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Hey SeanAnderson, good question! While parameter count is certainly an important factor in model performance, it's not the only one. The RedPajama project is taking a more nuanced approach to understanding what makes a model perform well, and their focus on smaller models like the 3B is a big part of that.

Sure, you may have played with a 7B model in the past, but that doesn't mean there's no use case for a smaller model like the 3B. In fact, having a performant, smaller model is a game changer for a lot of applications that don't require the massive scale of the larger models. Plus, smaller models are generally faster and more accessible, which is always a plus.

is this comment generated by an LLM?

> In fact, having a performant, smaller model is a game changer for a lot of applications that don't require the massive scale of the larger models.

So we are all in agreement here that a 3B model is fundamentally inferior to a larger model?

Not that it doesn’t have uses; not that there’s no value in research in small models.

Just, honestly, that these smaller models don’t have the capabilities of the larger models.

It’d be good to be a direct acknowledgment of that, because it seems like you’re going out of your way to promote the “it’s fine to have a small model”; and it is, roughly speaking. Parameter count isn’t everything. Small models are accessible, you can easily fine tune them. They are interesting.

…but, they are not as good, as far as I’m aware, in terms of output, in terms of general purpose function, as larger models.

There is no “one size fits all” here. A bigger model is just a bigger hammer, that in many uses is too bulky and slow to be a proper solution.

At my job, I can’t casually fire up 8xA100 80gb instances. And if I could, the performance wouldn’t have the throughput I require to be useful. Big models are operationally much more expensive.

The smallest/fastest model that is accurate enough for your use case is ideal.

> The smallest/fastest model that is accurate enough for your use case is ideal.

Sure.

…but it’s also fair to say that the smallest model that can fit your use case will be bounded by the parameter count.

No amount of training data can make 100 param model do text summarisation.

If you have a 3B param model, and you want a chat-GPT to embed in your app, do you think it’ll do?

I don’t.

The output is not at that quality level, because it’s too small.

Not everyone needs that; but these 3B / 7B models don’t have the capability to do everything.

You could do what letter is most likely to come next

For your first point where you are attempting to impose agreement, I believe the other commentator is saying that tradeoffs are non-negligible between the two.

Sounds like the difference between edge and centralized ML scoring.

Of the goal is to use it to access a large knowledge base (like Google, but with better semantic searching), then it doesn't matter as much. There are some cases where it still matter due to not making some connections (for example, you may want an answer to something and not realize it due to your ignorance - a smaller model will get that a few percentage less times).

But ultimately small models are very good for most things, and much more preferable (to run at the home to organize your digital life, with a small SBC or old computer)

> Hey SeanAnderson, good question! While parameter count is certainly an important factor in model performance, it's not the only one. The RedPajama project is taking a more nuanced approach to understanding what makes a model perform well, and their focus on smaller models like the 3B is a big part of that.

Sure, you may have played with a 7B model in the past, but that doesn't mean there's no use case for a smaller model like the 3B. In fact, having a performant, smaller model is a game changer for a lot of applications that don't require the massive scale of the larger models. Plus, smaller models are generally faster and more accessible, which is always a plus.

It's hard to pick out the actual answer: what is the application that this is good at? What has their "more nuanced" approach to understanding performance increased this model's performance at doing?

Completely agree. Perhaps they were planning to fine-tune it for something though.

You can ultra fine tune those models ... look at vicune 13B, if you know how to prompt it well, you can get it to work as """"well"""" as ChatGPT. Running on local hardware .... I just got vicune 13b on gradio[1] to act as japanese kanji personal trainer, and I've only used a simple prompt: "I want you to act as a Japanese Kanji quiz machine. Each time I ask you for the next question, you are to provide one random Japanese kanji from JLPT N5 kanji list and ask for its meaning. You will generate four options, one correct, three wrong. The options will be labeled from A to D. I will reply to you with one letter, corresponding to one of these labels. You will evaluate my each answer based on your last question and tell me if I chose the right option. If I chose the right label, you will congratulate me. Otherwise you will tell me the right answer. Then you will ask me the next question. Avoid simple kanjis, let's go."

[1] https://chat.lmsys.org/

How can someone get into using these models? How does ‘tuning’ work? How might I go about using these models for doing things like say summarizing news articles or video transcriptions? When someone tunes a model for a task, what exactly are they doing and how does this ‘change’ the model?

(I'm not an expert)

> How can someone get into using these models

You can use gradio(online) or download(git will not download, it's too big, do it manually) the weights at https://huggingface.co/lmsys/vicuna-13b-delta-v1.1/tree/main and then load the model in pytourch and try inference(text generation). But you'll need either a lot of RAM(16GB,32GB+) or VRAM(Card).

> How might I go about using these models for doing things like say summarizing news articles or video transcriptions Again, you might try online or setup a python/bash/powershell script to load the model for you so you can use it. If you can pay I would recommend runpod for the shared GPUs.

> When someone tunes a model for a task, what exactly are they doing and how does this ‘change’ the model? From my view ... not much ... "fine-tuning" means training(tuning) on a specific dataset(fine, as in fine-grained). As I believe(I'm not sure) they just run more epochs on the model with the new data you have provided it until they reach a good loss(the model works), that's why quality data is important.

