David Hall @dlwh
Research Engineering Lead at @StanfordCRFM . Previously co-founder at Semantic Machines ⟶ MSFT. Lead developer of Levanter, Breeze. he/him @[email protected] linkedin.com/in/dlwhall Berkeley, CA Joined September 2007-
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For medicine, how do good, mid-sized, general LLMs (which may be partially trained on medical text) compare in performance to models built on medical resources like PubMed? We find that the general-purpose models now do better (Bolton, Xiong, et al. 2024) arxiv.org/abs/2404.15894
Final Update: One more magnitude of testing Sophia. We're talking model sizes in the B's, tokens in the T's. Sophia once again wins out. For me at least this is clear evidence that Sophia may be a replacement for Adam even in large scale runs.
Final Update: One more magnitude of testing Sophia. We're talking model sizes in the B's, tokens in the T's. Sophia once again wins out. For me at least this is clear evidence that Sophia may be a replacement for Adam even in large scale runs. https://t.co/1l8XKBswaU
Update: As promised, one order of magnitude more compute testing AdamW vs. Sophia. This time applied to two different transformer architectures. Sophia is clearly the winner again. Will run one more ablation with another order of magnitude more compute to see if trend holds.
Update: As promised, one order of magnitude more compute testing AdamW vs. Sophia. This time applied to two different transformer architectures. Sophia is clearly the winner again. Will run one more ablation with another order of magnitude more compute to see if trend holds. https://t.co/plVdjoHr2K
Thanks to @dlwh for helping me get ramped up on the framework. Haliax, the underlying NN framework for Levanter, is a joy to write code in. I started contributing knowing the bare minimal Jax and found it pretty straightforward coming from PyTorch! x.com/dlwh/status/16…
Thanks to @dlwh for helping me get ramped up on the framework. Haliax, the underlying NN framework for Levanter, is a joy to write code in. I started contributing knowing the bare minimal Jax and found it pretty straightforward coming from PyTorch! x.com/dlwh/status/16…
Levanter is made possible due to the hard work of the entire team: @dlwh @ivanzhouyq @itsvadams @_jasonw_sy Many thanks to our collaborators and supporters: Google’s JAX team, NVIDIA JAX team, Google’s TRC, Radium
@dlwh has been leading the effort at @StanfordCRFM on developing levanter, a production-grade framework for training foundation models that is legible, scalable, and reproducible. github.com/stanford-crfm/… Here’s why you should try it out for training your next model:
Percy Liang @percyliang
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(((ل()(ل() 'yoav))).. @yoavgo
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Delip Rao e/σ @deliprao
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(((ل()(ل() 'yoav))).. @yoavgo
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4K Followers 155 Following phd student @berkeley_ai https://t.co/ZNJawlrerS machine learning, neural networks.New Griffin paper is really interesting and contains a lot of implementation details arxiv.org/abs/2402.19427 . Implementation is in Pallas which is a Jax like frontend to Triton/TPU lowering. They show that Associative Scan is inherently worse than Linear Scan in this context.…
For medicine, how do good, mid-sized, general LLMs (which may be partially trained on medical text) compare in performance to models built on medical resources like PubMed? We find that the general-purpose models now do better (Bolton, Xiong, et al. 2024) arxiv.org/abs/2404.15894
There's a new bill, SB-1047 "Safe and Secure Innovation for Frontier Artificial Intelligence Models Act". I think it could do a great deal of harm to startups, American innovation, open source, and safety. So I've written a response to the authors: 🧵 answer.ai/posts/2024-04-…
Llama 3 degrades more than Llama 2 when quantized. Probably because Llama 3, trained on a record 15T tokens, captures extremely nuanced data relationships, utilizing even the minutest decimals in BF16 precision fully. Making it more sensitive to quantization degradation.…
@dlwh @andersonbcdefg @_jasonw_sy Nice Ethan Epperly's math was pretty interesting on the choice of vector, I switched to rademacher without testing so good to hear it can actually make a difference. Yeah share if Tengyu shares anything interesting, optimization can be pretty surprising sometimes
@dlwh @andersonbcdefg @_jasonw_sy Another interesting tidbit, not sure if you guys have seen this at all, I found a case where 2 hutchinson samples every 10 steps performed better than 1 every 5 steps. Just a single anecdote but still weird and shows that how the monte carlo steps are sampled matters
@dlwh @andersonbcdefg @_jasonw_sy The elucidation of how sophia needs momentum is interesting though
@andersonbcdefg @dlwh ...not as good results as sophia with explicit lr schedule though it seems
@andersonbcdefg @dlwh Sure enough just did an experiment where I left sophia's momentum in at 0.95 and put schedulefree on top of that and got good results, so seems sophia at least needs momentum to work, and schedulefree seems to still work alongside momentum, will post example hopefully later today
@andersonbcdefg Was messing around with the combo again this morning, it just fails badly. After thinking, it might be that sophia-h needs the sequence of hutchinson steps to create the accurate preconditioner, whereas in schedule free `y` stems from the average `x` every time. @dlwh
@andersonbcdefg @dlwh Also kind of explains why sophia benefits from higher momentum, something about the stochastic momentum sequence helps create an accurate preconditioner through the hutchinson monte carlo samples, this is eradicated with not enough momentum (or replacing mom with schedulefree)
Putting together all the experiments, scaling looks very healthy. We're slightly more than 1.2x more efficient with Sophia vs. AdamW at scale. Doesn't get close to 2x the original paper stated but also original paper used a lot less compute. Seems like free lunch!
Our Reza is sharing how to optimize big MoE kernels !
1/4 Have you wondered how to optimize sys-perf for training Arctic-like models (MoE arch)? Let’s dive in! Our first technique: custom fused kernels. By crafting these kernels, we streamline irregular and sparse operators, boosting efficiency. #SnowflakeArctic #SystemOptimization
There is a really nice community of researchers developing transformer alternatives. Want to highlight these impressive folks. Simran Arora (@simran_s_arora), Chunting Zhou (@violet_zct), Dan Fu (@realDanFu), and Songlin Yang (@SonglinYang4)
@tamaybes but current models don’t allocate parameters to rotary embs! this means the Chinchilla D=20*N is skewed already for the actual param counts of most models, even if it held across datasets! If we disregarded the pos. encoding params the coefficients would change
@tamaybes a super-fun arcane historical detail: Gopher (and by extension Chinchilla) use Transformer-XL style position encodings. This means they spend 20B params (Gopher) and 5B params (Chinchilla) on just rel. position encoding!
Few terms in AI invoke more of a reaction than “open.” AI experts @jzemlin, @MitchellBaker, @percyliang & a16z's @AnjneyMidha discuss what it really means to be 'open,' the crucial role of open source in the future of AI & more on the AI + a16z Podcast. a16z.com/podcast/making…
HAI associate director and co-founder @chrmanning has a lifelong fascination with human language. His efforts to help computers learn, understand, and generate that language has made him an influential figure in NLP. (via @StanfordEng) stanford.io/3xTurBO
JUST IN: The 🇺🇸 FTC has banned non-compete agreements
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