Instructions to use itay1itzhak/OLMo-Flan-Seed-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use itay1itzhak/OLMo-Flan-Seed-2 with Transformers:

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

pipe = pipeline("text-generation", model="itay1itzhak/OLMo-Flan-Seed-2", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("itay1itzhak/OLMo-Flan-Seed-2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use itay1itzhak/OLMo-Flan-Seed-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "itay1itzhak/OLMo-Flan-Seed-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "itay1itzhak/OLMo-Flan-Seed-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/itay1itzhak/OLMo-Flan-Seed-2

SGLang

How to use itay1itzhak/OLMo-Flan-Seed-2 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/OLMo-Flan-Seed-2" \
    --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/OLMo-Flan-Seed-2",
		"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/OLMo-Flan-Seed-2" \
        --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/OLMo-Flan-Seed-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use itay1itzhak/OLMo-Flan-Seed-2 with Docker Model Runner:
```
docker model run hf.co/itay1itzhak/OLMo-Flan-Seed-2
```

Improve model card: Add abstract, project page, correct license, and add metrics tag

by nielsr HF Staff - opened Jul 17, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+17

-10

Improve model card: Add abstract, project page, correct license, and add metrics tag0ef786a4

nielsr

Jul 17, 2025

This PR enhances the model card by:

Including the paper's abstract to provide a comprehensive summary of the research.
Adding a direct link to the project page (https://itay1itzhak.github.io/planted-in-pretraining) for easier access to supplementary materials.
Correcting the license from apache-2.0 to mit in both the metadata and content, aligning with the license specified in the accompanying GitHub repository.
Adding the metrics: accuracy tag to the YAML metadata, reflecting the evaluation metric mentioned in the model card's "Evaluation" section.

These updates aim to provide more complete, accurate, and accessible information to users.

itay1itzhak changed pull request status to merged Jul 17, 2025

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