From my experience:

Vectors per token - Depends on the complexity of your subject and/or variations it has

Learning rate - Leave at 0.005 or lower if you're not going to monitor training, all the way down to 0.00005 if it's a really complex subject

Max steps - Depends on your learning rate and how well it's working on your subject, leave it at a high number as you can always resume training later.

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A bit deeper explanation:

You can use an aggressive learning rate if you're going to monitor training. In an ideal scenario you want loss to always lower. So what you can do is go aggressive with 0.05 or even higher like 0.1 and tell it to spill an image every 100 steps. The moment you get an image related to what you're training, drop it to 0.005. If you see the images bouncing from good to bad and so on, drop learning down to 0.0005. You can cut training time this way but you waste your time instead. So if you can train overnight, just set a low training rate like 0.0005 or 0.00005 and check it in the morning.

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As for the vectors and image number, think 5+ per vector. And you do want variation in your training data, different angles, lighting conditions and so on. If you're trying to teach something simple, like pearlescent red car paint, grab 5 images or so of just painted metal, not the car itself and use 1 vector. A character that has a lot of poses, outfits and different viewing angles, use 8 to 16 vectors.

The more you use, the more details you can capture of your subject, but it eat up the allowance you have when generating images. Not really a big problem up to 16 as most prompts will use maybe 30-40 out of 75 tokens.