This repository is for EleutherAI's work-in-progress project Pythia which combines interpretability analysis and scaling laws to understand how knowledge develops and evolves during training in autoregressive transformers.

We train and release a suite of 8 model sizes on 2 different datasets: the Pile, as well as the Pile with deduplication applied.

All 8 model sizes are trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 ~= 299.9B tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 ~= 2B tokens, evenly spaced throughout training. This corresponds to just under 1 epoch on the Pile for non-"deduped" models, and ~= 1.5 epochs on the deduped Pile (which contains 207B tokens in 1 epoch).

Config files used to train these models within the GPT-NeoX library can be found at the models/ directory within this repository.

We also upload the pre-tokenized data files and a script to reconstruct the dataloader as seen during training for all models. See Reproducing Training section for more details.

All Pythia models are hosted on the Huggingface hub. They can be loaded and used via the following code (shown for the 3rd pythia-70M-deduped model checkpoint):

from transformers import GPTNeoXForCausalLM, AutoTokenizer

model = GPTNeoXForCausalLM.from_pretrained(
  "EleutherAI/pythia-70m-deduped",
  revision="step3000",
  cache_dir="./pythia-70m-deduped/step3000",
)

tokenizer = AutoTokenizer.from_pretrained(
  "EleutherAI/pythia-70m-deduped",
  revision="step3000",
  cache_dir="./pythia-70m-deduped/step3000",
)

inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])

All models were trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Revision/branch step143000 (e.g. https://huggingface.co/EleutherAI/pythia-19m-deduped/tree/step143000) corresponds exactly to the model checkpoint on the main branch of each model.

Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, and checkpointed every 500 steps. The checkpoints on Huggingface are renamed for consistency with all 2M batch models, so step1000 is the first checkpoint for the 1.3B that was saved (corresponding to step 500 in training), and step1000 is likewise the first 6.7B checkpoint that was saved (corresponding to 1000 "actual" steps.)

We additionally have all model checkpoints in the format accepted by the GPT-NeoX library, but do not serve them at scale due to size of optimizer states and anticipated lower demand. If you would like to perform analysis using the models within the GPT-NeoX codebase, or would like the optimizer states, please email [email protected] and [email protected] to arrange access.

We provide the training data for replication of our training runs. The GPT-NeoX library requires the pre-tokenized training data in the form of 2 memory-mapped numpy arrays: a .bin and .idx file.

We provide these files, hosted on the Hugging Face hub.

To download and use the deduplicated Pile training data, run:

git lfs clone https://huggingface.co/datasets/EleutherAI/pythia_pile_idxmaps

python utils/unshard_memmap.py --input_file ./pythia_pile_idxmaps/pile_0.87_deduped_text_document-00000-of-00082.bin --num_shards 83 --output_dir ./pythia_pile_idxmaps/

This will take over a day to run, though it should not require more than 5 GB of RAM. We recommend downloading this rather than retokenizing the Pile from scratch, in order to preserve the data order seen by the Pythia models.

TODO: forthcoming: more information on how to replicate + relaunch the Pythia training runs, once the data is actually downloaded.

We provide a tool to view particular portions of the training dataloader used by all models during training, at utils/batch_viewer.py.

To run, first substitute the filepath to the downloaded .bin and .idx files for either the Pile or deduplicated Pile in utils/dummy_config.yml.

PYTHONPATH=utils/gpt-neox/ python utils/batch_viewer.py \
  --start_iteration 0 \
  --end_iteration 1000 \
  --mode save \
  --conf_dir utils/dummy_config.yml 

Passing --mode save will save a separate file containing each batch as a numpy array.

Passing --mode custom will save a dictionary for each batch to a JSONL file--it can be used to compute arbitrary statistics over each batch seen during training.

We also provide benchmark 0-shot and 5-shot results on a variety of NLP datasets:

• Lambada (lambada_openai)
• Wikitext (wikitext)
• PiQA (piqa)
• SciQ (sciq)
• WSC (wsc)
• Winogrande (winogrande)
• ARC-challenge (arc_challenge)
• ARC-easy (arc_easy)
• LogiQA (logiqa)
• BLiMP (blimp_*)
• MMLU (hendrycksTest*)

Evaluations were performed in GPT-NeoX using the LM Evaluation Harness, and are viewable by model and step at results/json/* in this repository.

TODO: 125m, 350m, and 1.3b models' step numbers from these evaluations correspond to .5x what is listed on Hugging Face. (e.g. here the final step is 71500, as it was during training. on the HF model hub these models' checkpoints were relabeled such that pythia-1.3b's "step143000" checkpoint is the same as the step 71500 checkpoint in these evals.)

We will also provide utilities for creating plots based on the dumped zero and few-shot results. Sample notebook and data format forthcoming.

A common explanation for language model training dynamics is that LMs have a mass of knowledge and when they come across new information they glom that knowledge on and slowly integrate it into the mass over time. One prediction that this mental model makes is that tokens encountered later in training will be more likely to be memorized than ones encountered earlier in training, as the model will not have time to adjust its representations to store the info without memorization. The primary goal of this experiment is to disprove this prediction and demonstrate that training order doesn't influence memorization.