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google-research
t5x
Python2023–2026
google-research / t5x

t5x

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5Distinct people
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3KGitHub stars
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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

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HN mentions by year5 dated mentions

Observed mentions: 2023-10-09 19:35:47 UTC to 2026-08-10 20:20:55 UTC.

01232023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments20232024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20242025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

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20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC303
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC101
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

5 sources
Disclaimer, I work on Gemma and open models at Deepmind and the opinions here are my own There were open models from EleutherAI (GPT-Neo), Google Brain (T5X, Bert), and HuggingFace was promoting open models (and others doing open work I haven't listed here) all prior to 2023 and the big Chatgpt moment. https://github.com/EleutherAI/gpt-neo/releases https://github.com/google-research/bert https://github.com/google-research/t5x If you're learning about AI models it's still worthwhile to review these models and codebases because they continue to be the basis of the technology that's being produced today! It'll give you a good perspective of how things have changed, similar to say learning about propeller planes before moving onto modern jet engines.
In Mark Zuckerberg attacks 'closed' AI rivals as Meta returns to open models
What are people's thoughts on how this compares to: - EasyLM [1] - Levanter [2] - T5X [3] - and more? [1]: https://github.com/young-geng/EasyLM [2]: https://github.com/stanford-crfm/levanter [3]: https://github.com/google-research/t5x Asking because I have worked extensively on training a large model on a TPU cluster, and started with Levanter, then tried MaxText, and finally ended up on EasyLM. My thoughts are: - Levanter is well intentioned but is unproven and lacking in features. For instance, their sharding is odd in that it requires embedding dimension to be a multiple of the number of devices, so I can't test using a model with embedding dimension 768 on a 512-device pod. Lost confidence in Levanter after finding some glaring correctness bugs (and helping get them fixed). Also, while I'm a huge fan of Equinox's approach, it's sadly
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underdeveloped (for instance, there's no way to specify non-default weight initialization strategies without manually doing model surgery to set weights). - MaxText was just very difficult to work with. We felt like we were fighting against it every time we needed to change something because we would be digging through numerous needless layers of abstraction. My favorite was after one long day of debugging, I found a function who's only purpose was to pass its arguments to another function untouched; this function's only purpose was to pass its arguments untouched to a new, third function, that then slightly changed them and passed them to a fourth function that did the work. - EasyLM is, as the name says, easy. But on a deeper dive, the sharding functionality seems to be underdeveloped. What they call "FSDP" is not necessarily true FSDP, it's literally just a certain axis that the JAX mesh is being sharded around that happens to shard some data axes and some model weight axes. I'm still searching for a "perfect" JAX LLM codebase - any pointers?
In Maxtext: A simple, performant and scalable Jax LLM
> Are you using a normal training script i.e. "continued pretraining" on ALL parameters with just document fragments rather than input output pairs? Yes, this one. > do you make a custom dataset that has qa pairs about that particular knowledgebase? This one. Once you have a checkpoint w knowledge, it makes sense to finetune. You can use either LORA or PEFT. We do it depending on the case. (some orgs have like millions of tokens and i am not that confident that PEFT). LoRA with raw document text may not work, haven't tried that. Google has a good example of training scripts here: https://github.com/google-research/t5x (under training. and then finetuning). I like this one. Facebook Research also has a few on their repo. If you are just looking to scrape by, I would suggest just do what they tell you to do. You can offer suggestions, but b
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etter let them take the call. A lot of fluff, a lot of chatter online, so everyone is figuring out stuff.
In Mixtral of experts

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