AI & IT Solutions
Adapt a model to your domain, and prove the adaptation worked.
Seaggle runs supervised fine-tuning, preference optimisation, and reinforcement-learning post-training against your data, with the evaluation discipline to show the adapted model is better rather than merely different.
When this fits
Signals that this is the right conversation
Adaptation is where AI budgets are most often wasted. Teams fine-tune when the real gap was retrieval, train on data that leaks into their own evaluation set, or ship a model that scores better on one benchmark and worse on the work that matters. The engineering is tractable; the discipline around it is what decides whether the result is usable.
- The base model understands the task but consistently misses your domain's format, tone, or terminology
- You need reliable structured output that prompting alone has not made dependable
- Latency or unit cost demands a smaller model doing the work of a larger one
- The task has a verifiable correct answer — code, maths, extraction — and you want the model optimised against it
- Data residency or policy means the model must run inside your own environment
What Seaggle delivers
Deliverables, arranged by when they arrive
Each of these is an artifact you receive and can use without us in the room.
Assess
- Adaptation decision: prompt, retrieve, fine-tune, or post-train — argued, not assumed
- Dataset audit for coverage, labelling quality, duplication, and evaluation contamination
- Held-out evaluation designed before any training run begins
- Compute and cost plan, including whether a smaller model can carry the task
Train
- Supervised fine-tuning with LoRA / QLoRA adapters, or full-parameter where it is justified
- Preference optimisation — DPO, ORPO, or KTO depending on the signal you can actually collect
- RL post-training with GRPO or PPO where rewards are verifiable or a reward model is warranted
- Distillation from a larger teacher where latency and cost dominate
Ship
- Quantisation and serving — throughput, concurrency, and memory profiled honestly
- Side-by-side evaluation against the base model on your tasks, not public leaderboards
- Regression and safety evaluation, including capability you must not lose
- Reproducible training pipeline, weights, and documentation handed to your team
Solution patterns
Shapes this work commonly takes
These are patterns rather than products. Select one to see how it works, where a person stays in the loop, and what we design against.
Supervised fine-tuning with parameter-efficient adapters
The model can do the task but not in your format, register, or domain vocabulary.
- How it works
- Curate and deduplicate an instruction dataset, hold out a contamination-checked evaluation split, train LoRA or QLoRA adapters, and compare against the base model on your own task suite.
- Human checkpoint
- Subject experts review sampled generations before the adapter is promoted.
- Risks we design against
- Overfitting to a narrow dataset and quietly losing general capability. Mitigated with mixed-data training, held-out general benchmarks, and regression gates.
Preference optimisation (DPO / ORPO / KTO)
Correctness is not binary — you need the model to prefer one good answer over another.
- How it works
- Collect preference signal from expert comparisons or production feedback, then train directly on preference pairs without standing up a full RL loop. Where only binary good/bad signal exists, KTO fits the data you actually have.
- Human checkpoint
- Preference data is audited for annotator agreement before it is trained on.
- Risks we design against
- Reward hacking toward verbosity or sycophancy. Watched with length-controlled evaluation and adversarial prompts.
RL post-training with GRPO
The task has a checkable answer — code that must run, maths that must be right, extraction that must validate — and you want the model optimised against that signal.
- How it works
- Sample a group of completions per prompt, score them with a programmatic or model-based reward, and compute advantage relative to the group. GRPO drops PPO's separate critic network by using group-relative baselines, which materially reduces memory and moving parts for reasoning-style tasks.
- Human checkpoint
- Reward functions are reviewed before training — a badly specified reward is the fastest route to a confidently wrong model.
- Risks we design against
- Reward hacking, entropy collapse, and unstable runs. Managed with KL control against a reference policy, group-size and clipping tuning, and continuous evaluation on held-out tasks.
Representative workflow
End to end, with the checkpoints visible
A worked example of how the pieces connect in production. The static sequence below is the whole content — nothing is hidden behind animation.
- 01
Decide adaptation is right
Rule out retrieval and prompting first, in writing, with the evidence.
- 02
Build the evaluation
Held-out set defined and contamination-checked before any training begins.
- 03
Curate the dataset
Deduplicate, balance, and audit labelling quality with subject experts.
- 04
Train
SFT, then preference optimisation or GRPO where the task and reward signal justify it.
- 05
Evaluate against base
Your tasks, side by side, including the capability you must not lose.
- 06
Quantise and serve
Throughput, latency, and memory profiled at your real concurrency.
- 07
Hand over
Reproducible pipeline, weights, evaluation, and documentation to your team.
Representative example
Evaluation & controls
What separates production work from a demonstration
A demonstration proves something can happen once. These controls are how you know it keeps happening correctly.
Evaluation before training
The held-out set is built and contamination-checked before the first run. An evaluation designed after the fact tends to flatter the model it is measuring.
Regression on retained capability
Adaptation trades something. We measure what, on general benchmarks and on the tasks you cannot afford to lose, rather than reporting only the improvement.
Reward specification review
For RL post-training, reward functions are reviewed and adversarially tested before use. Most reward hacking is a specification failure, not a model failure.
KL control and stability
Divergence from the reference policy is bounded so the model does not drift into degenerate outputs while chasing reward.
Reproducible pipelines
Seeds, data versions, hyperparameters, and environment are captured so a run can be repeated by your team, not only by ours.
Data residency by design
Where policy requires it, training and serving stay inside your environment on open-weight models, and we document that boundary for your auditor.
Technology we work with
Named to explain the work rather than to imply endorsement. Tool choices follow the requirement, and we work with what you already run wherever that is sensible.
- PyTorch
- Hugging Face TRL
- PEFT / LoRA / QLoRA
- DeepSpeed
- FSDP
- vLLM
- GPTQ / AWQ
- Ray
- Weights & Biases
- MLflow
Getting started
Where a first engagement begins
A feasibility engagement: we build the evaluation, audit the dataset, and run a bounded training experiment — ending with a defensible recommendation on whether adaptation earns its cost for your task.
Discuss model adaptationQuestions
When is GRPO the right choice over DPO or PPO?
GRPO suits tasks with a verifiable reward — code that runs, maths that checks, output that validates against a schema — because you can score sampled completions programmatically. It estimates advantage from the group of samples rather than a learned value network, so there is no critic to train and less to go wrong. DPO is simpler and better when what you have is preference pairs. PPO remains reasonable when a learned reward model is genuinely required. The signal you can collect decides this, not fashion.
Do we need our own GPUs?
Rarely to start. Parameter-efficient methods make most adaptation work feasible on rented capacity, and we size the plan around that. Where data residency or sustained volume changes the arithmetic, we will say so and cost it honestly.
How much labelled data is enough?
For instruction fine-tuning, quality and coverage matter far more than volume — a few thousand well-curated examples routinely beat far larger noisy sets. The dataset audit tells you where you actually stand before you commit to collection.
What if the adapted model is not better?
Then we tell you, with the evaluation to show it, and recommend the alternative. A feasibility engagement that ends in "do not do this" has saved you the far larger cost of finding out in production.
Discuss model adaptation
Bring the problem, the current environment, and what better should look like. We will tell you what is realistic before anyone signs anything.