Instructions to use cloudnathan5/gemma-4-12b-it-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use cloudnathan5/gemma-4-12b-it-MTP-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 cloudnathan5/gemma-4-12b-it-MTP-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf cloudnathan5/gemma-4-12b-it-MTP-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 cloudnathan5/gemma-4-12b-it-MTP-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf cloudnathan5/gemma-4-12b-it-MTP-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 cloudnathan5/gemma-4-12b-it-MTP-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cloudnathan5/gemma-4-12b-it-MTP-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 cloudnathan5/gemma-4-12b-it-MTP-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cloudnathan5/gemma-4-12b-it-MTP-GGUF:Q4_K_M
Use Docker
docker model run hf.co/cloudnathan5/gemma-4-12b-it-MTP-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use cloudnathan5/gemma-4-12b-it-MTP-GGUF with Ollama:
ollama run hf.co/cloudnathan5/gemma-4-12b-it-MTP-GGUF:Q4_K_M
- Unsloth Studio
How to use cloudnathan5/gemma-4-12b-it-MTP-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 cloudnathan5/gemma-4-12b-it-MTP-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 cloudnathan5/gemma-4-12b-it-MTP-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cloudnathan5/gemma-4-12b-it-MTP-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use cloudnathan5/gemma-4-12b-it-MTP-GGUF with Docker Model Runner:
docker model run hf.co/cloudnathan5/gemma-4-12b-it-MTP-GGUF:Q4_K_M
- Lemonade
How to use cloudnathan5/gemma-4-12b-it-MTP-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cloudnathan5/gemma-4-12b-it-MTP-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma-4-12b-it-MTP-GGUF-Q4_K_M
List all available models
lemonade list
Important Update: Unsloth assistant GGUFs
Unsloth has released working Gemma 4 12B instruct assistant models that work with upstream llama.cpp for mtp. Check them out here: https://huggingface.co/unsloth/gemma-4-12b-it-GGUF/tree/main/MTP
Generally you can find them at Unsloth's model pages in the files section where there will be an MTP folder containing the assistant model GGUFs.
Originally this model repo had experimental GGUF quantized versions of the gemma-4-12B-it-assistant from google, but I have since then updated them to be quantizations of Unsloth's MTP drafters which are compatible with the latest llama.cpp out of the box.
Gemma-4-12B-IT-QUANTIZED-MTP GGUFs
This model repo contains various GGUF format quantizations of Unsloth's gemma-4-12b-it-BF16-MTP.gguf. I did not include BF16, F16, or Q8_0 because those are available from Unsloth at that link. All of these quantizations were made using llama-quantize from llama.cpp. These models are just the MTP heads which are meant to be used alongside the base model, not on their own.
Example Usage
#!/usr/bin/env bash
# llama-server from llama.cpp
/path/to/llama.cpp/build/bin/llama-server \
-m /path/to/gemma-4-12b-it.gguf \ # Base model is some version of Gemma 4 12B it
-md /path/to/gemma-4-12b-it-Q4_K_M-MTP.gguf \ # Draft model for MTP set to one of these MTP models
--spec-type draft-mtp \ # Set speculative decoding type to MTP
--spec-draft-n-max 3 \ # Set the max number of tokens the drafter should generate (you can play around with this)
-ngld 99 # Keep all draft model layers on GPU (keeps draft generation speedy)
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Model tree for cloudnathan5/gemma-4-12b-it-MTP-GGUF
Base model
google/gemma-4-12B-it-assistant