pythia-weight-seed3

This model is part of the PolyPythias suite, an extension of the 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.

Paper

PolyPythias: Stability and Outliers across Fifty Language Model Pre-Training Runs

Oskar van der Wal, Pietro Lesci, Max Muller-Eberstein, Naomi Saphra, Hailey Schoelkopf, Willem Zuidema, and Stella Biderman. ICLR 2025.

Model Details

All models were trained on 300B tokens from The Pile.

Naming Convention

• pythia-{size}m - Original Pythia model (seed 1234)
• pythia-{size}m-seed{1-9} - PolyPythias variants with different random seeds
• pythia-160m-data-seed{1-3} - 160M models with only data ordering varied (weight init fixed)
• pythia-160m-weight-seed{1-3} - 160M models with only weight initialization varied (data order fixed)

The decoupled seed variants (data-seed and weight-seed) allow researchers to separately study the effects of data ordering vs. weight initialization.

Quick Start

from transformers import GPTNeoXForCausalLM, AutoTokenizer

# Load the final checkpoint
model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-70m-seed3")
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-70m-seed3")

# Generate text
inputs = tokenizer("The quick brown fox", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0]))

Available Checkpoints

Each model provides 154 intermediate checkpoints saved as Git branches:

To load a specific checkpoint:

model = GPTNeoXForCausalLM.from_pretrained(
    "EleutherAI/pythia-70m-seed3",
    revision="step50000",  # Any checkpoint step
)

Training Data

All models were trained on The Pile using pre-shuffled data orderings. The shuffled index files for each seed are available at:

EleutherAI/pile-preshuffled-seeds

This dataset contains .idx files for seeds 0-9 used with MMapIndexedDataset to load the memory-mapped Pile data in the correct order for each seed.

Reproducing Training Data Order

To reproduce the exact data ordering used for a specific seed:

1. Download the Pile dataset and tokenize it using the Pythia tokenizer
2. Download the corresponding seed folder from pile-preshuffled-seeds:

   # Using huggingface_hub
   from huggingface_hub import snapshot_download
   snapshot_download(
       repo_id="EleutherAI/pile-preshuffled-seeds",
       repo_type="dataset",
       allow_patterns="seed3/*",  # Download only seed3
       local_dir="./pile-seeds"
   )
   

3. Use the idx files with GPT-NeoX's MMapIndexedDataset:

   from dataset import MMapIndexedDataset
   dataset = MMapIndexedDataset(path_prefix, skip_warmup=True)
   

For complete training reproduction instructions, see the Pythia GitHub repository.

All PolyPythias Models

The complete collection is available at: EleutherAI/polypythias

14M Parameter Models

• pythia-14m-seed1 through pythia-14m-seed9

31M Parameter Models

• pythia-31m-seed1 through pythia-31m-seed9

70M Parameter Models

• pythia-70m-seed1 through pythia-70m-seed9

160M Parameter Models

• pythia-160m-seed1 through pythia-160m-seed9
• pythia-160m-data-seed1 through pythia-160m-data-seed3
• pythia-160m-weight-seed1 through pythia-160m-weight-seed3

410M Parameter Models

• pythia-410m-seed1 through pythia-410m-seed9

Evaluation Results

Evaluation results for all models are available in the polypythias-evals dataset.

Limitations

These models are released for research purposes only. They are not intended for deployment in production systems.

• Not instruction-tuned: These are base language models that pre