The biggest deal with this isn't the published lora adapter (which seems limited to llama 7b), but the cleaned training data, which is likely better than the previous data sets used to train the alpaca-inspired loras that have been publicly released so far.

https://arxiv.org/pdf/2106.09685.pdf

Not “on top” but more “in parallel” if I understand correctly

With the model's weights open to people, people can do interesting generative stuff. However, it's still hard to train the model to do new things: training large language models is famously expensive because of both their raw size and their structure. Enter...

LoRA is a "low rank adaptation" technique for training large language models, fairly recently published by Microsoft (https://github.com/microsoft/LoRA). In brief, the technique assumes that fine-tuning a model really just involves tweaks to the model parameters that are "small" in some sense, and through math this algorithm confines the fine-tuning to just the small adjustment weights. Rather than asking an ordinary person to re-train 7 billion or 11 billion or 65 billion parameters, LoRA lets users fine-tune a model with about three orders of magnitude fewer adjustment parameters.

Combine these two – publicly-available language model weights and a way to fine tune it – and you get work like the story here, where the language model is turned into something a lot like ChatGPT that can run on a consumer-grade laptop.

I don't think you can compare one LLMs weights to another directly, because the weights are a product of the LLM. In theory (I don't know actually) llama and chatGPT may be using different source datasets so you can't compare them like for like.

And then people being scared to even download said models because of "OMG IP!"

The asymmetry of power (and dare I say, domestication) is astounding.

at this point, their not even complaining about it must mean that they accept the data is public now

(Except maybe companies that have access to vast amounts of training data with an explicit license, e.g. because the content is created by their users rather than just scraped from the web?)

For them to make the argument in court that database rights over the database of training data mean they have rights over the model too, they'd need to argue that the model is a derivative work training data. And then it'd mean their model is also a derived work from all the billions of works they scraped to get that data set. It would destroy the business model of the OpenAIs of the world, there is no chance they try to argue this in court.

This doesn't follow at all.

They can argue they used that work under fair-use and/or that their work was transformative. This is a fairly clear extension of arguments used by search engines that indexing and displaying summaries is not copyright violation and these arguments have been accepted by courts in most circumstances.

The fair use case is much harder to make here than for search engines since the model will be directly competing with the content creators. And again, how could e.g. OpenAI simultaneously claim that their use of the original content to train the model, and then subsequent use the model and the model outputs, while simultaneously claiming that the model could not be used without infringing their DB rights? You can argue fair use for both or neither; trying to argue it for just one of my the two is just incoherent.

And everyone building models needs free access to the training data way more than they need copyright as a means to protect the model.

I suggest https://arxiv.org/abs/2303.15715 for a complete overview.

LLaMA: https://ai.facebook.com/blog/large-language-model-llama-meta...

Both have been the subjects of numerous HN posts in the last month.

LoRA, or Low-Rank Adaptation of Large Language Models, lets people fine tune a LLM (making it perform better for a particular application) using vastly less resources. Paper: https://arxiv.org/pdf/2106.09685.pdf

lora: efficient way of fine-tuning a model like llama, where instead of recreating an entire model, you're keeping the base model and generating a fine-tunings file to apply on top of it.

toaster: any machine with like 4GB of RAM available to fit the model

But AI workers mainly develop and use jargon because it is an easy and natural way to consolidate concepts.

Sure, there is a kind of conspiracy caused by publish or perish. Researchers may use jargon to make their work harder to reject on review; laborious speech and jargon can make statements sound more profound. However, no technical field is immune to this. We'll need to systematically change science before we can eliminate that problem.

Until we manage that, if you care about the concepts enough to want to understand them before there are good plain speech descriptions, just pop the jargon into google scholar and skim read a few papers, and you're good to go. If you don't care about the concepts that much, then don't worry about the jargon. The important concepts will get their own non-technical explanations in time.

As it stands, AI jargon is not that bad. It tends to be pretty fair and easy to understand, compared to jargon in, say, biochemistry or higher math.

LLM assistants is genuinely just moving _very_ fast, so if you don’t pay attention every day you just miss things.

I’m just enjoying having my interactive rubber-duck tbh

Like how SMS is now a thing used for all sorts of little stuff, but nobody thinks much about it.

Also, building websites that don't store user data at all. Everything is kept in browser storage. You could say that the chat-gpt interfaces people are building now are web3, because they don't store your api keys, nor your converstation history.

Second part was decentralising as much as possible. Decentralised domain-name systems (ENS), storage, hosting, and money of course. So that you own your data, and your identity.

