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My finetuned models beat OpenAI's GPT-4

mlops.systems

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Re: My finetuned models beat OpenAI's GPT-4

#61

Post by billmalarky »

colordrops wrote: post #59
kcorbitt wrote: post #43

(Disclaimer: I'm the founder of OpenPipe, one of the fine-tuning services OP tried and ultimately the one that produced the highest performing model, it appears.) Data extraction is a use case that fine-tuned models are fantastic at, so I'm not surprised that OP got good results. That said, I've also found it's pretty easy to beat GPT-4 across many task types if you have a way of getting strong training data. We publ…

Why isn't someone providing a "meta model" that uses an LLM to choose between various fine tuned models depending on the question to get overall better results than gpt4?

Founding AI Engineer at OpenPipe here, using a fine tuned "router LLM" to route between various specialized (inc fine tuned but not necessarily) applied models depending on the input is becoming a common pattern in more modern "graph like" LLM applications.

See LangGraph's "conditional edges" concept here: https://langchain-ai.github.io/langgraph/concepts/low_level/...

You can see how that "routing function" could include a call to a "Router LLM." And yes, fine tuning is a great method to better improve the routing intelligence of said Router LLM.

Great question btw!

Re: My finetuned models beat OpenAI's GPT-4

#62

Post by sheepscreek »

colordrops wrote: post #59
kcorbitt wrote: post #43

(Disclaimer: I'm the founder of OpenPipe, one of the fine-tuning services OP tried and ultimately the one that produced the highest performing model, it appears.) Data extraction is a use case that fine-tuned models are fantastic at, so I'm not surprised that OP got good results. That said, I've also found it's pretty easy to beat GPT-4 across many task types if you have a way of getting strong training data. We publ…

Why isn't someone providing a "meta model" that uses an LLM to choose between various fine tuned models depending on the question to get overall better results than gpt4?

Very loosely, isn’t this what is happening inside most LLMs that have a “multi-head” mechanism?

Re: My finetuned models beat OpenAI's GPT-4

#63

Post by uolmir »

gillesjacobs wrote: post #32
pandatigox wrote: post #9

Earlier quoted context omitted.

Your thesis sounds interesting! Do you have a link to it by any chance?

rovr beat me to it below. Here are more links: https://jacobsgill.es/phdobtained (fun fact: because my thesis contains published papers, I am in breach of a few journal's copyright by uploading my own thesis pdf, but fuck'em). LLM approaches were evaluated on my own time and but published (I left research after obtaining my PhD).

I don't know your circumstances but often you retain the right to distribute a "post print", ie the final text as published but absent journal formatting. A dissertation should fit that definition.

Re: My finetuned models beat OpenAI's GPT-4

#64

Post by bashfulpup »

colordrops wrote: post #59
kcorbitt wrote: post #43

(Disclaimer: I'm the founder of OpenPipe, one of the fine-tuning services OP tried and ultimately the one that produced the highest performing model, it appears.) Data extraction is a use case that fine-tuned models are fantastic at, so I'm not surprised that OP got good results. That said, I've also found it's pretty easy to beat GPT-4 across many task types if you have a way of getting strong training data. We publ…

Why isn't someone providing a "meta model" that uses an LLM to choose between various fine tuned models depending on the question to get overall better results than gpt4?

Already a big thing. See the constellation architecture used here:

https://arxiv.org/html/2403.13313v1

Re: My finetuned models beat OpenAI's GPT-4

#65

Post by GlassOwAter »

kcorbitt wrote: post #43

(Disclaimer: I'm the founder of OpenPipe, one of the fine-tuning services OP tried and ultimately the one that produced the highest performing model, it appears.) Data extraction is a use case that fine-tuned models are fantastic at, so I'm not surprised that OP got good results. That said, I've also found it's pretty easy to beat GPT-4 across many task types if you have a way of getting strong training data. We publ…

Is this something, as a tech enthusiast that's no expert, I can easily fine tune are run?

My use case would be fine tuning on technical docs. Specific news, 2 years of blog posts, primary source material, and Twitter explainer thread. I want to gather all the niche information of a topic from the last two years, dump it into this and have an LLM that is a subject-matter expert.

Re: My finetuned models beat OpenAI's GPT-4

#66

Post by simonw »

I'd be interested to see how well these fine-tuned models compare to Claude 3 Haiku (or one of the more expensive Claude models) with a larger set of examples.

The Claude models all have a 200,000 token limit and respond _really_ well to examples - you can feed them in as chat JSON message pairs of user input / ideal assistant output.

Haiku is dirt cheap for this kind of thing and with 200,000 tokens you can probably provide a dozen or so examples.

Re: My finetuned models beat OpenAI's GPT-4

#67

Post by gillesjacobs »

uolmir wrote: post #63
gillesjacobs wrote: post #32

Earlier quoted context omitted.

rovr beat me to it below. Here are more links: https://jacobsgill.es/phdobtained (fun fact: because my thesis contains published papers, I am in breach of a few journal's copyright by uploading my own thesis pdf, but fuck'em). LLM approaches were evaluated on my own time and but published (I left research after obtaining my PhD).

I don't know your circumstances but often you retain the right to distribute a "post print", ie the final text as published but absent journal formatting. A dissertation should fit that definition.

This is indeed often the case, however, my university reviews each thesis, and deemed it can only change to open access in 2026 (+5 years from defense).

I think this is default policy for thesis based on publication agreements here.

In any case, I am not too worried.

Re: My finetuned models beat OpenAI's GPT-4

#68

Post by jappgar »

furyofantares wrote: post #54
hubraumhugo wrote: post #35

Earlier quoted context omitted.

> Data extraction/structuring data is the only serious application of LLMs I fully agree. I realized this early on when experimenting with GPT-3 for web data extraction. After posting the first prototype on Reddit and HN, we started seeing a lot of demand for automating rule-based web scraping stacks (lots of maintenance, hard to scale). This eventually led to the creation of our startup ( https://kadoa.com ) focused…

The way you cut that quote turns it into an assertion that doesn't exist in parent post. They didn't make the (incorrect) statement that no other serious, useful application exists. But that's how it reads when you cut off before "I have actually engaged in for real work and found useful"

To be fair the original sentence could still be implying the same thing. The second half of the sentence just sounds like a hedge.

Re: My finetuned models beat OpenAI's GPT-4

#69

Post by scosman »

strickvl wrote: post #16
scosman wrote: post #14

Earlier quoted context omitted.

On that note: is there a good service for “here’s my dataset”, please fine tune these 9 models and give me evaluation stats?

OpenpPipe - https://openpipe.ai/ - is probably the service that most closely resembles what you’re asking for, but I found the evals weren’t really what I wanted — i.e. following my custom evaluation criteria — so you probably will end up having to do that yourself anyway. But for the finetuning, they’re all somewhat the same. Predibase and OpenPipe are two good options for that. Predibase has more base models for yo…

Wild to see them advertising collecting GPT4 responses for training other models. That’s definitely not allowed by TOS. I suspect many do, but front page advertising is another thing entirely.

Re: My finetuned models beat OpenAI's GPT-4

#70

Post by babelfish »

kcorbitt wrote: post #43

(Disclaimer: I'm the founder of OpenPipe, one of the fine-tuning services OP tried and ultimately the one that produced the highest performing model, it appears.) Data extraction is a use case that fine-tuned models are fantastic at, so I'm not surprised that OP got good results. That said, I've also found it's pretty easy to beat GPT-4 across many task types if you have a way of getting strong training data. We publ…

Is using model responses to train a new model against the ToS for the major LLM providers (OpenAI, Anthropic, etc)?
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