How to train your own large language models

How to train your own large language models(blog.replit.com)

283 points by benwen 3 years ago | 60 comments

koboll 3 years ago |

Ghostwriter is notably worse than GPT-4, so while it may be true in a sense that "Training a custom model allows us to tailor it to our specific needs and requirements", the reality is they'd be getting better results just using OpenAI right now. Probably true for almost every other use case.

That said, I am patiently waiting and champing at the bit for the day this isn't true anymore. Cool to see the groundwork being laid for it.

brucethemoose2 3 years ago | |

Stable Diffusion 1.5 is not SOTA, but in reality the sea of augmentations makes SD kinda unbeatable, if you are willing to put in the work to use them.

I think LLMs could end up the same way, if the comminity consolidates around a good one.

alpaca128 3 years ago | |

Not everyone wants to depend on and trust a cloud service, and not everyone needs GPT-4 quality.

If there's a viable way to tune and run models locally they could still be useful if you don't need it to play chess and imitate a Python interpreter at the same time.

PlantsRpeople2 3 years ago | | |

Is it possible to add to an LLM without re-training it, my understanding was no.

nemoniac 3 years ago | |

If one of your specific needs and requirements is that you do not share data with OpenAI then this is a viable option.

two_in_one 3 years ago | |

just my 5 cents. it should be easier to train small custom model which works off a big pre-trained one. getting latent state as an input. while big model does all the hard work. but, getting latent means it should be accessible. that's why open source models are so valuable, even if they are not that good in general. more over, open source models can be used in other projects in various setups.

birdplant 3 years ago | |

They’re competing directly with Microsoft (and getting crushed) because GitHub is their biggest competitor, so it makes sense that they wouldn’t want to use OpenAI products.

Agree that Ghostwriter is subpar though.

jackmott42 3 years ago | |

That could all change in a few months. We saw locally runnable, open source image generation catch up quick.

zacksiri 3 years ago |

This is interesting. I think the future of AI will not be re-creating something like ChatGPT, but using variations of these methodologies to train AI models for specific tasks.

There are some advantages to not having to make an LLM that impresses every human being on the planet. Imagine training the AI to be good in only one specific thing. I think it will become much more precise and deterministic.

This is just my hypothesis. I'm excited to see where this goes.

kkielhofner 3 years ago | |

There is plenty of less attention-grabbing work being done on "domain specific LLMs" like BioMedLM[0], Med-PaLM[1], BloombergGPT[2], etc.

That reminds me - I saw a somewhat-clever acronym variant for LLM that communicated this the other day but it escapes me ATM...

[0] - https://www.mosaicml.com/blog/introducing-pubmed-gpt

[1] - https://cloud.google.com/blog/topics/healthcare-life-science...

[2] - https://dev.to/reaminated/thoughts-on-bloomberggpt-and-domai...

zacksiri 3 years ago | | |

Yeah that's awesome. I honestly think the next 'leap' in AI will come from these 'domain specific' models.

Also I'm not talking about just 'prompt' output model. These ones are great and I'm sure they will be extremely impressive. However I'm talking more about being able to 'operate' something.

Imagine this, an AI able to operate some specific API in a deterministic / reliable way. I'm talking about complex operations.

SO the output is not so much a text prompt but an SOP and then actually Operating the SOP.

Imagine going into an app and say "can you boot up a cluster on AWS and run a wordpress site, point domain example.com to the site".

Imagine this "you know my database for app X, what was the latest snapshot", it replies with the date / time of the snapshot, and you reply with "can you move that snapshot from google cloud and create a new database from that snapshot on AWS cloud?", and it does it for you.

That's what I look forward to.

MrMovl 3 years ago | | |

I'm wondering about just that, I want to have a minimal model with little overhead that I can train to a specific body of texts but it doesn't need to know about all the rest. So basically it should be good at conversation and able to learn what I teach it, nothing else.

But I'm having trouble finding resources about how to achieve that.

ftxbro 3 years ago |

How expensive is it? My understanding is that it's not reasonable to train an LLM from scratch by yourself, and that if you want one that isn't just very stupid then you need to spend between hundreds of thousands and hundreds of millions of dollars. But if you don't want to train from scratch then you can fine-tune existing models for cheaper.

dkhudia 3 years ago | |

Disclaimer: I work for MosaicML (MosaicML is the creator of the training platform used by Replit).

Training these models from scratch on your domain specific data is not as expensive as one might think. We have provided some cost estimates in our blogs.

https://www.mosaicml.com/blog/mosaicbert

https://www.mosaicml.com/blog/training-stable-diffusion-from...

https://www.mosaicml.com/blog/gpt-3-quality-for-500k

moltar 3 years ago | | |

Do you have any examples on how to train a model that can write code but in a specific domain? Eg I only want to train it on a specific set of code. Eg let’s say functional React components in TypeScript.

ftxbro 3 years ago | | |

Thank you this is very interesting!

kkielhofner 3 years ago | |

Looking at what they're doing here probably not as much as you think.

As you note, with the plethora of open/open-ish LLMs today and LoRA + PEFT you can fine tune with low VRAM and pretty quickly so even a single A100 or whatever cloud GPUs are just fine. I've even seen people pull it off in reasonable time on super cheap T4s, A10s, etc.

I doubt anyone reading a blog post is attempting to train a "true" multi-billion param LLM from scratch.

lxe 3 years ago |

Shameless plug: I built a toy tool to really quickly get into llm finetuning by just pasting your training samples into a textbox:

https://github.com/lxe/simple-llm-finetuner

ukuina 3 years ago | |

This is brilliant! Thank you for creating and sharing this.

zwaps 3 years ago |

I am leading a similar initiative and I have also used Databricks for the preprocessing.

Most interesting is what happens between the preprocessing and the model training - the hand-off to the cluster workers.

I guess the efficient option is to partition the data, set up shards in-advance and ideally cache or even copy the data to local workers during init.

This, of course, breaks some of the promise of being able to scale training flexibly, for instance to experiment with the scaling of compute and data.

A different way to go about it is to use a streaming/iterable dataset/loader implementation with its own sharding logic that reads from a central store of parquets with some reasonable row-group size. This gives full flexibility in terms of node/gpu/worker/batch_size for experimentation - e.g. literally as parameters in PyTorch. Of course, one has to also implement caching of remote data since the data is kept centrally.

In my opinion, there is no satisfying/flexible solution for this, especially when one also wants to experiment with complex transformations or augmentations in the dataset/loader and remain portable across cloud offerings. So, this has to be implemented from scratch (not too difficult but still a lot of code). The coming datapipes also probably make this trivial.

Would love to hear more experiences in how you set this up!

Edit: I guess for NLP this is a good implementation and what Mosaic uses https://huggingface.co/docs/datasets/stream

rmbyrro 3 years ago |

> "a student coding on their phone in India should have access to the same AI as a professional developer in Silicon Valley. To make this possible, we train custom models with reduced cost."

In principle, that's great. But the reality is: whoever has the resources and benefits from something better will look for ways to get it. What they're communicating here is: the most resourceful developers on the planet aren't our ideal customer.

jxi 3 years ago |

Is this just a high level description of the process? There isn’t really any actual code to run or tutorial to follow.

detrites 3 years ago | |

Yes, they mention they plan to publish articles with specific details later on as well as to open source some models.

dhruvbird 3 years ago |

Did the blog mention any metrics like what the model size is etc... since that seems to be the one of the motivating factors?

VWWHFSfQ 3 years ago |

Did we ever get any resolution about what happened after this company threatened to sue their intern for making a side project that supposedly stole all their great ideas? I would like to know before I ever consider anything from them again.