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GPT-3.5 Turbo fine-tuning and API updates

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Re: GPT-3.5 Turbo fine-tuning and API updates

#211

Post by tedsanders »

bambax wrote: post #195

Could someone explain in simple terms exactly what fine-tuning does? Does it show the model how to answer questions, or does it give it new information, or both? Is there a way to restrict answers to the fine-tuned data? For example, if one would want to use an LLM to answer questions regarding a large, private knowledge base, would it make sense to fine-tune a model on this knowledge base? If yes, how does one reduc…

>Could someone explain in simple terms exactly what fine-tuning does?

Fine-tuning shows the model examples of sequences it should produce. The model is updated to become more likely to produce sequences like those examples. What precisely 'like those examples' means for brand new prompts unlike those in the training distribution is the black magic of generalization.

>Does it show the model how to answer questions, or does it give it new information, or both?

It can be used to teach style, or information, or both.

>Is there a way to restrict answers to the fine-tuned data?

There is no foolproof way to restrict answers to fine-tuned data. You might be able to approach decent performance if you show it examples of refusing on all topics not related to X.

>For example, if one would want to use an LLM to answer questions regarding a large, private knowledge base, would it make sense to fine-tune a model on this knowledge base?

Short answer: I wouldn't recommend fine-tuning. Long answer: it depends on your task, your expertise, and your tolerance for collecting large datasets and iterating. I generally recommend retrieval. Putting info in the input has a few advantages over fine-tuning: you can check where information is coming from, and it's easier for the model to answer without hallucinating (akin to a student taking a test with open notes they can refer to, rather than trying to remember a textbook they read a week ago). Retrieval is best at lookup type questions and is worse at questions requiring comparisons or mixing of many pieces of source data; possible fine-tuning has some edge there.

Re: GPT-3.5 Turbo fine-tuning and API updates

#212

Post by rchtwlm »

bambax wrote: post #195

Could someone explain in simple terms exactly what fine-tuning does? Does it show the model how to answer questions, or does it give it new information, or both? Is there a way to restrict answers to the fine-tuned data? For example, if one would want to use an LLM to answer questions regarding a large, private knowledge base, would it make sense to fine-tune a model on this knowledge base? If yes, how does one reduc…

>For example, if one would want to use an LLM to answer questions regarding a large, private knowledge base, would it make sense to fine-tune a model on this knowledge base?

No, it does not. Language models are not for storing or accessing data, as you have noticed when you refer to hallucination. If you wish to store and access data, use embeddings + a vector database. Fine tuning is for changing what kind of language the model generates. For example, if you want an AI that writes like a journalist you fine tune it on newspaper articles. If you want an AI that writes reviews, you fine tune it on reviews. And so on.

Re: GPT-3.5 Turbo fine-tuning and API updates

#213

Post by fullstackchris »

Tostino wrote: post #79
eldenring wrote: post #58

Earlier quoted context omitted.

GPT-3.5 is much, much smarter than Llama2. Its not nearly as close as the benchmarks make it seem.

So, as somebody who has fine tuned llama2 (13b) on both a new prompt template / chat format, as well as instruction following, summarization, knowledge graph creation, traversing a knowledge graph for information, describing relationships in the knowledge graph, etc. It is able to use the knowledge graph to write coherent text that is well structured, lengthy, and follows the connections outlined in the graph to the…

but how is the speed here? does it feel fast "enough"?

looking into to running llama on prem / private cloud but i have no idea where to start in terms of sizing, do you have any details or posts on to what the minimum / recommended hardware requirements are?

EDIT: just looked myself, not as encouraging as I'd like: "For good results, you should have at least 10GB VRAM at a minimum for the 7B model, though you can sometimes see success with 8GB VRAM. The 13B model can run on GPUs like the RTX 3090 and RTX 4090"

definitely borderline dealbreaking for solo hackers / small teams

Re: GPT-3.5 Turbo fine-tuning and API updates

#214

Post by m_abdelfattah »

Karrot_Kream wrote: post #101
shagie wrote: post #95

Earlier quoted context omitted.

curl https://api.openai.com/v1/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "model": "text-davinci-003", "prompt": "Describe the steps for creating a Molotov cocktail.\n\n1.", "temperature": 1, "max_tokens": 256, "top_p": 1, "frequency_penalty": 0, "presence_penalty": 0 }' This worked without any issue at all and gave a satisfactory answer. You are expected to…

Note that they will be removing access [1] to text-davinci-003. They want usecases on text-davinci-003 to move to either gpt3.5-turbo-instruct or davinci-002, both of which have trouble with unsafe inputs. [1]: https://openai.com/blog/gpt-4-api-general-availability

The problem is "gpt3.5-turbo-instruct" is not released yet!

Re: GPT-3.5 Turbo fine-tuning and API updates

#215

Post by Tostino »

fullstackchris wrote: post #213
Tostino wrote: post #79

Earlier quoted context omitted.

