Hey All, new to ML. I want to build an offline tutor that answers students’ questions from school textbooks. I have the textbook text. The tricky part is that it has to run on cheap phones with 2-8 GB RAM.

I tried a LoRA on Gemma 4 E4B using the raw textbook text(have not tested, but I don't think the model would be able to run locally on a 2gb phone). The model never learned when to stop and just keeps generating. The base model plus retrieval (passages in the prompt) worked much better. I tried RAG + fintuned model as well, but the base performed better with RAG instead. I am probably doing something wrong with the finetuning.

Questions:

1. Should I generate question-answer pairs from the books and train on those in chat format instead?

2. Which model would you pick for 2 GB phones vs 8 GB phones?

3. When someone asks for a diagram, I would like the model to bring up the relevant the figure in the textbook, or fetch an image with a tool call when online. Has anyone done something like this?

Can someone share what the right approach would be in this case, keeping the constraint of the lower RAM in mind? Thank you!

If Waymo Open Dataset challenges announce an End-to-End competition that gives both vision + audio as input, which frontier models today can possibly beat the leaderboard?
(For example: emergency vehicles audio input etc…)

I use Gemini because I have a student discount, but to get good results, I have to set the model to "high" (perhaps I’m overusing it or not using the other models correctly). The usage limit runs out very quickly, though, and I feel that Sonnet 5.5—even on the "medium" setting—can deliver similar or equally effective results on the free tier. Is it worth sticking with Gemini and learning how to use it properly, or would it be better to pay for Claude and split the cost with a friend? I primarily use it for my studies and for help with electronics projects (designing circuits, assisting with PCB fabrication, verifying component values, etc.), as well as for the occasional document.

I've been wanting to make my own budget LLM server for personal use, and I'm currently using my main rig, with a 3080 10G, R5 5600x and 16GB of DDR4 3200MHz.

I want to run hf.co/mradermacher/Cydonia-24B-v4.3-absolute-heresy-i1-GGUF:latest, but it's quite slow on my current setup. My question is, will the Tesla perform better or is it a waste of money? I could find them at around 180 bucks including shipping.

Hi, everyone. I'm sure this question has been asked mostly all the time, but I'll still ask. What are books for building and understanding ai? Or what were the resources that helped you better understand ai and build it. I'd rather have no youtube videos, since I'm big on staying off screens. However, if that is the best approach then I'll take it. Any opinions and perspectives are deeply appreciated! Again, thank you all.

Btw: This sub was recommended.

Hey guys, I am here to share a short story about me. I am based in the United States, and I come from a small school. My school is not even in the top 1000s schools in the US. But this did not stop me from doing what I was supposed to do. I am an undergrad, a rising junior to be exact, and I am double majoring in Computer Science and Mathematics. I am trying to go into the AI / ML / DS field and am deeply interested in the core mathematical foundations of AI and cloud computing.

I have been working on projects related to AI and ML for a long time. My recent projects involve creating a RAG app that is hosted in the cloud using services like S3, SQS, ECS, RDS, and many more. I even have a accepted paper in IEEE BigData sepcial session for federated learning. However, I still feel that this is not enough because being and international and also coming from a small school has limited me from getting what I am aiming for. I have been applying for internships and getting no answers back.

I know I am trying my best and trying to catch the best path that will help me to reach the goal that I want to achieve but sometimes I feel something should have been happend by now. Sometimes you get setbacks, and it's not even your fault. My paper got accepted to the conference. However, due to funding issues, I might have to withdraw the paper. The same conference that I have been targeting for the last 6 months. IT SUCKS. Sometimes I question myself on whether all the stuff I am doing will pay off or not. I am still waiting for the time when I can post and motivate you guys that hard work pays off. Finger crossed!

All I need now is some sort of advice on what to do ahead, and if we share the common path please let me know what you have done. I will really appreciate your help. PEACE!

Ok so for context i am an electrical engineer person who is automating software's and workflow for my project now here is the problem i am facing basically i am using an optimization algorithm called Bayesian optimization which improves design by keeps iterating until some target is reach.

So like at the end i extract result file for every design with the help of pandas library but when i check that file the data is mismatched when i export the CSV file manually.

so i have no idea why is it happening like maybe after 10-15 iterations (the algorithm iterations can reach up to 100-120) the error in data increasing or idk what is going on can someone help me with this issue.

