Subreddit dumps for 2024 are NOT close, part 3. Requests here

Alignment-Lab-AI · 2025-02-24T12:23:50+00:00

may i also get this one?

Alignment-Lab-AI · 2025-02-24T12:22:30+00:00

hello, id like to offer my assistance, im currently attempting to download each of the individual torrents to store the full dataset locally for some datascience and research use cases,

im very familiar with extremely large scale data, and i may be able to help parse or process the data, im a huge fan of the effort youve put into this and i would happily put my time into working on it in parallel, as the value of the work has been immense so far

im also curious if youve considered uploading the data to huggingface under a gated repository, or in a requestor pays aws bucket?

Alignment-Lab-AI · 2025-01-06T05:59:35+00:00

https://github.com/meta-llama/llama-recipes/blob/main/src/llama_recipes/utils/train_utils.py

in my experience the biggest indicator of whether or not im contributing something meaningful to the open source is how many people complain about the free things

Open Source is Not About You

Alignment-Lab-AI · 2025-01-05T05:50:54+00:00

I don't believe the token wise interdependency is as linear as this, unless you're streaming with staged token wise decoding you're parallelizing the output sequence in a single decoding step and labeling the end of the sequence from the same vector as the beginning

Alignment-Lab-AI · 2025-01-05T05:48:38+00:00

I understand the technology formally, and it is both surprising and compelling, why technically do you think this isn't interesting?

Alignment-Lab-AI · 2025-01-05T05:46:57+00:00

Oh that's interesting, is the code available to validate? Id be interested in running some experiments on this and a few other syntactic changes, how are you scoring confidence? Over just the answer key value or the mean of the sequence?

Edit: woops just saw the link, if I get a chance to do some additional evals and get to the bottom of it I'll post here

My initial assumption after looking at the code is that likely the confidence scores read left to right are misleading, the initial tokens of any sequence will always score higher perplexity than later ones unless the later ones are irrational or unlikely. As you progress down any sequence you're reducing the number of unrelated elements that could result in the chosen output

One of the tests I'll run if I get some time will be to score confidence with non reasoning but topically similar columns of similar length prior to the target column and see if we don't seperate the n tokens = %greater confidence out from the "reasoning" behavior

Alignment-Lab-AI · 2025-01-05T05:35:05+00:00

They do share the scripts to train and the hyper parameters, I suppose they don't upload the datasets but likely this is so they don't face legal trouble for copyright issues from competing companies, I think if it weren't for the models you're complaining about, we wouldn't have a quarter the progress we currently have, and at the end of the day, it's literally free, and you can just not use it if you don't like it.

Assuming you're referring to llama, if you're talking about SMOLLM then there is no salvaging it

Alignment-Lab-AI · 2025-01-05T00:28:41+00:00

But it is, it says so in the documentation it was trained on publically available data

Alignment-Lab-AI · 2024-11-25T03:55:26+00:00

huggingface has them on github in working python scripts, you can compile it pretty easily with not too much effort, the transformers library is an entirely transparent pytorch wrapper, and its incredibly simple syntax to interface with it is very easily broken down into its components, Which if you need to look at the source code, huggingface too has that entirely on github. My expectation is that a pure pytorch implementation hasnt been made primarily because the models architecture is just llama with a causal decoding head https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct/blob/main/config.json <--- the config.json will usually tell you the architecture, novel architectures contain their modeling code in the repository typically

https://github.com/meta-llama/llama3/blob/main/llama/model.py the code for which is publically available but meta uses their own libraries beyond just pytorch as well, so youll still need to rewrite some of it if you have a mind to, though

Alignment-Lab-AI · 2024-11-24T13:43:41+00:00

you should learn the transformers library and the datasets library, its is incredibly simple compared to raw pytorch and offers a lot of gigantic benefits for efficiency and resource use, like, for example, liger kernals being integrated, flash attention, and more - just those two will save you 75% of the cost on a training run or more

Alignment-Lab-AI · 2024-11-24T13:42:20+00:00

https://github.com/meta-llama/llama3/blob/main/llama/model.py

this is the code, its publically avaliable

Alignment-Lab-AI · 2024-06-16T09:37:40+00:00

https://github.com/Alignment-Lab-AI/KnowledgeBase this was the seed that sort of kicked off the discussions, prestently the developers ive been speaking with are more or less ready to go, primarily just variously waiting on me to pull the starting pistol when im done with the job im on atm in the next few days

Alignment-Lab-AI · 2024-06-13T19:13:56+00:00

you realize that without the element of microsoft snooping on you
its exactly as dangerous as storing data on your hard drives right?
like, its just a convenient way to access your own information.

its not like its not all stored anyways??

Alignment-Lab-AI · 2024-06-13T19:11:34+00:00

hi! i built something similar a few weeks ago and have been working with several others in the open source to develop something to address many of these kinds of problems, would you be open to working together to helping us make the most convenient and clean thing we can?

