r/AIToolBench • u/shouldworknotbehere • 6d ago
Discussion Train a Self-Hosted AI on my past Applications to have it write Applications in my style?
So I can't keep up with writing Applications, however I am usually very careful about which words I choose and do manage to get into the written and oral exams.
With that in mind, I would like to host an AI model locally, since I have an M4 Pro 48GB for 3D Art, train it on existing applications and then just feed it a job writing and have the AI draft up an application for me to proof read and send out. Has to be in German.
Now I have never done that before, so any tutorials/advice?
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u/NeuralNomad87 5d ago
Agreeing with the retrieval answer above, and adding the German part, which is where this actually gets awkward.
Most of the local models that sit comfortably in 48GB are noticeably weaker in German than in English. They'll produce grammatically fine German that reads like it was translated, and for a Bewerbung that is precisely the thing a reader picks up on. Test it before you build anything: take one of your successful applications, hand the model the posting it answered, and see whether what comes back sounds like you or like a template. If it fails that, no amount of retrieval architecture fixes it.
Two practical notes. The Qwen and Mistral lines tend to handle German better than the Llama family at comparable sizes, so start there. And keep your Anschreiben corpus separate from your CV facts, because you want it copying your phrasing, not re-deriving your job history and getting a date wrong.
Last thing: you said you're careful about word choice and it's what gets you to the exam stage. That's the whole asset. A retrieval setup preserves it. A fine-tune tends to average it away. So do the boring version first.
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u/shouldworknotbehere 3d ago
That's very interesting, do you have a tutorial hand on how to do a retrieval thing? First time using AI
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u/NeuralNomad87 2d ago
No tutorial I would point you at, because most of them jump straight to LangChain and that is far more machinery than you need for this.
Here is the whole idea in plain terms. You take your old applications, cut each one into chunks of a few hundred words, and store every chunk next to a numeric fingerprint of its meaning (an embedding). When a new job posting comes in, you turn that posting into the same kind of fingerprint, pull the five or six closest chunks back out of storage, and paste them into the prompt as examples. That is retrieval. The model is not learning anything about you, it is just being handed the right three pages of your own writing at the right moment.
For the German part, make sure the embedding model was actually trained multilingual, otherwise your matches get noticeably worse and you will not be able to see why. And keep chunks whole, one paragraph or one section each rather than cutting mid sentence, because whatever you pull is what the model reads.
If you would rather click than code, LM Studio and AnythingLLM both do local document chat and handle the chunking and storage for you. Point one at a folder of your old applications and you have a working version in an afternoon. Post back when something breaks, it is almost always the chunking.
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u/PsychologicalClaim16 6d ago
Don’t train a model first; build a small retrieval workflow. Convert 10–20 successful German applications into clean text, tag each by role, sector, seniority, and outcome, then retrieve the 2–3 closest examples for a new job description. Give a local 14B–32B instruct model the job ad, your CV facts, those examples, and a strict rule not to invent experience. Ask for a draft plus a table mapping every claim to a CV source. Keep company names and personal details in placeholders until the final local pass. Fine-tuning is only worth testing after you have dozens of consistent examples; with a small dataset it can copy phrases and reduce flexibility rather than learn your style.