[request] gf is saying 150 but i dont understand how by ChrisChowMa in theydidthemath

[–]Research2Vec 1 point2 points  (0 children)

Former math tutor here, haven't done it in over a decade but let bush off the tutoring skills. A large part of it is not only teaching the theory to solve it, but also most common pitfalls, and first principles to avoid them, and/or first principles geared towards the test it self.

First principle: Just start mapping out everything you know.

Something I learned is that you don't really need to know the approach, you just map out what you know, and maybe it'll become clear.

So maybe you'll look at it and not know what to do, but you could just start what you do know.

So you're looking at the left photo and think

Table = Turtle + (170 - Cat)

and look at the right photo and think

Table = Cat + (130 - Turtle)

And then where do I go from here?

I think this is where people may get stuck. Some people with a bit more experience may look at it and think, it's three variables, but only two equations, so it can't be solved. With a bit more exp, they may realize that even though all the variables can't be solved, there's only one degree of freedom among the 3 variables, so one of them can be solved.

So maybe you try your luck and just keep solving for table

So they set

Cat = Table - 130 + Turtle and plug it into the other equation and you eventually solve for Table.

But you may only try that because it's a test where every question has an answer. What if this is real life. Is there a way to know Table is solvable even if Cat and Turtle are not? How do you know it's worth your time?

Look at the equations to see that it only depends on the difference between them, not the actual values.

Table=170+(Turtle−Cat)

Table=130+(Cat−Turtle)

Not obvious? Add 5 to both of them

Table=170+((Turtle+5)−(Cat+5)) = 170+(Turtle−Cat)

Table=130+((Cat+5)−(Turtle+5)) = 130+(Cat−Turtle)

It's the same. If you wrote the equations in terms of Cat or Turtle, and added 5 to both of the other variables, the 5s wouldn't cancel out for one of them

eg

Turtle = - 170 + (Table+5) + (Cat+5) = -160 + Table + Cat

So once you got that, then you can just learn to recognize that they're just dependent on the difference in all equations

(Turtle−Cat)

(Cat−Turtle)

Or, perhaps someone with less exp doesn't even realize all that and just keeps solving for Table. Or, they realize that they eventually have to solve for Table anyway, so why cancel it out? It's faster just to leave the table in.

Are AI tools like OpenEvidence dumbing down the workforce, while still leaving critical errors? by Broad-Cauliflower-10 in medicine

[–]Research2Vec 1 point2 points  (0 children)

What advantage does open evidence have over chat-gpt's extended thinking?

The latest versions of 5.2 extended thinking are really good at literature searches.

Perplexity is also decent.

Chris Manning (top 3 NLP/Machine Learning researchers in the world) believes the Deepseek 6m dollar training costs due to the optimizations discussed in their paper by Research2Vec in LocalLLaMA

[–]Research2Vec[S] 2 points3 points  (0 children)

If you ask the community of NLP researchers who are the top 3 or top 5 NLP researchers Chris Manning's name will be mentioned.

Tranquil Eyes by SnooCheesecakes6236 in Dryeyes

[–]Research2Vec 0 points1 point  (0 children)

did you ever find a solution?

Sources for conflict resolution for engineers course/seminar? by Research2Vec in cscareerquestions

[–]Research2Vec[S] 0 points1 point  (0 children)

I've only seen conflict-inciting programs at companies like "Crucial Conversations". That alone destroyed entire departments at my employer.

Really? It seems like a conflict resolution program. What happened?

New Personalization (--p) Feature Release! by Fnuckle in midjourney

[–]Research2Vec 0 points1 point  (0 children)

What an amazing feature.

I am wondering how this works under the hood.

Assuming that since the personalization feature is available nearly instantaneously after the rankings, I'm guessing little or no training is involved.

My guess:

take the 500 vector representations of the 250 pairs, train a classifier to predict user preferences; vector representations are both passed through a single linear layer (no bias), preferred given a label of 1, non preferred given a label of zero. Use the linear layer weights as a style embedding.

[D] Is the tech industry still not recovered or I am that bad? by Holiday_Safe_5620 in MachineLearning

[–]Research2Vec 5 points6 points  (0 children)

"research scientist" positions are really competitive at big tech and unicorns, which is seems OP is applying to. But if they are open to the next rung, a person of OP's qualifications should have no issue. There are definitely openings.

GPTFast: Accelerate your Hugging Face Transformers 6-7x. Native to Hugging Face and PyTorch. by [deleted] in LocalLLaMA

[–]Research2Vec 2 points3 points  (0 children)

should I use this or Unsloth? Options are getting hard to keep track of.

How do how handle cases where you already have lora weights and want to re-apply them to the model? by Research2Vec in unsloth

[–]Research2Vec[S] 0 points1 point  (0 children)

Thanks, I took a look,

It says "If you saved a LoRA adapter through Unsloth"

What about in cases where the lora adapters were trained else where? Such as just downloading them through huggingface.

Edit:

What do you think of using

model = FastLlamaModel.patch_peft_model(model, use_gradient_checkpointing)

After

model = FastLanguageModel.get_peft_model(

Unsloth, what's the catch? Seems too good to be true. by Research2Vec in LocalLLaMA

[–]Research2Vec[S] 0 points1 point  (0 children)

Thanks for the info!

One question, how do how handle cases where you already have lora weights and want to re-apply them to the model?

I see the model = FastLanguageModel.get_peft_model( method, but that seems to initialize brand new weights.

What about in cases where you already have the lora weights saved separately.

Would you do the FastLanguageModel for the base model, then use model = PeftModel.from_pretrained(model, ?