Instructions to use google/t5gemma-2b-2b-ul2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/t5gemma-2b-2b-ul2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="google/t5gemma-2b-2b-ul2")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/t5gemma-2b-2b-ul2") model = AutoModelForSeq2SeqLM.from_pretrained("google/t5gemma-2b-2b-ul2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use google/t5gemma-2b-2b-ul2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "google/t5gemma-2b-2b-ul2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/t5gemma-2b-2b-ul2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/google/t5gemma-2b-2b-ul2
- SGLang
How to use google/t5gemma-2b-2b-ul2 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 "google/t5gemma-2b-2b-ul2" \ --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": "google/t5gemma-2b-2b-ul2", "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 "google/t5gemma-2b-2b-ul2" \ --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": "google/t5gemma-2b-2b-ul2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use google/t5gemma-2b-2b-ul2 with Docker Model Runner:
docker model run hf.co/google/t5gemma-2b-2b-ul2
Extra tokens for UL2
I have a question about the tokenizer, as I noticed that there are no additional_special_tokens with extra_id_{number} in the model's tokenizer config that was trained on the UL2 task. Is this intentional, or are you using different tokens for extra_id ? They were present in the previous UL2-trained model: https://huggingface.co/google/ul2/blob/main/tokenizer_config.json
Yes, It's intentional. The model uses token (ex- <unused0> to <unused98>) to serve the identical purpose of sentinel tokens for the UL2 denoising objective. These tokens are already integrated directly into the model's main vocabulary added_tokens_decoder and do not need to be separately listed under additional_special_tokens. Please have a look at this t5gemma-2b-2b-ul2 - tokenizer_config.json file.