Hey, i am learning ai model training so i want you to suggest me usecase for which i should create my first ai model.
Requirements
- I have a100 with 80gb ram so something that fit in it easily
hey man, thats really cool you are getting into ML. but look, we have several types of models (like: CNNs, RNNs, transformers)
if you are a student like me, I reccomend getting started with transformers.
also one more thing. if you ask help from a chatbot, they will say you can train big models like 30B+ models, but please stick to 230M-4B LoRA training for FT, and under 250M for PT.
also there are two main types of attention: bi directional and casual. I recommend finetuning a 350M LFM model with r32 a64 LoRA (not QLoRA. you have enough compute). then you should ry more models like qwen3.5-800M and maybe the new gemma4 models as well!
then once you are comfortable with things like loss masking, you should do a small PT run with around 50B tokens. try training a 120M model (it has just enough parameters to not dissapoint you if u didnt plan ur data quite right, so its just neough. maybe bumping it to 150M will make a considerable change as well. although some papers i read said that 141M is efficient, so go from somewhere around 120-150M)
for the PT architecture, use an LLM for the math and code. try to learn the high level things like how you calculate loss for PT, context length and position encoding.
for the architecture, use something like qwen3-600M and scale it down (smollm2’s 135M model’s arch works as well!)
since you have an A100, you can train this really quickly so you can comfortably learn things yourself. something with this field is that no matter how good you are with the math or science, if you havnt don anything practical, knowing just math and science wont help you.
then you should slowly write your own code and math. I also like this books (I am in HS yr10, but if u are above HS yr8 ish) you should read this book (the ebook is free, but the physical on is a bit expensive). wait, i actually have two books if u like reading.
- this is one is pretty good: Understanding Deep Learning (I am still reading both these books, but they teach a lot)
- https://mml-book.github.io/
if you know some math skim this a bit: (book – second link)
but I reccomend you to read both these together!
and for the love of god, please dont get advice from LLMs (I learnt it the hard way) 
BTW, you have a lot of compute, but dont waste it on big models, try to work on smaller models and then once you know what you are doing, scale up to bigger models.
here is the list of models I like FT:
- qwen3.5-800m and 2b
- gemma4 2b (the projectors kinda mess things up, so avoid this for now)
- LFM2.5 models (tbh for reason these models take very little VRAM. I am using unsloth zoo. the only problem is the license, but you are using them to learn, so… dont really care about the license)
- gemma3-270M
also here is a fun challeng before doing PT, try doing CPT (continuid pre training) on smolLM2 135M (to expand its context length) you got the VRAM for it so dont worry about it. Then do a chat FT to chat with it. and also you can try doing a reasoning FT as well, but try doing the normal user/assistant one, then you can later redo the FT on the CPTed model for the reasoning dataset (which has <think> [thought process] </think> XML tags, and the model can think like a human)
eitherways, I will give you some advice: be sensible on how you are using your compute!
Thanks, I’ll keep that in mind. I’ll work with 150m models first to understand all the basics and advance concepts only then I’ll move to bigger models. Help appreciated 
What helped me most when I fine-tuned my first model: write down how you made the labels. People ask about the training config, but the labels are where the project lives or dies. A short paragraph explaining how you verified them makes your work actually reusable.
Second thing. Keep some data locked away for evaluation, and if your data has any time or group structure, hold out whole sessions instead of taking a random shuffle. Random splits leak adjacent frames and timestamps, so your eval will overstate the model.
If any of the data belongs to someone else, publish the weights plus a description of the data and keep the raw material to yourself. Model cards with honest limitations get more respect than perfect sounding numbers.
What kind of model is it? If you say what it does, people here will give you useful specific feedback.
One use case that fits your plan well is a small text classifier, for example sorting short feedback messages into a few topics. It is a good first project because the data is easy to label, the result is easy to check, and a small model will train fast on your A100. Start with a simple baseline first, then fine tune the small model, and keep a held out set aside so you can honestly compare the two. That comparison teaches you more than chasing a bigger model, and it gives you a clear result to show at the end.
Nice that you have an A100 80GB, that is more than enough room for a first run, so I’d stop treating hardware as the question and pick the use case from something you actually care about getting right. A first model teaches the most when you can tell by eye whether it’s good.
A concrete one: take a small model (Qwen or Gemma at 0.5 to 1B) and fine tune it on a task where you can write about 200 labeled examples yourself, like sorting your own notes or messages into categories. Let AI write the training script and the data loading. Spend your own time on what Reaver1000 pointed at: building the eval set and reading the wrong answers, because that is what tells you whether training did anything. A from-scratch pretraining run can wait until you have done one fine tune end to end.
What do you want to do with this in six months: build a product, get a job, or do research? That changes which use case I’d pick.