Store the base once, every checkpoint as a verified delta.
Fine-tunes and checkpoints share most of their weights with the base model — so storing each one in full is mostly redundant. AT-1 stores the base once and every variant as a verified, addressable delta. Reconstruct any checkpoint byte-exact, fetch a single tensor without unpacking the whole model, and — on the aggressive tier — ship only what a task-accuracy certificate says still holds.
Three tiers, each with a guarantee
Every weight reconstructed bit-for-bit. The reference-delta packs each checkpoint against the base so shared weights aren't stored twice.
Int8 quantization of the delta with zero measured accuracy loss — same predictions, far smaller footprint per checkpoint.
The aggressive tier, gated by a task-accuracy certificate: SST-2 accuracy moved 91.06% → 90.94%, inside 0.12 points. You ship it only because the certificate proves the task still holds.
Anyone can quantize a model smaller — the risk is silently breaking it. The aggressive 19.4× tier is only claimed because we measure the task and certify it: on SST-2 the model went from 91.06% to 90.94% accuracy — a 0.12-point move. The certificate travels with the stored checkpoint, so you know the compression preserved the behaviour, not just the file size.
One command surface
Initialize a zoo on a base model, add each fine-tune as a delta, then reconstruct, verify and certify any of them on demand.
# store the base once, then each fine-tune / checkpoint as a verified delta at1 model-zoo init base.safetensors zoo/ # the shared reference at1 model-zoo add zoo/ ft-sentiment.safetensors # stored as a delta vs base at1 model-zoo add zoo/ ckpt-epoch3.safetensors # another addressable delta at1 model-zoo get zoo/ ft-sentiment --out out.safetensors # reconstruct any checkpoint at1 model-zoo verify zoo/ ft-sentiment # -> integrity: PASS at1 model-zoo certify zoo/ ft-sentiment --task sst2 # -> accuracy 90.94% (Δ 0.12 pt)
Reference-delta
Shared weights are stored once; each checkpoint keeps only its difference from the base.
Verified & addressable
Every checkpoint verifies byte-exact, and a single tensor can be fetched without unpacking the whole model.
Honest tiers
XOR-delta and quantization are known techniques — the value is the verification and the task certificate, and we only claim certified tiers.
Built for
ML platform teams keeping many fine-tunes of one base · checkpoint archives across training runs · model registries that must reconstruct any version byte-exact · anyone shipping a drop-in .safetensors replacement that stays verifiable.