Instructions to use batiai/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 batiai/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 batiai/gemma-4-31B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/gemma-4-31B-it-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf batiai/gemma-4-31B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf batiai/gemma-4-31B-it-GGUF: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 batiai/gemma-4-31B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf batiai/gemma-4-31B-it-GGUF: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 batiai/gemma-4-31B-it-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/gemma-4-31B-it-GGUF:Q4_K_M

Use Docker

docker model run hf.co/batiai/gemma-4-31B-it-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use batiai/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 "batiai/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": "batiai/gemma-4-31B-it-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/batiai/gemma-4-31B-it-GGUF:Q4_K_M

Ollama
How to use batiai/gemma-4-31B-it-GGUF with Ollama:
```
ollama run hf.co/batiai/gemma-4-31B-it-GGUF:Q4_K_M
```
Unsloth Desktop

How to use batiai/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 batiai/gemma-4-31B-it-GGUF: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": "batiai/gemma-4-31B-it-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use batiai/gemma-4-31B-it-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/gemma-4-31B-it-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/gemma-4-31B-it-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-31B-it-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use batiai/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 batiai/gemma-4-31B-it-GGUF: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 batiai/gemma-4-31B-it-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use batiai/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 batiai/gemma-4-31B-it-GGUF: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 "batiai/gemma-4-31B-it-GGUF: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 31B-it GGUF — Quantized by BatiAI

Optimized GGUF quantizations of google/gemma-4-31B-it — Google's largest dense Gemma 4 model with native multilingual + vision capabilities. Built directly from official Google BF16 weights by BatiAI for BatiFlow.

Gemma 4 31B-it is a dense 31 B model (every parameter active per token) — different trade-off from 26B-A4B's MoE design (26 B total / 3.8 B active). Pick 31B for deeper reasoning per query, 26B-A4B for higher throughput.

Quick Start

# 24 GB Mac — Smaller, imatrix-calibrated
ollama pull batiai/gemma4-31b:iq3

# 24 GB+ Mac — Imatrix 4-bit (recommended)
ollama pull batiai/gemma4-31b:iq4

# 32 GB+ Mac — Higher quality
ollama pull batiai/gemma4-31b:q4

# 36 GB+ Mac — Highest on-device quality
ollama pull batiai/gemma4-31b:q6

Available Quantizations

Tag	Quant	Recommended For
`:iq3`	IQ3_M (imatrix)	24 GB Mac, smaller footprint
`:iq4`	IQ4_XS (imatrix)	24 GB+ Mac, recommended
`:q4`	Q4_K_M	32 GB+ Mac, higher quality
`:q6`	Q6_K	36 GB+ Mac, near-lossless

Two modes — text-only by default, multimodal opt-in

Upstream Gemma 4 31B-it is multimodal (text + image + video understanding — but no audio; for audio see the smaller E2B / E4B variants). In the GGUF ecosystem this is delivered as two files: a main model.gguf (text tower) and a separate mmproj.gguf (multi-modal projector — the vision tower). We ship both, separate, so you can pick:

	Text-only (default)	Multimodal (opt-in)
Files needed	main GGUF only	main GGUF + `mmproj-*.gguf`
Capabilities	Q&A, coding, tool calling, RAG, agents	+ image / video understanding (OCR, captioning, visual reasoning)
`ollama pull`	✅ single command	⚠ Ollama mmproj integration is still rough — use llama.cpp directly
Disk / RAM	smaller (no vision weights)	larger (+ ~770 MB to ~1.2 GB)
Recommended for	most users (chat, code, agents)	OCR, image understanding

Multimodal usage (llama.cpp)

Download the main GGUF + the mmproj file:

# Pick a main model (text tower)
wget https://huggingface.co/batiai/Gemma-4-31B-it-GGUF/resolve/main/google-gemma-4-31B-it-IQ4_XS.gguf

# Pick the mmproj (vision tower) — Q6_K is the sweet spot, BF16 if you want zero loss
wget https://huggingface.co/batiai/Gemma-4-31B-it-GGUF/resolve/main/mmproj-Q6_K.gguf

Server mode (OpenAI-compatible Vision API):

llama-server \
  -m google-gemma-4-31B-it-IQ4_XS.gguf \
  --mmproj mmproj-Q6_K.gguf \
  -c 32768 --host 127.0.0.1 --port 8080

curl http://127.0.0.1:8080/v1/chat/completions -d '{
  "messages": [{
    "role": "user",
    "content": [
      {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}},
      {"type": "text", "text": "What does this screenshot show?"}
    ]
  }]
}'

One-shot CLI:

llama-mtmd-cli \
  -m google-gemma-4-31B-it-IQ4_XS.gguf \
  --mmproj mmproj-Q6_K.gguf \
  --image ~/Desktop/photo.jpg \
  -p "describe this image"

mmproj quantizations available

File	Quant	Size	When to use
`mmproj-Q6_K.gguf`	Q6_K	~770 MB	balanced (recommended)
`mmproj-BF16.gguf`	BF16	~1.2 GB	absolute zero quantization loss for vision

(Q8_0 not available because some vision tensors have shapes incompatible with Q8_0's column alignment — applies to every quantizer of this model. Q6_K's K-quant block layout handles them.)

Dense vs MoE — when to pick 31B vs 26B-A4B?

	31B-it (dense)	26B-A4B-it (MoE)
Active params / token	31 B (all)	3.8 B (8 experts of 32)
Throughput	slower (more compute per token)	faster
Reasoning depth	better (denser computation)	good
Memory at IQ4	~17 GB	~13 GB
Best for	hard reasoning / single-thread workloads	high-throughput / agents / streaming chat

Use 31B for quality-per-token, 26B-A4B for tokens-per-second.

Why BatiAI?

	BatiAI	Third-party (unsloth, etc.)
Source	Quantized directly from official Google weights	Often re-quantized from other GGUFs
Compatibility	✅ Verified on Ollama 0.20+	❌ Known issues with Ollama 0.20+
Tested on	Real Mac mini M4 + MacBook Pro M4 Max	Often untested
Tool calling	✅ Verified with BatiFlow's 57 tool functions	Often broken
Korean	✅ Validated	Not tested
Multimodal	✅ Vision mmproj available (this repo)	Often missing
Signing	`general.author: BatiAI` for provenance	Varies

About BatiFlow

flow.bati.ai

BatiFlow is a macOS-native AI desktop automation app — just 5 MB, built with Swift.

Free & Unlimited — On-device AI via Ollama, no API costs
100 % Private — All data stays on your Mac
Ultra Lightweight — Native macOS app, only 5 MB
57 built-in tools — calendar, notes, reminders, files, email, browser, messaging, and more

Related models in the BatiAI Gemma 4 lineup

Model	Modalities	Min RAM	Repo
Gemma 4 E2B-it	text + image + audio	8 GB	batiai/Gemma-4-E2B-it-GGUF
Gemma 4 E4B-it	text + image + audio	8 GB	batiai/Gemma-4-E4B-it-GGUF
Gemma 4 26B-A4B-it	text + image / video (MoE)	24 GB	batiai/Gemma-4-26B-A4B-it-GGUF
Gemma 4 31B-it	text + image / video (dense)	24 GB	this repo

Technical Details

Original Model: google/gemma-4-31B-it
Architecture: Gemma 4 dense (31 B parameters, all active per token)
Modalities: Text (primary) + Image / Video via opt-in mmproj. Audio not supported (use E2B / E4B for audio)
Context Window: 128 K tokens
License: Gemma
Quantized with: llama.cpp
Quantized by: BatiAI
GGUF metadata: general.author: BatiAI, general.url: https://flow.bati.ai

License

Mirrors the upstream Gemma license. Commercial use permitted per Google's Gemma terms.

BatiAI's quantization pipeline is provided under MIT.

Downloads last month: 347

GGUF

Model size

31B params

Architecture

gemma4

Hardware compatibility

3-bit

4-bit

6-bit

Model tree for batiai/gemma-4-31B-it-GGUF

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Quantized

(312)

this model

Collection including batiai/gemma-4-31B-it-GGUF

🍎 Gemma 4 — Google's Latest

Collection

Gemma 4 quantizations from Google's official weights. Best entry for 16GB Mac mini M4 (E4B Q4 = 57 t/s). • 5 items • Updated 5 days ago