RichardErkhov/EleutherAI_-_pythia-410m-deduped-v0-gguf

Quantization made by Richard Erkhov.

pythia-410m-deduped-v0 - GGUF

Model creator: https://huggingface.co/EleutherAI/
Original model: https://huggingface.co/EleutherAI/pythia-410m-deduped-v0/

Name	Quant method	Size
pythia-410m-deduped-v0.Q2_K.gguf	Q2_K	0.16GB
pythia-410m-deduped-v0.Q3_K_S.gguf	Q3_K_S	0.18GB
pythia-410m-deduped-v0.Q3_K.gguf	Q3_K	0.21GB
pythia-410m-deduped-v0.Q3_K_M.gguf	Q3_K_M	0.21GB
pythia-410m-deduped-v0.Q3_K_L.gguf	Q3_K_L	0.22GB
pythia-410m-deduped-v0.IQ4_XS.gguf	IQ4_XS	0.22GB
pythia-410m-deduped-v0.Q4_0.gguf	Q4_0	0.23GB
pythia-410m-deduped-v0.IQ4_NL.gguf	IQ4_NL	0.23GB
pythia-410m-deduped-v0.Q4_K_S.gguf	Q4_K_S	0.23GB
pythia-410m-deduped-v0.Q4_K.gguf	Q4_K	0.25GB
pythia-410m-deduped-v0.Q4_K_M.gguf	Q4_K_M	0.25GB
pythia-410m-deduped-v0.Q4_1.gguf	Q4_1	0.25GB
pythia-410m-deduped-v0.Q5_0.gguf	Q5_0	0.27GB
pythia-410m-deduped-v0.Q5_K_S.gguf	Q5_K_S	0.27GB
pythia-410m-deduped-v0.Q5_K.gguf	Q5_K	0.28GB
pythia-410m-deduped-v0.Q5_K_M.gguf	Q5_K_M	0.28GB
pythia-410m-deduped-v0.Q5_1.gguf	Q5_1	0.29GB
pythia-410m-deduped-v0.Q6_K.gguf	Q6_K	0.31GB
pythia-410m-deduped-v0.Q8_0.gguf	Q8_0	0.4GB

Original model description:

language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated

The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available on Hugging Face.

The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models match or exceed the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites.

Please note that all models in the Pythia suite were renamed in January 2023. For clarity, a table comparing the old and new names is provided in this model card, together with exact parameter counts.

Pythia-410M-deduped

Model Details

Developed by: EleutherAI
Model type: Transformer-based Language Model
Language: English
Learn more: Pythia's GitHub repository for training procedure, config files, and details on how to use.
Library: GPT-NeoX
License: Apache 2.0
Contact: to ask questions about this model, join the EleutherAI Discord, and post them in #release-discussion. Please read the existing Pythia documentation before asking about it in the EleutherAI Discord. For general correspondence: contact@eleuther. ai.

Pythia model	Non-Embedding Params	Layers	Model Dim	Heads	Batch Size	Learning Rate	Equivalent Models
70M	18,915,328	6	512	8	2M	1.0 x 10^-3	—
160M	85,056,000	12	768	12	4M	6.0 x 10^-4	GPT-Neo 125M, OPT-125M
410M	302,311,424	24	1024	16	4M	3.0 x 10^-4	OPT-350M
1.0B	805,736,448	16	2048	8	2M	3.0 x 10^-4	—
1.4B	1,208,602,624	24	2048	16	4M	2.0 x 10^-4	GPT-Neo 1.3B, OPT-1.3B
2.8B	2,517,652,480	32	2560	32	2M	1.6 x 10^-4	GPT-Neo 2.7B, OPT-2.7B
6.9B	6,444,163,072	32	4096	32	2M	1.2 x 10^-4	OPT-6.7B
12B	11,327,027,200	36	5120	40	2M	1.2 x 10^-4	—

Engineering details for the Pythia Suite. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have exactly the same architecture, and the same number of non-embedding parameters.

Uses and Limitations

Intended Use

The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change in the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch 143000 corresponds exactly to the model checkpoint on the main branch of each model.

You may also further fine-tune and adapt Pythia-410M-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face Transformers Library. If you decide to use pre-trained Pythia-410M-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment.

Out-of-scope use

The Pythia Suite is not intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions.

Pythia models are English-language only, and are not suitable for translation or generating text in other languages.

Pythia-410M-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-410M-deduped will not respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions.

Limitations and biases

The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-410M-deduped to produce factually accurate output.

This model was trained on the Pile, a dataset known to contain profanity and texts that are lewd or otherwise offensive. See Section 6 of the Pile paper for a discussion of documented biases with regards to gender, religion, and race. Pythia-410M-deduped may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.

If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-410M-deduped.

Quickstart

Pythia models can be loaded and used via the following code, demonstrated here for the third pythia-70m-deduped 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])

Revision/branch step143000 corresponds exactly to the model checkpoint on the main branch of each model.
For more information on how to use all Pythia models, see documentation on GitHub.

Training

Training data

Pythia-410M-deduped was trained on the Pile after the dataset has been globally deduplicated.
The Pile is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See the Pile paper for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult the datasheet for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the official website, or from a community mirror.

Training procedure

All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.

All Pythia models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so step1000 is the first checkpoint for pythia-1.4b that was saved (corresponding to step 500 in training), and step1000 is likewise the first pythia-6.9b checkpoint that was saved (corresponding to 1000 “actual” steps).
See GitHub for more details on training procedure, including how to reproduce it.
Pythia uses the same tokenizer as GPT-NeoX- 20B.

Evaluations

All 16 Pythia models were evaluated using the LM Evaluation Harness. You can access the results by model and step at results/json/* in the GitHub repository.
Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM.

LAMBADA – OpenAI

Physical Interaction: Question Answering (PIQA)

WinoGrande

AI2 Reasoning Challenge – Challenge Set

SciQ

Naming convention and parameter count

Pythia models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count.

current Pythia suffix	old suffix	total params	non-embedding params
70M	19M	70,426,624	18,915,328
160M	125M	162,322,944	85,056,000
410M	350M	405,334,016	302,311,424
1B	800M	1,011,781,632	805,736,448
1.4B	1.3B	1,414,647,808	1,208,602,624
2.8B	2.7B	2,775,208,960	2,517,652,480
6.9B	6.7B	6,857,302,016	6,444,163,072
12B	13B	11,846,072,320	11,327,027,200