AI & GenAI · SLM fine-tuning workbench
Small models, fine-tuned for your task — on evidence, not guesswork.
A project-based workbench for fine-tuning small language models (about 0.5B to 8B parameters) with LoRA/QLoRA. AI agents do the setup work; your people approve every decision; every result is traceable and reproducible.
Why small language models
Frontier models are overkill for narrow tasks.
01
3–10× lower unit cost
For extraction, classification, tagging and validation at volume.
02
Private by default
Run in your cloud or on-premises; sensitive data stays in your environment.
03
Fast on modest hardware
Served on T4-class GPUs or even CPU for many tasks.
04
Distilled from the best
Use a large LLM to curate and label training data, then run the SLM in production.
How it works
Five gated steps, one source of truth.
Every gate is a person's approval, and every approval saves a new version of the project manifest.
01
Set up the project
Describe the use case; an agent drafts the output schema, prompt and success metrics.
02
Prepare the data
Upload, clean, label and top up with synthetic data; publish a versioned dataset with a frozen test set.
03
Pick a base model
Benchmark candidate models on the test set and select the best fit on evidence.
04
Fine-tune
Settings filled in automatically for the model and hardware; every run tracked and versioned.
05
Test and promote
Compare against the base model, measure speed on target hardware, promote to the registry.
Features and benefits
Built for teams that need results they can trust.
Feature
Benefit
Project manifest
A versioned record of every decision, with author and diff.
One source of truth — nothing changes silently.
AI agents, human approval
Requirements, data, model-selection, tuning and evaluation agents propose; people approve.
Expert-level setup in hours, with full control kept by your team.
Frozen golden test set
Created early and never used for training.
Honest evaluation you can show to stakeholders.
Evidence-based model selection
Candidates benchmarked for accuracy and speed, filtered by size, licence and language.
The right base model, not the fashionable one.
Infra-aware auto config
Training settings adapt to T4, L4, A10G or A100; pre-flight memory checks.
Fewer failed runs and lower GPU spend.
Reproducible by default
Every model links to its exact data, config and code; export to GGUF, quantised or merged.
Audit-ready models, ready to serve on GPU or CPU.
Runs on the hardware you have
Infrastructure profiles built in.
T4
16 GB
Low-cost QLoRA training; inference testing
L4 / A10G
24 GB
Standard training for models up to ~8B
A100
40 / 80 GB
Fast training, larger batches, longer inputs
CPU
System RAM
Inference and speed testing
0.5B–8B
parameter models, fine-tuned with LoRA / QLoRA
5
AI agents doing the setup work
100%
of runs traceable and reproducible
Have a narrow task running on a frontier model? Let’s fine-tune it.
Bring one use case; we will benchmark base models, fine-tune and show accuracy, speed and cost against your current setup.