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lucidrains
PaLM-rlhf-pytorch
Python2022–2023
lucidrains / PaLM-rlhf-pytorch

PaLM-rlhf-pytorch

Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

6Distinct people
6Mentions
0Explicit recommenders
7.9KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week6 dated mentions

Observed mentions: 2022-12-25 17:50:02 UTC to 2023-05-09 14:09:30 UTC. Weeks start Monday, UTC.

0122022-12-19 00:00:00 UTC to 2022-12-26 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsDec 192022-12-26 00:00:00 UTC to 2023-01-02 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2023-01-02 00:00:00 UTC to 2023-01-09 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-09 00:00:00 UTC to 2023-01-16 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-01-16 00:00:00 UTC to 2023-01-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-23 00:00:00 UTC to 2023-01-30 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-30 00:00:00 UTC to 2023-02-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-06 00:00:00 UTC to 2023-02-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-13 00:00:00 UTC to 2023-02-20 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-02-20 00:00:00 UTC to 2023-02-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-27 00:00:00 UTC to 2023-03-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsFeb 272023-03-06 00:00:00 UTC to 2023-03-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-03-13 00:00:00 UTC to 2023-03-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-03-20 00:00:00 UTC to 2023-03-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-03-27 00:00:00 UTC to 2023-04-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-03 00:00:00 UTC to 2023-04-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-10 00:00:00 UTC to 2023-04-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-17 00:00:00 UTC to 2023-04-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-24 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-01 00:00:00 UTC to 2023-05-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-05-08 00:00:00 UTC to 2023-05-15 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMay 8

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Dec 192022-12-19 00:00:00 UTC2022-12-26 00:00:00 UTC110
Dec 262022-12-26 00:00:00 UTC2023-01-02 00:00:00 UTC211
Jan 22023-01-02 00:00:00 UTC2023-01-09 00:00:00 UTC000
Jan 92023-01-09 00:00:00 UTC2023-01-16 00:00:00 UTC101
Jan 162023-01-16 00:00:00 UTC2023-01-23 00:00:00 UTC000
Jan 232023-01-23 00:00:00 UTC2023-01-30 00:00:00 UTC000
Jan 302023-01-30 00:00:00 UTC2023-02-06 00:00:00 UTC000
Feb 62023-02-06 00:00:00 UTC2023-02-13 00:00:00 UTC000
Feb 132023-02-13 00:00:00 UTC2023-02-20 00:00:00 UTC101
Feb 202023-02-20 00:00:00 UTC2023-02-27 00:00:00 UTC000
Feb 272023-02-27 00:00:00 UTC2023-03-06 00:00:00 UTC000
Mar 62023-03-06 00:00:00 UTC2023-03-13 00:00:00 UTC000
Mar 132023-03-13 00:00:00 UTC2023-03-20 00:00:00 UTC000
Mar 202023-03-20 00:00:00 UTC2023-03-27 00:00:00 UTC000
Mar 272023-03-27 00:00:00 UTC2023-04-03 00:00:00 UTC000
Apr 32023-04-03 00:00:00 UTC2023-04-10 00:00:00 UTC000
Apr 102023-04-10 00:00:00 UTC2023-04-17 00:00:00 UTC000
Apr 172023-04-17 00:00:00 UTC2023-04-24 00:00:00 UTC000
Apr 242023-04-24 00:00:00 UTC2023-05-01 00:00:00 UTC000
May 12023-05-01 00:00:00 UTC2023-05-08 00:00:00 UTC000
May 82023-05-08 00:00:00 UTC2023-05-15 00:00:00 UTC101

What Hacker News says

6 sources
Introducing three new open-source PaLM models trained at a context length of 8k on C4. Open-sourcing LLMs is a necessity for the fair and equitable democratization of AI. The models of sizes 150m, 410m, and 1b are available to download and use here: https://github.com/conceptofmind/PaLM If you have any questions about the models or training be sure to reach out and ask! I will try to respond promptly. https://twitter.com/EnricoShippole/status/165559930145459404... The models are also compatible with many of Lucidrain's popular repositories such as Toolformer-pytorch, PaLM-rlhf-pytorch, and PaLM-pytorch. Please be sure to sponsor and help support Phil's great work: https://github.com/lucidrains/PaLM-rlhf-pytorch You can find the weights on Hugging Face if you prefer to download the PyTorch .pt files from there instead: https://huggingface.
Read the rest
co/conceptofmind/palm-1b All of the C4 data has been pre-tokenized with the GPTNEOX tokenizer and blocked at sequence lengths of 8192. This will help to save you the large cost of preprocessing data. The datasets are available on Hugging Face. An example chunk can be found here: https://huggingface.co/datasets/conceptofmind/c4_0-to-20_neo... The original unprocessed C4 dataset used can be found on the Hugging Face hub here: https://huggingface.co/datasets/c4 If you would like to preprocess your own dataset for training there is a dataset builder script provided. This uses Hugging Face datasets to efficiently map, tokenize, and block the data: https://github.com/conceptofmind/PaLM/blob/main/build_datase... A distributed training script is provided so that you may train or fine-tune your own PaLM models using Hugging Face accelerate. More information and experiments about the training will be detailed in the repository: https://github.com/conceptofmind/PaLM/blob/main/train_distri... The models were trained with Flash Attention, Xpos Rotary Embeddings for better length extrapolation, and multi-query single-key-value attention for more efficient decoding: https://arxiv.org/abs/2205.14135 Further instruction-tuning will be done on the new FLAN datasets we have released. https://huggingface.co/datasets/conceptofmind/cot_submix_ori... You can find his twitter here: https://twitter.com/ShayneRedford A basic inference script was provided in the repository which you can play around with. You may want to experiment with hyperparameters in order to get generations of varying quality. Changing a variable such as temperature matters a lot: https://github.com/conceptofmind/PaLM/blob/main/inference.py Different inference optimizations such as Flash Attention, Hidet, and Torch compile are used. You can read more about the Hidet compiler and project here: https://pytorch.org/blog/introducing-hidet/ Our work on Toolformer, PaLM, and related projects is all thanks to the generous sponsorship by CarperAI and StabilityAI: https://github.com/CarperAI This is not an official Google or StabilityAI product.
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