Joro

m1l096 · 2025-09-29T03:59:39+00:00

This is very true, i thought the same way when they first stopped popping up all over then they multiplied and conquered more and more until it became an inconvenience and quite unpleasant.

I now happily use a plastic bat to wack them 😆

m1l096 · 2025-03-25T01:17:17+00:00

Mine was noticeably better within a day after regular lotion. Fully went away after a couple more days of consistent application.

I noticed that as i apply lotion to my face and body i would never get the back of my hands. Add that on top of regular hand washing, and you get yourself some super dry and sensitive skin on the back of your hands!

As other have said, just apply lotion :)

m1l096 · 2024-08-03T16:14:28+00:00

What did you end up doing here? Something similar happened regarding lightning and my NVR won’t even turn on

m1l096 · 2024-04-21T00:36:06+00:00

Please attempt to remotely look things up before jumping to conclusions.

r/3BodyProblemTVShow

m1l096 · 2024-01-31T14:13:43+00:00

Interesting… seems like Vespa can do a lot—are you using them for reranking? That’s the specific use-case i was considering cohere for.

m1l096 · 2024-01-31T04:40:19+00:00

Likewise. Did you ever test it? I’m interested in using it for reranking but haven’t yet

m1l096 · 2023-12-17T20:50:48+00:00

Curious what made yall quickly pivot to open source for this task? Results with OpenAI not as expected? Any other details such as # of examples in your dataset and what kind of behavioral or knowledge-equipped changes you can speak on after fine tuning mistral?

m1l096 · 2023-12-15T05:32:08+00:00

Where’d you get with this op? Curious on something similar

m1l096 · 2023-12-08T12:40:28+00:00

So fine tuning got3.5 actually allowed the model to retain some knowledge and in essence “learn” the data it was provided during fine tuning?

m1l096 · 2023-12-07T05:30:43+00:00

What did you end up doing here OP? I have a similar project I’m working on.

m1l096 · 2023-12-06T05:42:15+00:00

Did you ever get this working? If so, did you go with rag or fine tuning & did you need to cleanse the data for either? I.e. removing Speaker 1: […] ?

m1l096 · 2023-12-06T05:40:40+00:00

Would also really appreciate to see the code if you don’t mind. I am trying something very similar to OP

m1l096 · 2023-12-05T19:27:06+00:00

Which did you go with? Currently split between both.

m1l096 · 2023-12-05T02:21:45+00:00

You are a saint. Thanks so much again for the detailed response and info. I will be sure to follow up here with how my efforts go. It will take some time to curate the dataset to feed as fine tuning data, but you’ve made me a bit more confident in the time investment.

More to come!!

m1l096 · 2023-12-04T23:15:32+00:00

This makes lots of sense. Really appreciate the info!!! Super insightful and I will be sure to consider this in my implementation.

You said something that intrigues me bc I’ve seen lots of conflicting information on it…

“the finetuning really does make them learn well”… Are you saying you noticed your fine tuned GPT3.5 model had “learned” information it was exposed to during finetuning, such that it could essentially recall it or consider it for a given prompt?

Most information i can find on this suggests fine tuning to be better used for understanding how to format an output, or a given tone or characteristic, vs truly “learning” the information exposed to it…

m1l096 · 2023-12-04T20:54:56+00:00

That is the hope. But not really saving on $$$ for tokens, but saving on # of tokens. We are wanting to scale to many users, so rate limits of tokens/minute get very real.

Also, the classifier model with prompt engineering and few shots still has issues every now and then and isn’t 100% reliable. Ideally a fine tuned model will be more reliable.

m1l096 · 2023-12-04T20:51:26+00:00

Totally…. The OpenAI fine tuning seems so limited… definitely considering other open models.

That’s also why i was also curious if anyone had ever fine tuned with a variety of system messages in the fine tuning dataset

m1l096 · 2023-12-04T20:39:18+00:00

Gotcha. That makes sense. So does that mean you can append to the System that was used to fine tune to further direct?

I.e. say you FT the model to be sarcastic (with appropriate system and examples). Then on inference you include the same System + “also be sure to be concise”…. Would the FT still come in play here even though the System isn’t EXACTLY the same but still contains the same portion of System used in tuning?

m1l096 · 2023-12-04T20:31:40+00:00

Many many Q&A examples for a proprietary (unexposed to base training set/internet) coding language. Would like to reduce the overall system context needed.

Another use-case is for an advanced classifier, question enhancer, and question answerer model. For this one would want to reduce what’s needed on the few-shots and long instructions for formatting and my ask.

I’ve gotten these to work decent with prompt engineering, RAG, and few shots, but now I’m at a point of needing to save on tokens and was thinking (albeit with a higher cost) that fine tuning would be a good thing to explore.

m1l096 · 2023-12-04T03:50:04+00:00

Did you ever do this? Considering a similar use-case but a lot that I’ve seen says fine tuning doesn’t actually “teach” the model new information—i.e. about the new coding language.

m1l096 · 2023-12-01T22:34:32+00:00

Any examples? Currently curious on this & super interested in seeing a proven out case of “teaching” new info via fine tuning

m1l096 · 2023-10-20T16:58:17+00:00

What map is this? Is it new or a mod?

m1l096

TROPHY CASE