Text Generation
Transformers
Safetensors
qwen3
dflash
speculative-decoding
block-diffusion
draft-model
efficiency
qwen
gemma
diffusion-language-model
text-generation-inference
Instructions to use z-lab/gemma-4-26B-A4B-it-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use z-lab/gemma-4-26B-A4B-it-DFlash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="z-lab/gemma-4-26B-A4B-it-DFlash")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("z-lab/gemma-4-26B-A4B-it-DFlash") model = AutoModel.from_pretrained("z-lab/gemma-4-26B-A4B-it-DFlash") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use z-lab/gemma-4-26B-A4B-it-DFlash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "z-lab/gemma-4-26B-A4B-it-DFlash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "z-lab/gemma-4-26B-A4B-it-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/z-lab/gemma-4-26B-A4B-it-DFlash
- SGLang
How to use z-lab/gemma-4-26B-A4B-it-DFlash with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "z-lab/gemma-4-26B-A4B-it-DFlash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "z-lab/gemma-4-26B-A4B-it-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "z-lab/gemma-4-26B-A4B-it-DFlash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "z-lab/gemma-4-26B-A4B-it-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use z-lab/gemma-4-26B-A4B-it-DFlash with Docker Model Runner:
docker model run hf.co/z-lab/gemma-4-26B-A4B-it-DFlash
This works with a quantsized version?
#4
by khronnuz - opened
I am running an FP8 version of Gemma 4, would this work or only works with the original version from google?
Seconding this, would like to know ii it works with quantization
Yes it works but you may get a lower token acceptance rate.