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 Settings

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
```

itay1itzhak commited on Jul 16, 2025

Commit

9d8e4ee

verified ·

1 Parent(s): 5468cd1

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -32,8 +32,8 @@ We study whether cognitive biases in LLMs emerge from pretraining, instruction t
 - **Language(s)**: English
 - **License**: Apache 2.0
 - **Finetuned from**: `allenai/OLMo-7B`
-- **Paper**: https://arxiv.org/abs/2507.XXXXX
-- **Repository**: https://github.com/anonymous/bias-causal-origins
 ## Uses

 - **Language(s)**: English
 - **License**: Apache 2.0
 - **Finetuned from**: `allenai/OLMo-7B`
+- **Paper**: https://arxiv.org/abs/2507.07186
+- **Repository**: https://github.com/itay1itzhak/planted-in-pretraining
 ## Uses