The last time I checked, the decentralised storage and hosting were the most difficult to solve. That is - we have torrents of course, but if you wanted to pay decentralised web to host and run your scripts indefinitely, it was not feasible.

1. Identify the jargon terms you don't understand

2. Lookup papers that introduce the jargon terms

3. Skim-read the paper to get the gist of the jargon

If you don't want to do this, then you don't have to feel uneducated. You can simply choose to feel like your time is more important than skimming a dozen AI papers a week.

But for example, here's what I did to understand the parent comment:

1. I had no idea what lora is or how it relates to alpaca.

2. I looked up https://github.com/tloen/alpaca-lora

3. I read the abstract of the Lora paper: https://arxiv.org/pdf/2106.09685.pdf https://github.com/tloen/alpaca-lora

4. Now I know that Lora is just a way of using low rank matrices to reduce finetuning difficulty by a factor of like 10,000 or something ridiculous

5. Since I don't actually care about /how/ Lora does this, that's all I need to know.

6. TLDR; Lora is a way to fine-tune models like Llama while only touching a small fraction of the weights.

You can do this with any jargon term at all. Sure, I introduced more jargon in step 4 - low rank matrices. But if you need to, you can use the same trick again to learn about those. Eventually you'll ground yourself on basic college level linear algebra, which if you don't know, again you should learn.

The sooner you evolve this "dejargonizing" instinct rather than blocking yourself when you see new jargon, the less overwhelmed and uneducated you will feel.

Or, you know, you could ask ChatGPT to explain it to you... Granted the term was coined 2021>=. Even if it wasn't but the paper is less than 32k tokens... 0.6c for the answer doesn't seem all that steep.

edit: grammar

It works astoundingly well with poorly written technical manuals. Looking at you, CMake reference manual O_O. It also helps translate unix man pages from Neckbeardese into clean and modern speech.

With science papers it's a bit more work. You must copy section by section into GPT4, despite the increased token limit.

But sure. Here's how it can work:

1. Copy relevant sections of the paper

2. As questions about the jargon:

"Explain ____ like I'm 5. What is ____ useful for? Why do we even need it?"

"Ah, now I understand _____. But I'm still confused about _____. Why do you mean when you say _____?"

"I'm starting to get it. One final question. What does it mean when ______?"

"I am now enlightened. Please lay down a sick beat and perform the Understanding Dance with me. Dances"

This actually works surprisingly well.

What you get is a teacher that never tires, is infinitely patient, has infinite time, doesn't limit questions, doesn't judge you, really listens and has broad, multidisciplinary knowledge that correct-ish (for when it's needed). I've recently read somewhere that Stanford (?) has almost as many admin workers as they do students. Seems to me that this is a really bad time to be that bloated. Makes you wonder what you really spend your money on, is it worth it (yeah, I know, it's not just education that you get in return) and if you can get the same-ish effect for a lot cheaper and on your timetable.

Not that the models or field, now, are in a state that would produce a good teaching experience. I can however imagine a future not so distant that this would be possible. Recently on a whim I've asked it to produce a options trading curriculum for me. It did a wonderful job. I wouldn't trust it if I didn't know a little bit myself about the subject before but I came off really impressed.

This text discusses various studies and advancements in the field of natural language processing (NLP) and machine learning. One study focuses on parameter-efficient transfer learning, and another examines the efficiency of adapter layers in NLP models. Further studies evaluate specific datasets for evaluating NLP models. The article proposes a method called LoRA (low rank adaptation) for adapting pre-trained neural network models to new tasks with fewer trainable parameters. LoRA allows for partial fine-tuning of pre-trained parameters and reduces VRAM usage. The article provides experimental evidence to support the claims that changing the rank of Delta W can affect the performance of models, and that LoRA outperforms other adaptation methods across different datasets. The authors propose LoRA as a more parameter-efficient approach to adapt pre-trained language models to multiple downstream applications.

That's not what's happening in the parent comment. They're talking about projects like

https://github.com/ZrrSkywalker/LLaMA-Adapter

https://github.com/microsoft/LoRA

https://github.com/tloen/alpaca-lora

and specifically the paper: https://arxiv.org/pdf/2106.09685.pdf

Lora is just a way to re-train a network for less effort. Before we had to fiddle with all the weights, but with Lora we're only touching 1 in every 10,000 weights.

The parent comment says GPT4all doesn't give us a way to train the full size Llama model using the new lora technique. We'll have to build that ourselves. But it does give us a very huge and very clean dataset to work with, which will aid us in the quest to create an open source chatGPT killer.

- NumPy basics pdf - first 2-3 chapters - Deep Learning with PyTorch by Voight Godoy [2] - first 2-3 chapters if you had experience with neural networks, or the whole of it if you didn't.

