I will be adding significant updates to this repository to include:
- RLHF (Reinforcement learning with human feedback)
- Use Decoder weights from HuggingFace t5 (Big thanks to Jason Phang)
- Add LoRA
- Integration with Web Search APIs
- External database integration
- Chain-of-thought prompting
- Integration with a Calculator API
- Remove ColossalAI for now. Just pure PyTorch
- Make fixes to the dataloader. Use OpenWebText instead
Open-source pre-training implementation of Google's LaMDA research paper in PyTorch. The totally not sentient AI. This repository will cover the 2B parameter implementation of the pre-training architecture as that is likely what most can afford to train. You can review Google's latest blog post from 2022 which details LaMDA here. You can also view their previous blog post from 2021 on the model here.
I have been greatly inspired by the work of Dr. Phil 'Lucid' Wang. Please check out his open-source implementations of multiple different transformer architectures and support his work.
Developer updates can be found on:
lamda_base = LaMDA(
num_tokens = 20000,
dim = 512,
dim_head = 64,
depth = 12,
heads = 8
)
lamda = AutoregressiveWrapper(lamda_base, max_seq_len = 512)
tokens = torch.randint(0, 20000, (1, 512)) # mock token data
logits = lamda(tokens)
print(logits)
- There may be issues with NaN for fp16 training.
- Pipeline parallelism should be used with ZeRO 1, not ZeRO 2.
- T5 Relative Positional Bias in Attention
- Gated GELU Activation in the Feed forward layer
- GPT-like Decoder Only architecture
- Autoregressive with Top-k sampling
- Sentencepiece Byte-pair encoded tokenizer
- Finish building pre-training model architecture
- Add pre-training script
- Integrate Huggingface datasets
- Use The Pile from Eleuther AI.
- Build the GODEL dataset and upload to HuggingFace datasets
- Implement GPT-2 tokenizer
- Add Sentencepiece tokenizer training script and integration
- Add detailed documentation
- Add logging with Weights And Biases
- Add scaling with ColossalAI.
- Add finetuning script
- Add pip installer with PyPI
- Implement a JAX / Flax version as well
- Add inference only if someone wants to open-source LaMDA model weights
- Enrico Shippole
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author = {Romal Thoppilan and
Daniel De Freitas and
Jamie Hall and
Noam Shazeer and
Apoorv Kulshreshtha and
Heng{-}Tze Cheng and
Alicia Jin and
Taylor Bos and
Leslie Baker and
Yu Du and
YaGuang Li and
Hongrae Lee and
Huaixiu Steven Zheng and
Amin Ghafouri and
Marcelo Menegali and
Yanping Huang and
Maxim Krikun and
Dmitry Lepikhin and
James Qin and
Dehao Chen and
Yuanzhong Xu and
Zhifeng Chen and
Adam Roberts and
Maarten Bosma and
Yanqi Zhou and
Chung{-}Ching Chang and
Igor Krivokon and
Will Rusch and
Marc Pickett and
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Meredith Ringel Morris and
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Toju Duke and
Johnny Soraker and
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Josh Lee and
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Joe Fenton and
Aaron Cohen and
Rachel Bernstein and
Ray Kurzweil and
Blaise Aguera{-}Arcas and
Claire Cui and
Marian Croak and
Ed H. Chi and
Quoc Le},
title = {LaMDA: Language Models for Dialog Applications},
journal = {CoRR},
volume = {abs/2201.08239},
year = {2022},
url = {https://arxiv.org/abs/2201.08239},
eprinttype = {arXiv},
eprint = {2201.08239},
timestamp = {Fri, 22 Apr 2022 16:06:31 +0200},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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publisher = {arXiv},
year = {2019},
copyright = {arXiv.org perpetual, non-exclusive license}
}
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doi = {10.48550/ARXIV.2002.05202},
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author = {Shazeer, Noam},
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publisher = {arXiv},
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copyright = {arXiv.org perpetual, non-exclusive license}
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@article{DBLP:journals/corr/abs-2101-00027,
author = {Leo Gao and
Stella Biderman and
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journal = {CoRR},
volume = {abs/2101.00027},
year = {2021},
url = {https://arxiv.org/abs/2101.00027},
eprinttype = {arXiv},
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}