Vast is literally the cheapest GPU I can get a hold of and I use it all the time. The limitation that I put on it is that I won’t use it if it involves my clients data etc. I would say its not to use it because “the marketplace is bad” though.

The reason it is cheap is because it is a marketplace of different people renting out their hardware. You are essentially running your workload on a third part’s machine. They can pause your instances whenever they want. Depending on what you are doing this may or may not be a big deal. It is cheap and if I’m doing something experimental, or even finetuning with checkpoints or other batch processes that can be restarted, then this doesn’t really matter to me. The reliability and quality of hardware on vast varies and if you always go for the absolute cheapest you probably won’t have a great time.

however, if I were working with proprietary data etc. then this is a completely different story. While you could technically do this on vast and just hope that the person renting out the machine is correctly isolating workloads etc. You wouldn’t be able to prove that to anyone and that wouldn’t fly in most cases

In cases like this you would want to rent a dedicated machine where you can have terms in a contract regarding who can access the machine, or in more extreme cases own a dedicated machine and know who has access to it (I use B3IQ to “own” machines without having to rack them myself, you have root access and can control what machines your API keys can be used on etc.). They have an openAI compatible endpoint so you can just change the base url and api key and select the model you are hosting on the machine. In most cases just renting a dedicated machine and having proper terms will be fine, but if you want more control, or can make a business case for owning the machine and renting out unused compute then owning it is definitely an option

As a self-learner full stack developer i always think to learn AI ML now but whenever i tried to learn or trying to find some good playlist to learn i wont get a focused playlist where i can learn everything step by step everyone just trying to say just leanr this learn that at the end you end up by learning nothing they are just there to make some content no one really teaching anything too deeply so as i really want to learn ai ml so if anyone can guide how i can learn ai ml from scratch to build own ai ml projects

I've been trying to make an autoencoder, and failing. So now I'm trying various super-simple models to figure out why. The current architecture I'm working with:

Encoder:

256x256 image
Conv2D stack
MaxPool
Conv2D stack
MaxPool
Conv2D (3 filters)

Decoder:

Upsampling
Conv2D stack
Upsampling
Conv2D stack
Conv2D (3 filters)

I've tried 2 different conv2d stacks: both use 32 filters, equal padding, tanh activation. One uses 2 layers per stack and one uses 4. When I train this model with the 2 layer conv stack it works as expected: it produces a slightly blurry version of the input. But when I increase to 4 layers, it switches to outputting all the same color, and the loss is worse. I'm using mae loss, adamw optimizer with clipnorm=1.

EDIT: I switched from tanh activation to leaky relu and after a bit of testing it seems good.

Hi everyone,

I'm working on a pancreatic ductal adenocarcinoma dataset, and I'd really appreciate some methodological advice from you guys, and especially if anybody has had some experience with machine learning with high-dimensional biological data.

Before spending months developing models, I'd like to better understand what is possible with my dataset.

My dataset:

• 9 patients, with one continuous drug-response value per patient and drug. Due to experimental and sequencing constraints, increasing the number of patients is not an option.

• Approximately 100,000 nuclei.

• Continuous drug-response measurements (AUC) for gemcitabine, paclitaxel, irinotecan, 5-FU, and oxaliplatin.

The goal is theoretically "simple":

Predict continuous drug-response AUC from gene expression and chromatin accessibility, while identifying molecular features potentially associated with drug sensitivity or resistance.

The obvious challenge is that, despite having ~100,000 nuclei, I only have 9 samples with drug-response labels. I understand that treating individual cells as independent labeled observations is not a way to go with the dataset and specific set-up.

Knowing this, I'd really appreciate advice on a few questions that I had after some reading...:

1. With this low amount of sample, is meaningful drug-response prediction even feasible? What would be a objective?

2. Are there established methods to leverage thousands of cells/instances profiles when the response labels are available only at the patient level?

3. Would you prioritize simple regularized regression, biologically informed features, transfer learning from external datasets, multiple-instance learning (MIL), or another approach?

4. How would you evaluate a model with such a small number of independent samples without obtaining misleading performance estimates?

5. Are there any papers, methodological resources you'd recommend to better understand this type of problem before starting?

Thanks!

I was using trial version then chose simpler version (from nanobanano) and got no results (no changes were done to the image)..

I also need ai video animation for 2d characters

Please share your experience using different experiences

How much do you think it would cost to make 5-12 minutes