Kyle Corbitt @corbtt
Currently building @OpenPipeAI. Formerly @ycombinator, @google. I am always down to go on a quest. Seattle, SF, Barcelona Joined September 2012-
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Pro tip: you can activate walking mode at any time for an immediate power up of +10 IQ.
Prompt engineering is still critical for GPT-5+ models, but the job changes completely. It isn't about random tricks like "I'll tip you $20." It's about methodically enumerating potential edge cases and explaining the expected behavior for each one. PMs gonna love it. 😁
Ok, initial results fine-tuning Phi 3 vs Mistral 7B are in. Seems pretty good for the parameter count, but not a huge game changer. For summarization dataset it failed to produce coherent output; need to figure out why. That's our smallest training dataset, could be related?
Ok, initial results fine-tuning Phi 3 vs Mistral 7B are in. Seems pretty good for the parameter count, but not a huge game changer. For summarization dataset it failed to produce coherent output; need to figure out why. That's our smallest training dataset, could be related? https://t.co/x40YlnUwpP
In a year you'll be able to directly prompt your social media feeds. "Yes, I know I spent 4 seconds looking at that picture. No, that doesn't mean I only want to see thirst traps for the next 3 days." Platforms should build this directly, or someone will build it on top.
We are building @OpenPipeAI on the following principles: 1. Frontier models will keep getting faster, cheaper and better. 2. The demand curve for intelligence is more elastic than the demand curve for *anything else*. Everything we do follows from those two core beliefs.
Assuming weights drop in the morning, @OpenPipeAI we will have support for Phi-3 live on-platform tomorrow. We'll also put it through our fine-tuning eval harness and let you know how well it tunes.
Assuming weights drop in the morning, @OpenPipeAI we will have support for Phi-3 live on-platform tomorrow. We'll also put it through our fine-tuning eval harness and let you know how well it tunes.
Today at @answerdotai we've got something new for you: FSDP/QDoRA. We've tested it with @AIatMeta Llama3 and the results blow away anything we've seen before. I believe that this combination is likely to create better task-specific models than anything else at any cost. 🧵
Seems like more folks are replicating our mediocre results: Llama 3 8B is only ~10% better than Mistral 7B, while being 10% larger. @Teknium1 speculates we're hitting a saturation point in small LLM perf. 😢
Seems like more folks are replicating our mediocre results: Llama 3 8B is only ~10% better than Mistral 7B, while being 10% larger. @Teknium1 speculates we're hitting a saturation point in small LLM perf. 😢 https://t.co/XH7Duwuxri
Trans-buddhism: a neo-religious movement popular in the late anthropocentric period. Its central dogma was that any human who developed sufficiently strong vibes in life would reincarnate as a computer after death.
Trans-buddhism: a neo-religious movement popular in the late anthropocentric period. Its central dogma was that any human who developed sufficiently strong vibes in life would reincarnate as a computer after death.
Tbh Mixtral compares pretty favorably to Llama 3 70B on the cost/perf curve. I expect it'll continue seeing a lot of use. I also expect some Llama 3 8B MoE merge-ups to absolutely crush.
Does anyone have a real use cases for requesting multiple completion choices? (ie. requesting `n` > 1 in the OpenAI API)? It complicates the API surface and I'm unconvinced it's actually, like, useful.
Ok took a minute to get efficient LoRA serving working but we've got end-to-end fine-tuning, serving and evals set up with Llama 3 at @OpenPipeAI. Come check it out!
Ok, initial results on our Llama 3 8B vs Mistral 7B benchmark are in and look... kinda underwhelming? It overperforms a bit on summarization but otherwise results are similar. Going to keep playing with hparams.
Ok, initial results on our Llama 3 8B vs Mistral 7B benchmark are in and look... kinda underwhelming? It overperforms a bit on summarization but otherwise results are similar. Going to keep playing with hparams. https://t.co/wtqIPnD5ty
Ok I promise I'll have some prettier graphs for you soon, but wanted to get the first set of results out ASAP. When fine-tuned on our smallest test dataset, Llama 3 outperforms Mistral 7B 56% of the time. Will have more results in a couple of hours when our larger/more realistic…
This is how foundation model companies build a data flywheel that makes it hard to keep up. You'd better believe that OpenAI is using GPT-5 to filter and synthesize training data for GPT-6 already.