With the above, you will get the basics to understand this book about transformers, and the architecture of the models, and everything else, from this book:

- Natural Language Processing with Transformers: Building Language Applications with Hugging Face ( https://www.amazon.com/Natural-Language-Processing-Transform... ).

I took a weekend to go through the books in this order, and now I finally understand what people mean with all that jargon :)

1 - https://numpy.org/doc/1.18/numpy-user.pdf - 2 - https://www.amazon.com/Deep-Learning-PyTorch-Step-Step/dp/B0...

I am trying to be very selective about what to add in there and as concise as possible, but I would welcome any suggestions for format and additional content.

Big thanks for your glossary, find it very useful and overlapping with my personal obsidian notes, hope it continues to receive updates

Can this potentially do that?

Ideally I’d like to have it generate descriptions of large amounts of code but would rather not burn tokens and lose privacy via OpenAI api. But I’d gladly keep a high end GPU burning on such task , even if that was actually slightly more expensive.

Edit: To clarify I do this partially now on batches of code via openAI api currently. It’s around 1-3 cents for a typical 400-800 line source code file. And I don’t mean feeding the full code base in as a single input.

On gpu, processing the input prompt, even for huge prompts, is almost instant. Meaning, even if your prompt is huge, it will start generating new tokens after your prompt very quickly. On a rented A6000 gpu, using llama+lora 30b, you can use huge prompts and it will start giving a new output right away.

On cpu (i.e. the project llama.cpu), it takes a very, very long time to process the input prompt, before it begins to generate new tokens. Meaning, if you provide a huge copy/paste of code, it will take a long time to ingest all that input, before it begins outputting new tokens.

Once it finally starts outputting new tokens, the rate is surprisingly fast, not much slower than gpu.

I wish I knew the reason for this, but I'm not an expert :) I've just seen this in practice.

I’ve been using OpenAI API with chatblade and been giving it code in various languages and it’s quite surprising how well it describes the purpose and code implementation in english. The english description would be useful and relevant for developers trying to quickly familiarize themselves with a code base.

For a typical 400-600 line file it looks like it would cost around 1-3 cents per file. However loss of privacy isn’t so great.

How do you find output quality of llama + lora for such a task?

Code is research code I’d run it on.

Jokes aside, the limiting factor will be either a technique to pack all of the code into smaller tokens like semantic search (someone else will be able to comment on this as that's the limit of my understanding) or GPU memory for input tokens.

Buying a "high end GPU" isn't buying a 4090 or even two, it's 250k on a DGX unit and putting it in a datacentre. You will probably be able to find a service that would sign a confidentiality agreement and provide you with this service for less than 250k.

I haven't started to play with LLMs locally in anger yet, but I was under the impression that you could use a 4090 in combination with FlexGen to achieve this rather than having to buy special hardware?

But it is pretty cheap to rent something at vast.ai or whatever to get 40GB for a final run.

This is for inference, not training, and I’d do it on blocks of code with less than 4096 tokens. That’s what I have to do with gpt 3.5 turbo via api.

I can do what I want with the API and it is around 1-2 cents per batch of code I run through it, typically 400 lines of average code is around 4k tokens.

If you're trying to get codebase level introspection you might need to wait a bit for some of these techs to mature.

Exciting space and yea, learning heaps myself day by day.

In my opinion the code descriptions gpt 3.5 turbo has been spitting out for me are good quality and concise. I’d argue they are probably better than what many of the developers themselves would write, especially when english isn’t native for the developer.

Does anyone have a projection based on historical GPU cost reductions as to how long we have to wait until a DGX unit costs as little as a 4090 does today?

[0]https://github.com/THUDM/ChatGLM-6B/blob/main/README_en.md [1]https://huggingface.co/spaces/multimodalart/ChatGLM-6B

It seems like working on locally runnable LLM that has been fine tuned to focus on code should be a high priority.

That said, there are some summarisation tasks I prefer to run locally even given the massive drop in quality (e.g. using Alpaca), both for privacy reasons and to keep myself up-to-date on local LLMs.

P.S. I'm discounting older local summarisation-specific networks, I have found local LLMs to be a jump in quality over them

The answer will be pretty damn good , especially with gpt 4, but it’s still good with gpt 3.5 turbo. Try it out yourself with whatever code you have.

This english description of code can then be searched against. That’s the primary practical application I’m thinking about doing currently.

We're headed to Elysium and that's dystopian.

(apparently there's some friction between those two!)

via https://github.com/antimatter15/alpaca.cpp/issues/124#issuec...

? (I have not tried this yet.)