Instructions to use KikoCis/gemma-4-26B-A4B-it-IQ2_M-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 KikoCis/gemma-4-26B-A4B-it-IQ2_M-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 KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_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 KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_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 KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M

Use Docker

docker model run hf.co/KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M

LM Studio
Jan

vLLM

How to use KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KikoCis/gemma-4-26B-A4B-it-IQ2_M-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": "KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M

Ollama
How to use KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF with Ollama:
```
ollama run hf.co/KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M
```

Unsloth Studio

How to use KikoCis/gemma-4-26B-A4B-it-IQ2_M-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 KikoCis/gemma-4-26B-A4B-it-IQ2_M-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 KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF to start chatting

How to use KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_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": "KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use KikoCis/gemma-4-26B-A4B-it-IQ2_M-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 KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_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 KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_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 "KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_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"

Docker Model Runner
How to use KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF with Docker Model Runner:
```
docker model run hf.co/KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M
```

Lemonade

How to use KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF:IQ2_M

Run and chat with the model

lemonade run user.gemma-4-26B-A4B-it-IQ2_M-GGUF-IQ2_M

List all available models

lemonade list

Gemma 4 26B-A4B IT — IQ2_M (domain imatrix)

Custom IQ2_M quantization of google/gemma-4-26B-A4B-it with a domain-mixed importance matrix (45% code, 45% agentic, 10% general).

Size: 9.7 GB (smaller than the 31B dense at 10.4 GB)
BPW: 3.29
Architecture: Gemma 4 MoE — 128 experts, 8+1 active per token, 25.2B total / 3.8B active

Benchmark results (reasoning OFF)

Note on BFCL scores: All BFCL scores reported here use our internal simplified evaluation (single-function-call subset with custom prompt/scoring), NOT the official Berkeley Function Calling Leaderboard methodology. Our scores are not directly comparable to the official leaderboard. We are working on running the official BFCL evaluation for comparable numbers.

Model	Size	Active	HumanEval+	MBPP+	BFCL v3	NL2Bash F1
Gemma 4 31B IQ2_M	10.4 GB	31B	88.41%	82.01%	92.25%	84.71%
This model (26B MoE IQ2_M)	9.7 GB	3.8B	80.49%	77.78%	92.75%	79.64%
Qwen3.6 IQ2_M	11.1 GB	3B	80.49%	78.31%	94.75%	81.63%
Gemma 4 E4B Q8_0	7.8 GB	4.5B	73.78%	73.28%	93.75%	79.75%

Key findings

BFCL 92.75% — beats the 31B dense (92.25%) with 0.7 GB less and 8x fewer active params
HumanEval+ 80.49% — ties Qwen3.6 IQ2_M at comparable size
MoE expert isolation provides natural quantization resilience, as predicted by our specialization boundary study
All benchmarks run with reasoning OFF; thinking mode expected to add +5-15pt on code tasks

Quickstart

huggingface-cli download KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF gemma4-26b-a4b-IQ2_M.gguf --local-dir .

# Without thinking
llama-cli -m gemma4-26b-a4b-IQ2_M.gguf -ngl 99 --ctx-size 8192 --temp 0.1 \
  -p "Write a Python function to merge two sorted lists"

# With thinking (recommended for complex tasks)
llama-cli -m gemma4-26b-a4b-IQ2_M.gguf -cnv -ngl 99 --ctx-size 8192 \
  --reasoning on --reasoning-budget 1024

Files

gemma4-26b-a4b-IQ2_M.gguf — quantized weights (9.7 GB)
gemma4-26b-a4b-domain.imatrix — importance matrix

License

Apache 2.0.

Real-World Agent Test Warning (April 2026)

Benchmark scores do not predict agent capability. In Docker-based autonomous testing, fine-tuned E4B models (95% BFCL) scored 0/10 while the unfine-tuned base scored 6/10. Fine-tuning for BFCL destroyed general reasoning (error recovery, strategy adaptation, anti-repetition). Fine-tuned E4B models have been withdrawn.

For autonomous agent tasks, use the base Gemma 4 model or a larger model at higher BPW. See: The Benchmark Trap — Full Study

Downloads last month: 52

GGUF

Model size

25B params

Architecture

gemma4

Hardware compatibility

2-bit

Model tree for KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Quantized

(331)

this model

KikoCis
/

gemma-4-26B-A4B-it-IQ2_M-GGUF

Gemma 4 26B-A4B IT — IQ2_M (domain imatrix)

Benchmark results (reasoning OFF)

Key findings

Quickstart

Files

Related

License

Real-World Agent Test Warning (April 2026)

Model tree for KikoCis/gemma-4-26B-A4B-it-IQ2_M-GGUF