Instructions to use tawatchai/gemma4-e4b-nothink-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tawatchai/gemma4-e4b-nothink-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tawatchai/gemma4-e4b-nothink-gguf", filename="gemma4-e4b-nothink.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 tawatchai/gemma4-e4b-nothink-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 tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf tawatchai/gemma4-e4b-nothink-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 tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf tawatchai/gemma4-e4b-nothink-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 tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tawatchai/gemma4-e4b-nothink-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 tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M
Use Docker
docker model run hf.co/tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use tawatchai/gemma4-e4b-nothink-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tawatchai/gemma4-e4b-nothink-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tawatchai/gemma4-e4b-nothink-gguf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M
- Ollama
How to use tawatchai/gemma4-e4b-nothink-gguf with Ollama:
ollama run hf.co/tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M
- Unsloth Studio
How to use tawatchai/gemma4-e4b-nothink-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 tawatchai/gemma4-e4b-nothink-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 tawatchai/gemma4-e4b-nothink-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tawatchai/gemma4-e4b-nothink-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tawatchai/gemma4-e4b-nothink-gguf with Docker Model Runner:
docker model run hf.co/tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M
- Lemonade
How to use tawatchai/gemma4-e4b-nothink-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tawatchai/gemma4-e4b-nothink-gguf:Q4_K_M
Run and chat with the model
lemonade run user.gemma4-e4b-nothink-gguf-Q4_K_M
List all available models
lemonade list
gemma4-e4b-nothink (GGUF, Q4_K_M)
A repackaging of the Gemma 3n E4B Instruct model (8.0B effective params,
multimodal: text + vision + audio) as a Q4_K_M GGUF for use with
Ollama / llama.cpp, with a "nothink" recipe:
chat templating is disabled and the model receives the raw prompt
(TEMPLATE {{ .Prompt }} + PARSER passthrough), so it never emits
hidden reasoning / <think> blocks. Good for deterministic
completion-style use, programmatic prompting, and pipelines that want
full control over message framing.
- Architecture: gemma4 (a.k.a. Gemma 3n)
- Parameters: ~8.0B
- Context length: 131,072
- Embedding length: 2,560
- Quantization: Q4_K_M
- File size: ~8.9 GB
- Capabilities: text completion, vision, audio
- Origin: Exported from local Ollama tag
gemma4:e4b-nothink
The underlying weights are Google's Gemma model. Use is governed by the Gemma Terms of Use and the Gemma Prohibited Use Policy. By downloading or using these files you agree to those terms.
Files
| File | Purpose |
|---|---|
gemma4-e4b-nothink.Q4_K_M.gguf |
The quantized model weights |
Modelfile |
Ollama recipe (template/parser/sampling params) |
LICENSE |
Gemma Terms of Use |
Quick start with Ollama
1. Install Ollama
See https://ollama.com/download. Make sure ollama is on your PATH and
the daemon is running (ollama serve or the desktop app).
2. Pull the files from this repo
# Install once
pip install -U "huggingface_hub[cli]"
# Download into ./gemma4-e4b-nothink/
hf download tawatchai/gemma4-e4b-nothink-gguf \
--local-dir ./gemma4-e4b-nothink
3. Register the model with Ollama
cd ./gemma4-e4b-nothink
ollama create gemma4:e4b-nothink -f Modelfile
ollama create reads Modelfile, finds the relative FROM ./gemma4-e4b-nothink.Q4_K_M.gguf, and imports it into Ollama's local
store. After this completes you can delete the working directory if you
want โ the blob is copied into ~/.ollama/.
Verify:
ollama list | grep gemma4
ollama show gemma4:e4b-nothink
4. Run it
Interactive:
ollama run gemma4:e4b-nothink
One-shot:
ollama run gemma4:e4b-nothink "Translate to German: Where is the train station?"
OpenAI-compatible API (default http://localhost:11434/v1):
curl http://localhost:11434/v1/chat/completions \
-H 'content-type: application/json' \
-d '{
"model": "gemma4:e4b-nothink",
"messages": [{"role": "user", "content": "Hello!"}],
"temperature": 0.1
}'
Quick start with llama.cpp
# Download just the GGUF
hf download tawatchai/gemma4-e4b-nothink-gguf \
gemma4-e4b-nothink.Q4_K_M.gguf --local-dir .
# Run
./llama-cli -m ./gemma4-e4b-nothink.Q4_K_M.gguf \
-p "Hello!" --temp 0.1 --top-k 64 --top-p 0.95
Because this is a "nothink" / passthrough variant, you are responsible for any chat formatting (turn markers, system messages, etc.). If you want the standard Gemma chat template instead, use
google/gemma-3n-E4B-itdirectly, or replaceTEMPLATE {{ .Prompt }}in the Modelfile with the Gemma chat template.
Sampling defaults (from Modelfile)
temperature 0.1
top_k 64
top_p 0.95
Override per-request via the Ollama / OpenAI API as usual.
Faster downloads (optional)
pip install hf_transfer
export HF_HUB_ENABLE_HF_TRANSFER=1
hf download tawatchai/gemma4-e4b-nothink-gguf --local-dir ./gemma4-e4b-nothink
License
Gemma is provided under the Gemma Terms of Use. See LICENSE in this
repo and https://ai.google.dev/gemma/terms.
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