn", "on",
"--threads", "16",
"--parallel", "1",
"--webui-mcp-proxy",
"--temp", "1.5",
"--top-p", "0.95",
"--top-k", "64",
"--ctx-size", "256000",
"-np", "1",
"--cache-type-k", "q8_0",
"--cache-type-v", "q8_0"

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment