Instructions to use ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-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 ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-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 ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

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 ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

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 ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

Use Docker

docker model run hf.co/ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-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": "ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

Ollama
How to use ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF with Ollama:
```
ollama run hf.co/ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0
```
Unsloth Desktop

How to use ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

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": "ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF with Docker Model Runner:
```
docker model run hf.co/ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0
```

Lemonade

How to use ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

Run and chat with the model

lemonade run user.gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF-Q4_0

List all available models

lemonade list

Hermes Agent

How to use ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-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 ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

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 ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0

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 "ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF:Q4_0" \
  --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 12B IT QAT Assistant MTP Q4_0 GGUF

This repository contains a Q4_0 GGUF conversion of Google's official Gemma 4 12B IT QAT assistant / drafter checkpoint:

Source checkpoint: google/gemma-4-12B-it-qat-q4_0-unquantized-assistant
Intended target model: google/gemma-4-12B-it-qat-q4_0-gguf
Output file: gemma-4-12B-it-qat-assistant-MTP-Q4_0.gguf
Runtime: llama.cpp with Gemma 4 MTP / draft-mtp support

This is not a standalone chat model. It is a draft model for speculative decoding and must be loaded together with a matching Gemma 4 12B IT QAT target model.

Usage

llama-server \
  -hf google/gemma-4-12B-it-qat-q4_0-gguf:Q4_0 \
  --model-draft gemma-4-12B-it-qat-assistant-MTP-Q4_0.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 4

For multimodal use, keep the target model's matching mmproj enabled or pass it explicitly.

Conversion

Converted with ggml-org/llama.cpp tag b9553 / commit 9e3b928fd:

python3 convert_hf_to_gguf.py \
  gemma-4-12B-it-qat-q4_0-unquantized-assistant \
  --outfile gemma-4-12B-it-qat-assistant-MTP-BF16.gguf \
  --outtype bf16

llama-quantize \
  gemma-4-12B-it-qat-assistant-MTP-BF16.gguf \
  gemma-4-12B-it-qat-assistant-MTP-Q4_0.gguf \
  Q4_0

The quantization log reports:

BF16 model size: 806.57 MiB
Q4_0 quantized size: 292.90 MiB
Effective BPW: 5.81
token_embd.weight was quantized to Q6_K by llama.cpp's standard mostly-Q4_0 recipe.

Local Validation

Smoke-tested with llama.cpp b9553 on macOS / Apple Metal. The server loaded the Q4_0 draft model and initialized draft-mtp successfully:

loading draft model 'gemma-4-12B-it-qat-assistant-MTP-Q4_0.gguf'
common_speculative_impl_draft_mtp: adding speculative implementation 'draft-mtp'
srv load_model: speculative decoding context initialized

File SHA-256:

11d666fcab5284a4a333a4785f26dd72588855c07d7522331439f9aca7fee84e

Notes

This file is intended for benchmarking against the Q8_0 assistant drafter. Final output correctness is still verified by the target model during speculative decoding, but lower draft quantization can reduce acceptance rate or speed if the draft distribution diverges too much.

Downloads last month: 221

GGUF

Model size

0.4B params

Architecture

gemma4-assistant

Hardware compatibility

4-bit

Model tree for ysong21/gemma-4-12B-it-qat-assistant-MTP-Q4_0-GGUF

Base model

google/gemma-4-12B-it-assistant

Finetuned

google/gemma-4-12B-it-qat-q4_0-unquantized-assistant

Quantized

(21)

this model