Instructions to use itay1itzhak/T5-Tulu-Seed-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use itay1itzhak/T5-Tulu-Seed-0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="itay1itzhak/T5-Tulu-Seed-0")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("itay1itzhak/T5-Tulu-Seed-0")
model = AutoModelForSeq2SeqLM.from_pretrained("itay1itzhak/T5-Tulu-Seed-0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use itay1itzhak/T5-Tulu-Seed-0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "itay1itzhak/T5-Tulu-Seed-0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itay1itzhak/T5-Tulu-Seed-0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/itay1itzhak/T5-Tulu-Seed-0

SGLang

How to use itay1itzhak/T5-Tulu-Seed-0 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 "itay1itzhak/T5-Tulu-Seed-0" \
    --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": "itay1itzhak/T5-Tulu-Seed-0",
		"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 "itay1itzhak/T5-Tulu-Seed-0" \
        --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": "itay1itzhak/T5-Tulu-Seed-0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use itay1itzhak/T5-Tulu-Seed-0 with Docker Model Runner:
```
docker model run hf.co/itay1itzhak/T5-Tulu-Seed-0
```

Update model card: Refine pipeline tag, license, and add project page

by nielsr HF Staff - opened Jul 17, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+14

-11

nielsr

Jul 17, 2025

This PR enhances the model card for itay1itzhak/T5-Tulu by:

Refining the pipeline_tag: Changed from text2text-generation to text-generation to better reflect the model's primary use case as a causal language model for text generation and its architecture (T5ForConditionalGeneration used with AutoModelForCausalLM).
Correcting the license: Updated from apache-2.0 to mit to accurately reflect the license specified in the accompanying GitHub repository.
Adding the project page link: Included a direct link to the official project page (https://itay1itzhak.github.io/planted-in-pretraining) under "Model Details" for easier access to additional resources and context.
Adding metrics tag: Included accuracy to the metadata to improve discoverability based on the model's evaluation metrics.

These changes improve the clarity, accuracy, and discoverability of the model on the Hugging Face Hub.

Update model card: Refine pipeline tag, license, and add project pageb56f665c

itay1itzhak changed pull request status to merged Jul 17, 2025

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