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PointSav Documentation

The engineering library for the PointSav platform — operating systems and services for regulated businesses that own their data, their AI, and their record-keeping outright. Where the monorepo holds the code, this wiki holds the reasoning: architecture, services, security, and the governance commitments that bind future development.

PointSav Digital Systems

SLM operationalization plan

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67d6fdc8 · PointSav Digital Systems ·

fix(reference): add dated Correction callouts to 9 of 23 reference/ articles, verified against canonical origin/main — stale repo-rename references, Do-Not-Use vocabulary, wrong license claim + unbuilt service-search presented as complete, fabricated hardware specifics, sibling-article RAM-figure conflation, wrong LoRA training framework/rank/precision/GPU tier, compliance-relevant deployed-vs-planned overclaim (escalated), retired research category still listed; 14 verified clean

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@@ -47,6 +47,8 @@ This property has a practical implication for quality management: a somewhat low

## LoRA training framework

**Correction (2026-08-02, verified against canonical `origin/main`):** four specific claims below don't match the real training script (`service-slm/scripts/run-dpo-training.py`). (1) No Axolotl anywhere in the codebase — the real pipeline calls Hugging Face `peft`/`trl`/`transformers` directly (`LoraConfig`, `DPOTrainer`, `SFTTrainer`). (2) The real rank is `LORA_R = 32`, not 16 (comment: "r=32/alpha=64: a sound default"). (3) The model loads 4-bit quantized (`BitsAndBytesConfig(load_in_4bit=True, ...)`, i.e. QLoRA), not "full-precision." (4) The real Tier B "trainer" node is documented (`app-orchestration-slm/CLAUDE.md`) as an L4 with 24GB, not an A100 with 80GB — the 80GB H100 node is a *different* node ("graph," running Llama 3.3 70B for grammar, not the LoRA trainer). Minor: this article's "OLMo 3.1 32B Think" should be "OLMo 3 32B-Think" (no ".1"). **Flagged, not resolved.**

[[yo-yo-lora-training-pipeline|Adapter training]] uses the Axolotl framework, which supports the OLMo 3.1 32B Think model via the standard Hugging Face `AutoModel` interface. A per-tenant adapter trains with low-rank adaptation at rank 16, using the full-precision training path on an A100 GPU with 80 gigabytes of memory. The Axolotl configuration is parameterised by tenant-specific corpus path and output adapter path, so the same training driver handles all tenants by providing different input and output paths.

The intended training cadence is quarterly, timed to when each tenant's corpus has accumulated sufficient volume to produce a meaningful signal. An initial training run costs approximately ten to twenty USD in GPU compute at the planned instance class and window length.
Important Information

Corporate structure. PointSav Digital Systems ("PointSav") is currently a trade name of Woodfine Capital Projects Inc. ("Woodfine"), planned to become a wholly-owned Woodfine subsidiary upon incorporation. PointSav does not itself offer, sell, or solicit any security. Any securities offering associated with Woodfine's real-property direct-hold solutions is made exclusively by Woodfine, and only by means of the applicable Private Placement Memorandum.

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