You might try https://github.com/oobabooga/text-generation-webui they have a pretty easy setup config. Again, you'll need a lot of RAM and a good CPU for inference on CPU or a GPU.

https://huggingface.co/lmsys/vicuna-13b-delta-v1.1/tree/main

A newer but much better system actually reduces the model size while reducing the functionality of the system - similar to training a NN for a very specific task (as was typical several years ago), but now it can happen with far less data. https://arxiv.org/pdf/2305.02301.pdf This paper is quite fantastic, and will likely shape up to be a quite important glue task for LLM models to generate.

While I recognize that this only one example of what you can do, you can just ask chatgpt to program you a traditional program that does something like this and not have to run a (pretty big/power-intensive/slow on most hardware) 3B/7B parameter model for simple tasks like these.

Yeah it wouldn't be as flexible as a LLM (for example synonyms won't work), but I doubt that for this particular task it'll be that big of problem, and you can ask it to tweak the program in various ways (for example introducing crude spaced-repetition) making it arguably better than the AI solution which takes sometime to prompt engineer and will never be "perfect".

I don't really know how much better fine-tuning makes these models, so I can't think of anything that they can actually be used for where they aren't worse than traditional programs, maybe as an AI in games? for example making them role-play as a historical figure in Civilization 6.

My example here was silly and I admit. But the point was that this simple task cab become more "nuanced"(Aside from ChatRWVK-raven, no other model quite "works" like Vicuna or "tuned LLama"), it can, given the correct prompt act as someone in a fictional work which might help you learn the language better by increase conversational time(most important metric, I'm talking comprehensible input here) by the virtue of being more enjoyable.

Overall I like the progress: LLama releases -> LLama fine turned on larger models gets similar performance to ChatGPT on lower parameters(more efficient) -> People can replicate LLama's model without anything special, effectively making LLMs a "Commodity" -> You are Here.

Sure, a 13B model can be fine-tuned to be pretty decent, which is quite remarkable compared to GPT3's 175B paramters. But a 3B model has 1/4th as many parameters as Vicune-13B, or about twice as many as GPT2. Can you really fine-tune that to do anything useful that wouldn't be better handled by a more specialized open-source model?

Depends on what you want it for. Chatting isn't the only application. For text summarization a model like Vicuna-13b has similar performance to ChatGPT 3.5. Fine-tuned models like the one in this thread might perform way better than the initial ones that leaked from Meta. The important thing is that there's constant progress in this area from the Open Source community and we're about to see amazing things in the future.

Finetuning which can easily be done on consumer hardware and can give these models a lot more power for specific applications.

Also, ChatGPT just can't do a lot of things because of their "rules". I was doing question answering about products on Amazon with ChatGPT and refused to answer any questions about underwear, certain books/videos, etc

I'm in the market for a laptop. If I was crazy and wanted to run or train models like these, what kind of resources would I need?

Would the way the m2 MacBooks share memory be an advantage, or would the lack of cuda support be a killer? Can you do anything with 16GB, or do you need 128gb or something like that? How large are the datasets?

I've only used scikit-learn and pandas so far, I'm not very familiar with neural networks yet

It's not crazy to want to train or run models like these, it's actually quite popular right now! :) The question for you to answer is how handy with scikit-learn and pandas are you, and how much do you want to be on the bleeding edge of things? Most stuff is coming out for CUDA first, since that's what the industrial grade GPUS (A100s) use, so with Apple Arm you either have to wait for someone to port it, or port it yourself.

On the other hand, getting > 8 GiB VRAM on a laptop GPU is rare; you're definitely not getting 128 GiB VRAM, so Apple Arm, with 32 or 64 GiB or RAM (get 128 if you can afford it) is going to get you more gigabytes of usable RAM for training/inference.

Yeah. It seems to me that it's really hard to get more than 10-14 GB of VRAM without using some sort of hyper expensive cluster. What would it cost if you wanted to do it with Nvidia? Being able to share ordinary ram with the GPU in a Mac could maybe be a unique value proposition

RTX 3090 or 4090 gets you 24Gb of VRAM, which is enough to run llama-30b (quantized to 4-bit with groupsize of 1024 or higher) at speeds comparable to ChatGPT. You can also get two and run the model split across them, although pumping data back and forth slows things down.

A brand new RTX A6000 (48Gb VRAM) is probably the largest you can get in a single card that can run in a regular PC. It can be had for $4-5k and is sufficient for llama-65b.

Beyond that, yeah, you're looking at dedicated multi-GPU server hardware.

> It seems to me that it’s really hard to get more than 10-14 GB of VRAM without using some sort of hyper expensive cluster.

Both consumer and workstation (the latter may be cheaper per RAM, but with fewer shaders) 16-24 GB GPUs (RTX 3080Ti/3090/4090/A4000/A4500/A5000), including in laptops, are not hard to find (pricey, but not “hyperexpensive clusters”), and its not until you jump above a single 48 GB RTX A6000 that you need a “cluster”.