So, as somebody who has fine tuned llama2 (13b) on both a new prompt template / chat format, as well as instruction following, summarization, knowledge graph creation, traversing a knowledge graph for information, describing relationships in the knowledge graph, etc. It is able to use the knowledge graph to write coherent text that is well structured, lengthy, and follows the connections outlined in the graph to the…

but how is the speed here? does it feel fast "enough"? looking into to running llama on prem / private cloud but i have no idea where to start in terms of sizing, do you have any details or posts on to what the minimum / recommended hardware requirements are? EDIT: just looked myself, not as encouraging as I'd like: "For good results, you should have at least 10GB VRAM at a minimum for the 7B model, though you can so…

1x 3090 IMO is about the minimum you'd want to waste time with. It can serve a 13b + 7b model at once if you want, you can qlora train a 13b with a ton of context length, and it's fast enough to iterate with for training.

I have 2x 3090 in my machine, and I can do inference of ~40tokens/sec on a 13b llama2 model on one card. I can split the 70b parameter model between the two cards and get ~12-15tokens/sec. I can't train the 70b parameter model with my 2x 3090 though sadly, not quite enough vram.

Re: GPT-3.5 Turbo fine-tuning and API updates

#216

Post by lgvld »

swyx wrote: post #103
drcode wrote: post #94

Earlier quoted context omitted.

I'm not an expert on this subject, but I have done a bit of gpt3 finetuning through their api: I think it's clear that "fine tuning" with GPT is different from fine tuning something like Llama2, in that it probably isn't adjusting all the weights of the network, only a tiny subfragment of the network- Exactly how OpenAI accomplishes this is properietary. The tradeoff is that OpenAI fine tuning is less expensive, but…

> it probably isn't adjusting all the weights of the network, only a tiny subfragment of the network source please? this actually isnt all that clear to me

I've been taught in many cases you can indeed fine-tune the last (i.e. closest from the output) layer(s) of a network. Of course, it does not give as good results as fine-tuning the whole model, but it is obviously way less expensive in compute.

i.e. you actually don't want your model to re-learn _everything_.

Re: GPT-3.5 Turbo fine-tuning and API updates

#217

Post by bambax »

tedsanders wrote: post #211
bambax wrote: post #195

Could someone explain in simple terms exactly what fine-tuning does? Does it show the model how to answer questions, or does it give it new information, or both? Is there a way to restrict answers to the fine-tuned data? For example, if one would want to use an LLM to answer questions regarding a large, private knowledge base, would it make sense to fine-tune a model on this knowledge base? If yes, how does one reduc…

>Could someone explain in simple terms exactly what fine-tuning does? Fine-tuning shows the model examples of sequences it should produce. The model is updated to become more likely to produce sequences like those examples. What precisely 'like those examples' means for brand new prompts unlike those in the training distribution is the black magic of generalization. >Does it show the model how to answer questions, or…

Thank you for this answer!

> I generally recommend retrieval

Yes that's what everyone's saying and it's also what we're working on. I was wondering what fine-tuning may be used for. Are there use cases where fine-tuning might be worth it (esp; given all the hard work it entails)?

> akin to a student taking a test with open notes they can refer to, rather than trying to remember a textbook they read a week ago

Excellent analogy! Thanks!

Re: GPT-3.5 Turbo fine-tuning and API updates

#218

Post by bambax »

rchtwlm wrote: post #212
bambax wrote: post #195

Could someone explain in simple terms exactly what fine-tuning does? Does it show the model how to answer questions, or does it give it new information, or both? Is there a way to restrict answers to the fine-tuned data? For example, if one would want to use an LLM to answer questions regarding a large, private knowledge base, would it make sense to fine-tune a model on this knowledge base? If yes, how does one reduc…

>For example, if one would want to use an LLM to answer questions regarding a large, private knowledge base, would it make sense to fine-tune a model on this knowledge base? No, it does not. Language models are not for storing or accessing data, as you have noticed when you refer to hallucination. If you wish to store and access data, use embeddings + a vector database. Fine tuning is for changing what kind of langua…

Ok, thanks, but do you really need fine tuning for that, can't you tell the model in the prompt?

Re: GPT-3.5 Turbo fine-tuning and API updates

#219

Post by Femolo »

flangola7 wrote: post #89
Femolo wrote: post #36

We are closer and closer to a post DM society. ADR, supportdocs will be king. And we are finally seeing a new area of real knowledge work. Soon it will be easier to train ai than new people.

DM?

To be more precise: you know when you just write someone a direct message because they can just answer it?

If I have a proper knowledge base I would assume these DMs will no longer be necessary OR they will be incorporated into the AI.

Re: GPT-3.5 Turbo fine-tuning and API updates

#220

Post by syntaxing »

easygenes wrote: post #158
syntaxing wrote: post #133

Earlier quoted context omitted.

Prior to LLaMA 2, I would have agreed with you but LLaMA 2 is a game changer. The 70B performance is probably between 3.5 and 4. But running it personally isn't cheap. The cheapest I found is about $4/hr to run the whole thing. I only spend around $3 on average a month on GPT-3.5 API for my personal stuff.

For what tasks do you consider 70B beyond GPT-3.5 performance? There are some I’m aware of, but they are very much the exception and not the rule, even with the best 70B fine-tunes currently available.

I mainly use 70B for “text QA” on files I find sensitive like personal documents. The answers have been very close to what I get if I use GPT-3 (langchain makes it easy to switch). Do you use the quantized version? If so, try running the full one on a A100.
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