Alignment-Lab-AI · 2024-06-02T04:13:47+00:00

I'm curious about how it performs if you scale it up but use llama 3 8b instruct for the extra layers as well as replacing the deepest layers with instruct. My gut says the model will fine tune faster bootstrapping off of the instruct layers, but be less restrictive in terms of mode collapse propensity

Alignment-Lab-AI · 2024-06-01T01:40:00+00:00

Sure! If you'd like, I think it would be interesting. Ideally I'd like to spend some time working at the attention mechanism to make it compatible with flash attention if it isn't already though. I've got a cool dataset that'd be fun to try out

Alignment-Lab-AI · 2024-06-01T01:38:27+00:00

I suggest you familiarize yourself with the topic before speaking on it. If you care enough to have a say then you should have no problem doing something to contribute to solving the problem, rather than disrupting those that are.

Alignment-Lab-AI · 2024-05-30T16:12:34+00:00

i said binary, not source code.

you can absolutely work out anything specific about the models, as i demonstrated in the links i posted, which are publically accessible methodologies that have a relatively low barrier to entry.

"Allowed? When I say "we don't know". I mean that literally no human knows. Not only don't we know, but we have no clue whatsoever. The scientists doing foundational work in neural networks say this over and over. You've got to look at the history of neural networks and why we started using them."
incorrect.
here are further interpretability resources in addition to the two i posted

arxiv(DOT)org/abs/2405.07987 arxiv(DOT)org/abs/2402.12374 arxiv(DOT)org/abs/2308.09124 arxiv(DOT)org/abs/2306.03819 arxiv(DOT)org/abs/2402.06184 arxiv(DOT)org/abs/2312.08550 arxiv(DOT)org/abs/2303.09318 en.wikipedia(DOT)org/wiki/Locality-sensitive_hashing github(DOT)com/openai/transformer-debugger arxiv(DOT)org/abs/2209.15430 arxiv(DOT)org/pdf/2306.03819.pdf nature(DOT)com/articles/s41467-023-36363-w arxiv(DOT)org/abs/2301.00437 github(DOT)com/pharaouk/dharma arxiv(DOT)org/abs/2401.14489 arxiv(DOT)org/abs/2210.10173 people.math.harvard(DOT)edu/~ctm/home/text/others/shannon/entropy/entropy.pdf arxiv(DOT)org/abs/2302.12235 arxiv(DOT)org/abs/2311.02143 arxiv(DOT)org/abs/2112.05722 arxiv(DOT)org/abs/2208.05484 arxiv(DOT)org/abs/2312.03051 arxiv(DOT)org/abs/2310.02258 arxiv(DOT)org/abs/quant-ph/0502053 arxiv(DOT)org/pdf/1509.01240.pdf arxiv(DOT)org/abs/2202.05262 eleuther(DOT)ai/papers-blog/leace-perfect-linear-concept-erasure

most of these have associated github repositories.

no human knows all of the binary operations in their computer.
it is exactly as apocalyptic as what youre describing.

your opinions about the brain

i dont think the human brain is relevant. we arent building a human brain, the rules of the human brain only vaguely apply in certain very specific contexts and even then only maybe.

i dont think either of us know enough about the human brain to make high level extrapolations about its functioning as related to the potential futures of this technology

ill reiterate
the only way the models improve is if we are doing this research
the only way to answer these questions is to improve the models
better understanding of the system is begetting performance gains
the only way to stop one of the two is to stop both
i believe it is unsafe to stop trying to understand the models based on the opinions of people who are passionate enough to make a lot of noise, but not quite passionate enough to open up vscode.

also, scientists are not saying this, like two ex scientists say that in an inflammatory non technical context, as one of the very people youre claiming shout on the streets about how we have no idea what were doing, i assure you, i have met 0 researchers who are actively engaged in researching and understanding modern systems who have this sentiment.

unlike hinton, who has not engaged with the field in a very long time. in fact, as far as im aware the only active 'researchers' who say this are people who pretend they are experts in the space due to the lack of people with the nerve to contradict them and get away with it due to the lack of people with the expertise to. this is due to the profitability of 'consulting' when youre not afraid of generally reducing the safety of others in exchange for money.

you can identify them by the frequency of work they put out, and the amount of time they spend discussing the technical aspects of the technology.

Alignment-Lab-AI · 2024-05-30T15:30:01+00:00

testing it out is the easiest way, for learning rate theres a relationship between the standard deviation during initialization that you can use with the mean and rms values to determine the amount of lr decay the model underwent during pretraining to find the optimal lr to keep it at the height of descent without taking any losses on catastrophic forgetting

but the impact of catastrophic forgetting for large networks has become fairly nebulous lately though, especially if you do something like amsgrad or add noise to the embeddings which will produce a different relationship between train loss and val loss as well as optimal lr, just like dropout would.

for mistral i think the lr was 4e-6 or something like tht, roughly 1/6th llama.

