Instructions to use TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink 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 TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink 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 TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-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 TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-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 TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M
Use Docker
docker model run hf.co/TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M
- LM Studio
- Jan
- vLLM
How to use TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M
- Ollama
How to use TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink with Ollama:
ollama run hf.co/TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M
- Unsloth Desktop
- Pi
How to use TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-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": "TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink with Docker Model Runner:
docker model run hf.co/TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M
- Lemonade
How to use TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M
Run and chat with the model
lemonade run user.gemma-4-26B-A4B-it-GGUF-Tater-NoThink-UD-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-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 TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-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 "TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink:UD-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-4-26B-A4B-it-GGUF Tater NoThink
This is a Tater NoThink test build of unsloth/gemma-4-26B-A4B-it-GGUF, bundled with compatible Multi-Token Prediction (MTP) and DFlash draft models for llama.cpp speculative decoding.
The main model weights and projector are from the upstream Unsloth GGUF release. The only intended change to the main text model is its embedded GGUF tokenizer.chat_template metadata:
gemma-4-26B-A4B-it-UD-Q4_K_M.gguf
The template is patched to force:
{%- set enable_thinking = false -%}
This makes the model prefer the no-thinking chat-template path even when a client accidentally exposes or passes a thinking flag.
Included Files
gemma-4-26B-A4B-it-UD-Q4_K_M.gguf: Tater NoThink patched GGUF.mmproj-F16.gguf: unchanged upstream vision projector.gemma-4-26B-A4B-it-MTP-Q8_0.gguf: Q8_0 MTP draft model converted from Google's official Gemma 4 assistant checkpoint. This is a sidecar draft model, not a standalone chat model.gemma-4-26B-A4B-it-DFlash-Q8_0.gguf: Q8_0 DFlash draft model from the exact Gemma 4 26B-A4B-it DFlash checkpoint. This is also a sidecar draft model, not a standalone chat model.
Speculative Decoding In Tater
Download the main model and one compatible draft model, then configure the llama.cpp provider in Tater.
MTP
- Select
gemma-4-26B-A4B-it-UD-Q4_K_M.ggufas the main model. - Enable Speculative Decoding.
- Set Method to Multi-Token Prediction (MTP).
- Select
gemma-4-26B-A4B-it-MTP-Q8_0.ggufunder Draft Model (GGUF). - Leave Maximum Draft Tokens at the recommended value of
3, then use Save & Load.
The draft must be paired with this Gemma 4 26B target family. It is not interchangeable with DFlash, DSpark, or drafts made for another target model.
DFlash
- Select
gemma-4-26B-A4B-it-UD-Q4_K_M.ggufas the main model. - Enable Speculative Decoding.
- Set Method to DFlash.
- Select
gemma-4-26B-A4B-it-DFlash-Q8_0.ggufunder Draft Model (GGUF). - Set Maximum Draft Tokens to
3, then use Save & Load.
The DFlash checkpoint was trained with a block size of 16, but its published mixed-workload benchmark found 3 to be the best general starting value. Structured tasks such as code or JSON may benefit from 4 or 5.
llama.cpp command-line example
Use a recent llama.cpp build with Gemma 4 MTP support:
llama-server \
--model gemma-4-26B-A4B-it-UD-Q4_K_M.gguf \
--model-draft gemma-4-26B-A4B-it-MTP-Q8_0.gguf \
--spec-type draft-mtp \
--spec-draft-n-max 3 \
--jinja \
--flash-attn on
For DFlash, replace the draft model and speculative type:
llama-server \
--model gemma-4-26B-A4B-it-UD-Q4_K_M.gguf \
--model-draft gemma-4-26B-A4B-it-DFlash-Q8_0.gguf \
--spec-type draft-dflash \
--spec-draft-n-max 3 \
--jinja \
--flash-attn on
Actual speedup depends on prompt, hardware, context length, and draft acceptance rate.
Source And Attribution
Upstream repository:
- https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF
- https://huggingface.co/google/gemma-4-26B-A4B-it-assistant
- https://huggingface.co/z-lab/gemma-4-26B-A4B-it-DFlash
- https://huggingface.co/williamliao/gemma-4-26B-A4B-it-DFlash-GGUF
License:
- Apache 2.0
This repo is meant as a practical Tater compatibility build, not a new model or fine tune.
Draft model provenance
The MTP sidecar was converted from google/gemma-4-26B-A4B-it-assistant revision 6e5aaaf4c42b98394530b8fda2e95cadd65c151c with llama.cpp revision 8e7f22b67ef4, using Q8_0 output. During conversion, the source tokenizer configuration's empty extra_special_tokens list was treated as an empty mapping to satisfy the converter; model weights were not changed.
The DFlash sidecar is redistributed unchanged from williamliao/gemma-4-26B-A4B-it-DFlash-GGUF revision d1800ac59f255542ae096018fa696f03918066a6. That conversion is derived from the Apache-2.0-licensed z-lab/gemma-4-26B-A4B-it-DFlash checkpoint and was tested by its publisher with llama.cpp and the same Gemma 4 26B-A4B-it target family.
| File | Size | SHA-256 |
|---|---|---|
gemma-4-26B-A4B-it-MTP-Q8_0.gguf |
461,766,592 bytes | 9764ab8276181017bf565c2be9e7c61cc59b6f74f877c3d1514ca36e01d040f1 |
gemma-4-26B-A4B-it-DFlash-Q8_0.gguf |
472,432,704 bytes | 48ecabebc399e5424b89913197204f80cda23e42228c67b6725f1ab1cc8f5da3 |
DSpark Status
A Gemma 4 26B-A4B-it DSpark GGUF exists, but its current SpecForge conversion requires llama.cpp PR #26275 and the dflash.bonus_anchor runtime behavior. That support is not present in the llama.cpp revision bundled with the current Tater release, so the DSpark file is intentionally not mirrored here yet. It will be suitable to add after Tater moves to a compatible llama.cpp revision and passes a paired-model test.
Tater Usage
Use this repo as a llama.cpp GGUF model in Tater. For vision, select mmproj-F16.gguf as the projector.
Recommended first file:
gemma-4-26B-A4B-it-UD-Q4_K_M.gguf
- Downloads last month
- 2,043
4-bit
8-bit
Model tree for TaterTotterson/gemma-4-26B-A4B-it-GGUF-Tater-NoThink
Base model
google/gemma-4-26B-A4B