Instructions to use EleutherAI/pythia-160m-data-seed1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EleutherAI/pythia-160m-data-seed1 with Transformers:

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

pipe = pipeline("text-generation", model="EleutherAI/pythia-160m-data-seed1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-160m-data-seed1")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-160m-data-seed1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EleutherAI/pythia-160m-data-seed1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EleutherAI/pythia-160m-data-seed1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EleutherAI/pythia-160m-data-seed1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EleutherAI/pythia-160m-data-seed1

SGLang

How to use EleutherAI/pythia-160m-data-seed1 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 "EleutherAI/pythia-160m-data-seed1" \
    --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": "EleutherAI/pythia-160m-data-seed1",
		"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 "EleutherAI/pythia-160m-data-seed1" \
        --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": "EleutherAI/pythia-160m-data-seed1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EleutherAI/pythia-160m-data-seed1 with Docker Model Runner:
```
docker model run hf.co/EleutherAI/pythia-160m-data-seed1
```

stellaathena commited on Jan 11

Commit

dfa32dc

verified ·

1 Parent(s): 3154bbf

Add model documentation

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README.md +16 -0

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@@ -1,3 +1,19 @@
 # PolyPythias
 This model is part of the **PolyPythias** suite, an extension of the [Pythia](https://github.com/EleutherAI/pythia) project providing 45 additional training runs across 5 model sizes with 9 different random seeds each. These models enable systematic study of training stability and reproducibility in language models.

+---
+language:
+  - en
+tags:
+  - pytorch
+  - causal-lm
+  - pythia
+  - polypythias
+license: apache-2.0
+datasets:
+  - EleutherAI/pile
+  - EleutherAI/pile-preshuffled-seeds
+library_name: transformers
+arxiv: 2503.09543
+---
 # PolyPythias
 This model is part of the **PolyPythias** suite, an extension of the [Pythia](https://github.com/EleutherAI/pythia) project providing 45 additional training runs across 5 model sizes with 9 different random seeds each. These models enable systematic study of training stability and reproducibility in language models.