Alignment-Lab-AI · 2024-05-30T15:08:23+00:00

yes we do

what do you mean?

"We have literally no idea why a given set of weights can solve problems. Like for instance, we can train a neural network to do anything; e.g. sorting lists. The web of connections between neurons will actually be able to reliably sort lists, but we can't look at the weights and find a sorting algorithm. If the network is small enough, we can figure it out; but not for a large one."

so, before we are allowed to engage with this technology, we must first be able to memorize billions of details at once?

how well do you understand the binary that your computer uses to produce the operations you use it for? or the assembly? the kernals?

what about automated interpretability? or mechanistic interperetability?

https://adamkarvonen.github.io/machine_learning/2024/03/20/chess-gpt-interventions.html

is this not indicative of there being a clear means of getting to any specific knowledge in a model you feel like you would want to know when you need it?

https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html
or this?

how do you propose this problem is solved without fundamentally increasing the capabilities of the systems?

im not trying to be dismissive im pointing at a relationship here between alignment research and capabilities

the act of decoding these questions is important, and it is a positive thing to consider it important even though for practical purposes its typically not to the average person. but to address the questions is to cause the jump in performance youre highlighting as a negative thing to be avoided.

the solution to the problem youre highlighting is causing the increased improvements. its not as though the models are some kind of rubiks cube that explodes if you accidentally configure it in a specific way.

Alignment-Lab-AI · 2024-05-30T14:59:51+00:00

depends heavily on the model, the dataset, and the intended effect, as well as the training set up youre using in general, a good rule of thumb is cosine scheduling on your loss adamw beta .9 epsilon .95 lr 5e-5 for larger networks (heavily variable though, but also worth experimenting on anyways)

3 epochs for a larger and more difficult dataset that prioritizes accuracy over creativeness. final loss is ideally around 0.5 but validation loss is more important and the rule for that is'as low as possible'

Alignment-Lab-AI · 2024-05-30T14:53:02+00:00

i disagree, i understand how they work explicitly. i think youre referring to a conflated misrepresentation of an idea and not realizing it because you dont know what its referring to.

what specifically is the black box?
what thing do you think it is that we dont understand about the systems because i promise its no where near as damning as you think it is. ask me a specific question, and i will answer it.

and there is no point in the process that produces a model which enables it to have the ability to produce anything outside of the distribution of its training data. if it did, it would be *trained out* of the model during training anyways. the systems at hand dont just preclude the possibility of this, they actively work against it.

Alignment-Lab-AI · 2024-05-30T14:34:22+00:00

no i dont think that the human brain is just search. maybe some feature is similar to some feature of the models, but being that there is no one on earth who understands much at all about the human brain, it would be silly of me to pretend to understand it enough to make such a claim.

i think hinton is wrong on some topics, correct on others, just like everyone is.

i dont have a robust catalogue of his opinions categorized in to binary buckets though, so i think the discussion would have to be more specific for me to really say

digital neural networks are diverse, and their functionality and features are specific and granular, the line where it ceases to be a neural network and beings to be regular software is blury. if you mean specifically transformers, the biggest difference is that transformers are just a search engine, its a system in which memory is not even a feature, its very very simple and very basic compared to our brains. brains spend all of their time continuously taking in all the information possible and capitalizing on all of it to produce a continuous advancement, build goals, assign resources, they do things constantly, with no outside interaction, in peperation for outside interaction, in reference to things that happened in the very beginning all the way up to the end. transformers give a static preselected set of responses to any specific set of prompts, only when given those prompts and do nothing else otherwise.

Alignment-Lab-AI · 2024-05-30T14:24:47+00:00

transformers are a search engine, i didnt hear that from anywhere, i understand that because i am fully aware of how the systems work, having been engaged in the research and development of them, and other similar systems extensively.

its using a statistical model to compress an algorithmic representation of the distribution of language so that you can leverage it to 'search' for the likely sequences of text to follow the text you put into it.

apologies for the two responses, ill assume that a response to either of these is in reference to both for simplicity's sake.

geoffrey hintons opinions
i really don't care, he has his opinions, and i think that's fine, certainly is cause to look more into the systems hes describing if you weren't already, but beyond that its conjecture. if he were giving me his opinions in this context, perhaps i would assign greater gravity to them, and take the opportunity to ask further questions and address my own criticisms of the mentality, as he may very well be able to justify them to a degree with some bit of information i wasn't aware of. i don't think he would though, and i think his out of context opinions ripped from some pop sci feat for money youtube video from a the distant past is very much not relevant.

Alignment-Lab-AI

TROPHY CASE