Layer 3 of 9
Reuse giants; specialize safely.
Weights store everything the optimizer discovered — billions of floats distilled from data and schedules.
Fine-tuning nudges subsets toward domain nuance; LoRA freezes backbone tensors while learning thin adapters.
Foundation checkpoints ship from labs under licenses — Deadwood tracks lineage automatically.
Quantization maps FP32 tensors into INT8/INT4 blocks with calibration datasets.
Distillation trains compact students on teacher logits; merges fuse complementary adapters.
Without custodianship, your team inherits every sharp edge below.
Typical DIY cost
Opinionated APIs wire custodied data, runners, and proofs together — no boilerplate archaeology.
from deadwood import ModelLibrary
library = ModelLibrary()
model = library.select(
task="classification",
constraint="memory",
hardware="rtx4090",
)
finetuned = library.finetune(
model="llama-7b",
data=clean_data,
method="lora",
)ModelLibrary aligns checkpoints with custodied datasets — mismatched tokenizers never silently corrupt batches.
Quantization presets adapt per SKU — LoRA ranks resize automatically when VRAM budgets swing.