Open Pre-Trained Transformer Language Models (OPT): What does it take to train GPT-3?

Neural Search Talks — Zeta Alpha

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Do you have fond childhood memories of summer camp? For a chance at $250,000, campers must compete in a series of summer camp-themed challenges to prove that they are unbeatable, unhateable, and unbreakable. Host Chris Burns is joined by the multi-talented comedian Dana Moon to recap the first five episodes of season one of Battle Camp . Plus, Quori-Tyler (aka QT) joins the podcast to dish on the camp gossip, team dynamics, and the Watson to her Sherlock Holmes. Leave us a voice message at www.speakpipe.com/WeHaveTheReceipts Text us at (929) 487-3621 DM Chris @FatCarrieBradshaw on Instagram Follow We Have The Receipts wherever you listen, so you never miss an episode. Listen to more from Netflix Podcasts.…

לפני 3 שנים 47:12

M4A•בית הפרקים

Links

❓Feedback Form: https://scastella.typeform.com/to/rg7a5GfJ

📄 OPT paper: https://arxiv.org/abs/2205.01068

👾 Code: https://github.com/facebookresearch/metaseq

📒 Logbook: https://github.com/facebookresearch/metaseq/blob/main/projects/OPT/chronicles/OPT175B_Logbook.pdf

✍️ OPT Official Blog Post: https://ai.facebook.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/

OpenAI Embeddings API: https://openai.com/blog/introducing-text-and-code-embeddings/

Nils Reimers' critique of OpenAI Embeddings API: https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9

Timestamps:

00:00 Introduction and housekeeping: new feedback form, ACL conference highlights

02:42 The convergence between NLP and Neural IR techniques

06:43 Open Pretrained Transformer motivation and scope, reproducing GPT-3 and open-sourcing

08:16 Basics of OPT: architecture, pre-training objective, teacher forcing, tokenizer, training data

13:40 Preliminary experiments findings: hyperparameters, training stability, spikiness

20:08 Problems that appear at scale when training with 992 GPUs

23:01 Using temperature to check whether GPUs are working

25:00 Training the largest model: what to do when the loss explodes? (which happens quite often)

29:15 When they switched away from AdamW to SGD

32:00 Results: successful but not quite GPT-3 level.

Toxicity? 35:45 Replicability of Large Language Models research. Was GPT-3 replicable? What difference does it make?

37:25 What makes a paper replicable?

40:33 Directions in which large Language Models are applied to Information Retrieval

45:15 Final thoughts and takeaways

21 פרקים

#Tech #Zeta Alpha

Open Pre-Trained Transformer Language Models (OPT): What does it take to train GPT-3?

Neural Search Talks — Zeta Alpha

published לפני 3 שנים

שתפו

M4A•בית הפרקים

Links

❓Feedback Form: https://scastella.typeform.com/to/rg7a5GfJ

📄 OPT paper: https://arxiv.org/abs/2205.01068

👾 Code: https://github.com/facebookresearch/metaseq

📒 Logbook: https://github.com/facebookresearch/metaseq/blob/main/projects/OPT/chronicles/OPT175B_Logbook.pdf

✍️ OPT Official Blog Post: https://ai.facebook.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/

OpenAI Embeddings API: https://openai.com/blog/introducing-text-and-code-embeddings/

Nils Reimers' critique of OpenAI Embeddings API: https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9

Timestamps:

00:00 Introduction and housekeeping: new feedback form, ACL conference highlights

02:42 The convergence between NLP and Neural IR techniques

06:43 Open Pretrained Transformer motivation and scope, reproducing GPT-3 and open-sourcing

08:16 Basics of OPT: architecture, pre-training objective, teacher forcing, tokenizer, training data

13:40 Preliminary experiments findings: hyperparameters, training stability, spikiness

20:08 Problems that appear at scale when training with 992 GPUs

23:01 Using temperature to check whether GPUs are working

25:00 Training the largest model: what to do when the loss explodes? (which happens quite often)

29:15 When they switched away from AdamW to SGD

32:00 Results: successful but not quite GPT-3 level.

Toxicity? 35:45 Replicability of Large Language Models research. Was GPT-3 replicable? What difference does it make?

37:25 What makes a paper replicable?

40:33 Directions in which large Language Models are applied to Information Retrieval

45:15 Final thoughts and takeaways

21 פרקים

#Tech #Zeta Alpha

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Open Pre-Trained Transformer Language Models (OPT): What does it take to train GPT-3?