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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

#221

Post by Herring »

matheusmoreira wrote: post #201
sheepscreek wrote: post #119

Earlier quoted context omitted.

> forget the phony ethics, and focus on the best version of this technology I’ve experimented a lot between the censored and uncensored versions of Llama 2. Based on this, I’ve concluded that fine-tuning for political correctness and ethics negatively affects all answers. They become repetitive and washed out.

I hope this technology keeps improving to the point we can run it on our own machines. It's too good to be censored.

> In one hour, the chatbots suggested four potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization.

https://arxiv.org/pdf/2306.03809.pdf

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

#222

Post by syntaxing »

manjoh wrote: post #175
syntaxing wrote: post #148

Earlier quoted context omitted.

I use it a lot for personal coding projects, grammar correction/sentence rewording, and translation (it works better than google translate for longer text). I explicitly call out personal stuff since my job provides an in-house front end that uses the GPT API (I'm actually not sure which version it is, but guessing from the response quality, its probably GPT-4). My work one has made me noticeably more productive. It…

Can you talk about how you integrate gpt API at work and why not just use chatgpt 4?

The server is provided by my employer so I can’t go into the implementation detail. But overall, most companies provide access to the API endpoint instead of using chatGPT itself since OpenAI uses your results to train (hence why it’s free for 3.5). The API endpoint supposedly doesn’t use your data for training which is why I use the API endpoint for personal stuff as well.

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

#223

Post by syntaxing »

blorenz wrote: post #154
syntaxing wrote: post #149

Earlier quoted context omitted.

GPT-4 is significantly more expensive so I can definitely see you spending that amount. For really complex stuff, I switch over the GPT-4 and it will cost me almost $3 a "question" (as in going from the beginning to solving it). Honestly worth it since it solves my problem but it adds up quick so I try to stick with 3.5 when I can.

Can’t you get by with ChatGPT-4 for these personal assistant type questions? That’s what I do and my 20 a month goes a long way. I’d be interested to see if I am missing out on anything using GPT to is way in contrast to the API.

I actually used to use ChatGPT but switched to the API once I had GPT-4 access. Mainly it’s because I simply didn’t use the $20 worth of the GPT-4 at the time. It was extremely slow and the question per hour limitation was annoying and stressful. I would always worry I would need it for something unexpected so I never used more than 15 questions at a time (but this has probably changed these couple months). In addition, the privacy implications are better for the API since the terms are better for how they handle your data. I also like how I can tie in GPT anywhere. I use the matrix bridge so you can give access to people like my parents who are not as tech literate to sign up and get used to chatgpt interface. I allow them to talk to it as a bot through WhatsApp bridge.

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

#224

Post by BoorishBears »

Herring wrote: post #221
matheusmoreira wrote: post #201

Earlier quoted context omitted.

I hope this technology keeps improving to the point we can run it on our own machines. It's too good to be censored.

> In one hour, the chatbots suggested four potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization. ht…

> explained how they can be generated from synthetic DNA using reverse genetics

Was that a secret? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9066064/

> supplied the names of DNA synthesis companies unlikely to screen orders

My naive Google search implies that'd be most of them...

https://arstechnica.com/science/2022/12/experts-debate-the-r...

> identified detailed protocols and how to troubleshoot them

Googling "reverse genetics for influenza" gets the same protocols...

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5297655/

> recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization.

I googled "Who to hire for reverse genetics" and the first result was a CRO

https://www.wur.nl/en/research-results/research-institutes/b...

Please feel free to contact the expert of our contract research organization (CRO) if you have a question concerning reverse genetics and reverse vaccinology.

-

LLMs have the sum knowledge of a lot of Google searches. I wish we'd stop drumming up the most ludicrous risk profiles when they're capable of damage in much more boring ways.

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

#225

Post by chaosbolt »

>release what was supposed to be open as a free beta to fix bugs

>release its more powerful brother as a subscription nased service

>heavily nerf both

>release fine tuning to maybe make the nerfed gpt 3.5 as good as it was at launch but only if you finetune it well enough

>keep the unnerfed version for internal use at microsoft

>profit

I mean at least Google is honest about it, they have the best product, you won't get it because it's more valuable as an internal tool than public, sure announce Bard after gpt launches to not have your stocks go down but it's bad and even then will probably never launch. At least meta made their nerfed version opensource.

I legit was a beast with the gpt 4 of a couple months ago, now I'm back to a 1 man developer, using it now makes me waste time more than gain it, since I have to fix its errors, might as well do it myself... so I can see how you don't want to give it to others.

If your reply is going to be something obviously wrong like "it wasn't nerfed" then just don't waste your time man...

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

#226

Post by hnuser123456 »

bambax wrote: post #218
rchtwlm wrote: post #212

Earlier quoted context omitted.

>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?

It seems like LLMs can easily start to "forget" requests to modify their style or tone if it's a small part of the overall context, and they drift back towards the style or tone they naturally have (or were encouraged to have by their original trainers.)

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

#227

Post by tempaccount420 »

Sohcahtoa82 wrote: post #67
TheCaptain4815 wrote: post #9

Quite exciting, this is the first time their "Chat" model is available for finetuning. Has anyone successfully bypassed the current Ai detectors using fine-tuned models? I know it's possible, I'm just trying to conceptualize how the dataset would be organized...

> Has anyone successfully bypassed the current Ai detectors using fine-tuned models? Current AI detectors are pure garbage. Anybody paying for one is getting scammed. Anybody using one to actually make decisions is making a grave error. It's a real shame that some schools are using AI detectors to detect students using ChatGPT to write essays for them, because there have been many cases where the detectors flag essay…

I wouldn't be surprised if students will soon adopt the style of ChatGPT, say because they're using it to learn English, or just talking to it a lot.

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

#228

Post by atonalfreerider »

erik1332 wrote: post #204
matheusmoreira wrote: post #200

Earlier quoted context omitted.

I wish I could point the AI at huge GitHub codebases and have it explain the whole thing to me. Would make contributing to open source software so much easier.

There is a GTP-4 plugin available for this purpose. I have not tested it myself, but it may be worth trying out?

You are referring to this one, yes? https://recombinant.ai

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

#229

Post by kuida0r3 »

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…

Andrew Ng's deeplearning.ai just released a course on this yesterday https://www.deeplearning.ai/short-courses/finetuning-large-l... - at a high level it lets one put more data into the model than fits into the prompt.

And yes your use case of a large private knowledge base is one of the prime examples she used in the course. Scenarios that are domain-specific or privacy conscious probably makes more sense for finetuning as opposed to prompting.

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

#230

Post by SparkyMcUnicorn »

easygenes wrote: post #199
SparkyMcUnicorn wrote: post #91

Earlier quoted context omitted.

I haven't fine-tuned GPT-3.5-turbo yet, but overall llama2 is probably going to be cheaper. Especially if 13B is good enough for you. You can get roughly $0.003/1K tokens using modal.com for inference on a fine-tuned llama2 13B model, and there's definitely cheaper options available. Unless you have a ton of data, the cost for training llama2 is usually a few dollars.

I'm curious about this. Can you point me to, e.g. some example code for setting up an inference endpoint with a base llama2 model on modal.com?

Here's one if their tutorials using vLLM, and they have a few other guides and example repos as well.

https://modal.com/docs/guide/ex/vllm_inference

https://github.com/modal-labs

Alternatively, Runpod is fairly cheap and easy to get stuff running in a few minutes and can be point/click only using their templates.

https://www.runpod.io/console/gpu-secure-cloud?template=f1pf...

("serverless" example) https://github.com/ashleykleynhans/runpod-worker-oobabooga

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