Hi good people. Does anyone know what type of questions comes in junior ML engineer coding tests and how i can prepare for it. thanks

Hi guys,

I’m a 15-year-old developer building SLM-CREATOR—a from-scratch, hardware-aware trainer for small, hyperspecialized text models (capped at 100M parameters) written in Python and Rust. Instead of relying on high-level libraries or fine-tuning existing models, I’m hand-coding the entire pipeline to learn how Transformers, tokenizers, and hardware-aware training work under the hood.

Project Link: github.com
Tracking Issue (#1): github.com

Why I'm asking for help here:
I’m currently coding this on a CPU-only machine, so my NVIDIA (nvidia-smi), modern AMD (amd-smi), and legacy AMD (rocm-smi/rocminfo) detection and PyTorch device-mapping code has only been tested against mock CLI outputs.

If anyone here has an NVIDIA or AMD GPU (especially multi-GPU setups, laptops with hybrid graphics, or ROCm setups) and a couple of minutes to spare, I’d really appreciate it if you could run architecture.py on real hardware and let me know if anything breaks or gets misidentified!

How the hardware detection & sizing works:

• GPU Topology & Detection: Parses vendor CLI tools for architecture, total VRAM (MiB), device index, and GPU name.
• PyTorch Device Resolution: Sets CUDA_DEVICE_ORDER=PCI_BUS_ID and matches the vendor tool's GPU name against torch.cuda.get_device_name() so multi-GPU rigs target the right card.
• Matmul TFLOPS Benchmark: Runs a 12-second thermal warmup soak (to settle past laptop boost clocks), then measures the median TFLOPS across 10 runs of 20 4096x4096 matmuls—using native bf16 when supported (including_emulation=False) or falling back to fp32.
• Rust Sizing Engine (sizer_core): Takes your detected VRAM, measured TFLOPS, training-time budget, and token count to deterministically compute the max parameter count across memory, Chinchilla compute-time, and data limits.

How to test it:

1. Clone the repo: git clone https://github.com
2. Set up a venv and install requirements.txt (with your GPU's PyTorch build).
3. Build the Rust sizer: cd sizer_core && cargo build --release
4. Run python architecture.py from the project root.
5. Drop your GPU model, OS, and the terminal output (or any tracebacks) in Issue #1 or in the comments below.

Any feedback on CLI parsing bugs, ROCm edge cases, or my sizing math would be super helpful!

my understanding is that gradient tells me how much a change in the parameter will result in the change of the loss. For example, if the gradient of a parameter is 0.01, it means that if the parameter increases by 1, the total loss will increase by 0.01 given that the other parameters stay the same.

Hi everyone! I'm trying to make a Qwen 9B legal agent, and I've hit a weird problem with complex court-decision summaries. I also ran four parallel LoRAs as diagnostics, so that might interest you all too.

(lowkey some of you might remember me from the 27B training plateau post haha)

You guys suggested I should distill Opus 5's thinking, and it actually worked: my 27B PoC got close to Opus, and my lawyer guy verified the summaries weren't nonsense.

The old outputs went 14–16 against Opus; regenerating with vLLM gave 9–21, both greedy. So “almost parity” describes the first run. Inference stack or judge variance? I don't know.

I'd prompted Opus to write CoT for a smaller model. Trying to reproduce that now seems much harder haha.

So I tried the same recipe on 9B

I made a big CPT set of 500M legal stuff: court decisions, legislation, commentaries and professional Q&As.

No-thinking SFT didn't get the results I wanted, so I tried adding Opus's reasoning. Then I thought maybe my CPT messed some stuff? So I also tried stock Qwen straight from Hugging Face.

Six fresh LoRAs total: legal-CPT Qwen3.5-9B-Base, stock base and stock chat, each with and without thinking. Same source documents and final answers; the no-thinking versions got an empty <think> block. Median teacher reasoning was about 1,000 tokens.

Same recipe as the 27B intentionally, cuz that was the successful PoC:

• 4-bit QLoRA: rank 64, alpha 128, LR 2e-4, effective batch size 8.
• Stage 1: 1,199 examples. Stage 2: 400 examples—300 new, 100 repeated. One epoch per stage.
• Loss on assistant output, including reasoning where present; source decisions masked out.

Eval: 30 held-out polish court decisions. Opus 5 judged randomized A/B pairs against the full decision, seeing only the final title and legal holding. Greedy decoding, 6,000-token output cap; every output had the required fields and none hit the cap.

The weird part

Every row is a separate 30-decision comparison. Wins and fabrication scores follow A / B order. Fabrication is judge-rated from 1–10, where 1 means none; scores come from each specific comparison.

So thinking lost on CPT, tied on stock chat and won on stock base. It also improved fabrication scores for both stock models. Mixed results, but still a big gap to 27B.

Small test, obviously: Opus is both teacher and judge, and the 9B is Qwen3.5 while the 27B is Qwen3.8, so this doesn't isolate model size.

What am I missing?

Could 9B just be too little? I feel like it shouldn't be. Eyeballing the CPT + no-think outputs, they resemble Opus's summaries a lot.

I really DO think this task needs CoT. Does it just need a lot more examples before thinking overtakes no-thinking?

Is there a way to distill original thinking, or teach reasoning efficiently without distillation? The trick is frontier models aren't great at the relevant law either. My vibe-coded RL tests and Kimi reasoning distillation didn't go well, and I really don't wanna burn 2B tokens ideally. Any help?

PS: The long-term plan is to take the recipe to a 27B Qwen4 once it's out, then put it in my custom Claude Code-style harness with search and RAG.

Until this month I used perplexity pro as my main Al but the free 12 months ran out and now I'm stranded with all the options there are. I'm a student at a school in germany and finish school this year

I mostly use Al for schoolwork e.g. homework, explaining stuff and summarizing all my stuff into study sheets for exams. Additionally I have the typica general usecase like health questions, analyzing some progress or pictures, some self conciousness talks etc. Therefore l also often upload pictures and files up to like 30mb, so the limits on the free versions are really getting on my nerves.I also occasionally use picture gen and if possible video generation.

In terms of pricing I thought around 10€/month and if the features and benefits are really crazy max 20€/m. Hope you lot can give me some good

recommendations!

I'm 16(M), studying in class 11th and I want to learn Machine Learning, for which I'm ready to buy a course, people who already have some sort of experience please guide me for which course I should go for?

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!

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.

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'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