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44K Followers 682 Following Maybe the real AGI was the friends we made along the way. @xAIThis is the prompt they are using for Llama-3 function calling (seems to work well even though it's not specifically fine tuned for that): github.com/ShishirPatil/g…
📊Delighted to welcome Command-R-Plus, Llama-3, and and Gemini-Pro-1.5 into the Berkeley Function Calling Leaderboard. Check out how they stack up across different categories, P95 latency, and costs at gorilla.cs.berkeley.edu/leaderboard.ht… Congratulations to @cohere, @AIatMeta, and…
Unsloth has surpassed 500K+ monthly model downloads on @huggingface! 🥳 🦥 Thanks for the support! 🩷 See our collection of 4bit models + more: huggingface.co/unsloth
@danielhanchen @mattshumer_ @corbtt @huggingface Yes yes yes yes yes yes!!!!!!!🤗🤗🤗🤗
@mattshumer_ @corbtt @huggingface I haven't yet gotten to dataset prep stuff with Unsloth, but if it helps, I can create a custom collator to train on completions natively with Unsloth :)
Clearly expressing yourself is the work
Prompt engineering is still critical for GPT-5+ models, but the job changes completely. It isn't about random tricks like "I'll tip you $20." It's about methodically enumerating potential edge cases and explaining the expected behavior for each one. PMs gonna love it. 😁
David or @OpenPipeAI showing a side project he build on gpt3 couple years ago now resurrected with all the latest #aitinkerers #seattle
Groundbreaking claims here beyond anything anyone could have expected 😲
🚨BREAKING🚨 CEO of company states the next release of their product will be an improvement over the previous generation and the next one from there will also be another improvement.
@corbtt this is the most dystopian thing i’ve read all day
@corbtt @huggingface I almost always use (and LOVE) Axolotl... but wanted to try Unsloth to try to extend L3 70B's ctxlen w/ just one A100.
New paper from Norway: Banning smartphones in school - significantly decreased doctors visits for psychological symptoms and diseases among girls - reduced bullying among both genders - improved girls’ GPA and attendance rates - largest effect sizes were among the poorest kids
What huge AI advancement are you convinced is coming in the next 12 months? (leave a comment below with your guess 🤔 ) Rapid Fire Questions with @mattshumer_ , @alliekmiller and @luisceze Sign up today 👉 octo.ai
@corbtt @OpenPipeAI 100% I was telling this to someone else today. There is literally un-limited demand for intelligence as the intelligence to cost ratio climbs higher and higher.
Hadn't realized EU AI Act starts regulating models when they cost roughly ~$7M compared to the USA (Biden EO) at ~$70M. Given epochai.org/blog/trends-in… found flop/s per $ doubles every 2.5, it seems the regulation threshold will increasing catch more models, even those not SOTA.
Key diff between EU and US regulation= thresholds, where EU does major tests at 10^25+ flops and WH at 10^26 flops. But what does this mean in terms of dollars? It means $7m vs $70m, based on a napkin analysis. This is a big deal!
@corbtt @OpenPipeAI Can't wait to see it! One word of caution is that our model does not like log-likelihood benchmarks, because it behaves really differently from models with "standard training". Generation based evals (like few shots) is more appropriate for the phi models.
Sigh, hyperparameter tuning on llama3 is a bit of a challenge (for codegen in particular) Can't be too high (changes a highly capable model way too much), and can't be too low (the model doesn't learn new tasks well enough)
i wonder if REFT / RepEng really is the future 🤯 cc @voooooogel
Sigh, hyperparameter tuning on llama3 is a bit of a challenge (for codegen in particular) Can't be too high (changes a highly capable model way too much), and can't be too low (the model doesn't learn new tasks well enough)
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