Instructions to use ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF",
	filename="gemma-4-12B-it-MTP-ik_llama-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ji-farthing/gemma-4-12B-it-MTP-ik-llama-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 ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ji-farthing/gemma-4-12B-it-MTP-ik-llama-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 ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ji-farthing/gemma-4-12B-it-MTP-ik-llama-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 ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ji-farthing/gemma-4-12B-it-MTP-ik-llama-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 ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF with Ollama:
```
ollama run hf.co/ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF:Q4_K_M
```

Unsloth Studio

How to use ji-farthing/gemma-4-12B-it-MTP-ik-llama-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 ji-farthing/gemma-4-12B-it-MTP-ik-llama-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 ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF with Docker Model Runner:
```
docker model run hf.co/ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF:Q4_K_M
```

Lemonade

How to use ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-12B-it-MTP-ik-llama-GGUF-Q4_K_M

List all available models

lemonade list

Gemma 4 12B IT MTP Assistants for ik_llama

These are converted GGUF assistant/draft models for using Gemma 4 12B IT with ik_llama MTP speculative decoding.

They are not standalone chat models. Use one of these files as --model-draft next to the matching Gemma 4 12B IT target GGUF.

Files

gemma-4-12B-it-MTP-ik_llama-Q8_0.gguf
gemma-4-12B-it-MTP-ik_llama-Q4_K_M.gguf

Conversion Notes

Source assistant GGUF:

unsloth/gemma-4-12b-it-GGUF, MTP/gemma-4-12B-it-MTP-F16.gguf

The public assistant architecture string and tensor names were converted to ik_llama's gemma4_mtp schema. The unused public-assistant rope_freqs.weight tensor was omitted because ik_llama's Gemma 4 MTP assistant loader expects 48 tensors for this assistant.

Example

llama-server \
  -m /path/to/gemma-4-12b-it-IQ4_XS.gguf \
  --model-draft /path/to/gemma-4-12B-it-MTP-ik_llama-Q4_K_M.gguf \
  --spec-type mtp:n_max=4,p_min=0.0

Older ik_llama builds may use legacy speculative flags. Use a build that includes Gemma 4 12B MTP/CUDA support.

Smoke Test

Local smoke on an RTX 4070 with ik_llama build 4561 (6b9de3dba):

Target: Gemma 4 12B IT IQ4_XS
Draft: Q4_K_M
Raw completion TG: about 129 tok/s with MTP vs about 60 tok/s plain
Draft acceptance in the small smoke: about 0.55

Downloads last month: 252

GGUF

Model size

0.4B params

Architecture

gemma4_mtp

Hardware compatibility

4-bit

8-bit

Inference Providers NEW

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Model tree for ji-farthing/gemma-4-12B-it-MTP-ik-llama-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Quantized

unsloth/gemma-4-12b-it-GGUF

Quantized

(1)

this model