Stack Innovations
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Stack Innovations
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Prompting and RAG come first — they solve most problems faster and cheaper. When you still need a narrow style, a strict format, lower latency, or a smaller bill, we fine-tune a small model with LoRA, eval it before and after, and ship the version that actually wins.
A ledger of named models where fine-tuning earned its keep — cheaper inference, stricter format, lower latency, more consistent voice. Each one was prompted and RAG-tested first; only these crossed the line where a tuned model wins. 6 of 30 shown · ledger updates as models ship.
Fine-tuning is a curve, not a button. Drag the learning rate too high and the loss spikes and diverges; too low and it crawls. Shrink the dataset and the model memorises — validation loss turns back up while training loss keeps falling. That gap is overfitting, and it's the whole game.
The model is the easy part. The work is the data — curating it, holding out an honest eval, picking the smallest base that can win, and proving the tuned version beats the prompt it replaces. This is the room we work in: data, train, eval, deploy.
Before a single GPU spins up, we push prompting and RAG as far as they go and grade them on a frozen eval set. Most of the time that's the answer — cheaper, faster to ship, nothing to maintain. Fine-tuning starts only where they hit a wall.
A tuned model is only as good as its examples. We gather clean, balanced, representative pairs, strip duplicates and leakage, and freeze a held-out eval split before any training. Garbage in, confidently-wrong out — this is where most fine-tunes quietly fail.
We rarely touch the full model. LoRA and QLoRA train a small adapter on top of a frozen base — cheap, fast, and reversible. Sweep the learning rate, watch train and val loss together, and stop the moment validation turns up.
A lower training loss means nothing on its own. We score the tuned model against the prompt-only baseline on the frozen set — accuracy, format compliance, refusals, regressions. If it doesn't beat the baseline by a real margin, it doesn't ship.
A tuned small model only pays off if it's cheap to run. We quantise to int4 or int8, merge or load the adapter, and serve it behind an API on vLLM or Modal — measuring real p95 latency and cost-per-call, not just a benchmark.
Data drifts and your domain moves. We log live inputs, watch accuracy against fresh held-out samples, and retrain the adapter on a cadence — cheap, because it's only an adapter. The base stays put; the tuned layer keeps pace.
A fine-tune isn't a one-time event. Every batch of fresh production data sharpens the next adapter; every drift alert triggers a cheap retrain. Tune once and forget, and accuracy decays as your domain shifts underneath it. Monitored and retrained, the tuned model holds its edge.
The frameworks, trainers, and serving tools we actually wire together to curate, tune, evaluate, quantise, and deploy. No mystery platform — just the kit that turns a dataset into a small model that earns its place.
A free fine-tuning audit to start — bring the task you're trying to nail and a sample of your data, and we'll tell you honestly whether prompting and RAG already get you there, or whether a small tuned model is the cheaper, faster win. A straight answer, not a pitch.
Get a fine-tuning audit →