Elastic Compute #1 nightly LoRA training pipeline

The PointSav compounding substrate requires periodic retraining to incorporate the operator interactions and editorial decisions accumulated since the previous cycle. Elastic Compute #1 is the compute node that runs this retraining nightly — a GPU-equipped cloud spot instance (Yo-Yo compute) that rebuilds the knowledge graph and produces updated LoRA (Low-Rank Adaptation) adapter weights for the platform's local language model. The pipeline operationalises the theoretical claim that every productive session improves the platform for the next one: it converts raw interaction data into model weights the next session inherits.

Elastic Compute #1 is a g2-standard-4 Google Cloud spot instance equipped with a single NVIDIA L4 GPU (24 GB VRAM). Each night it runs a two-phase, four-hour pipeline that produces fine-tuned adapter weights for the workspace language model. Phase 1 extracts structured business entities from the operator data corpus and writes them to a property graph. Phase 2 reads accumulated engineering and apprenticeship training tuples, checks whether the corpus has crossed a minimum threshold, and runs a parameter-efficient training pass against the base model. The two phases are mandatory and sequential — they cannot overlap because both require exclusive access to the L4 GPU.

[edit]Why the phases are separate

The L4 GPU serves two incompatible workloads within the nightly window. During Phase 1, vLLM loads OLMo 3 32B Think (4-bit quantised) to run entity extraction inference. During Phase 2, the QLoRA training loop loads OLMo 3 7B Think safetensors for gradient computation. A GPU cannot serve an active vLLM inference process and a PyTorch training loop simultaneously — memory addresses conflict and context switching between CUDA kernels at this scale is not supported. nightly-run.sh enforces the boundary explicitly: Phase 1 ends with stop-yoyo.sh, which drains the vLLM process and frees the GPU before Phase 2 begins. Each phase has a configurable budget, defaulting to 7200 seconds (two hours) each.

[edit]Phase 1 — DataGraph rebuild

At the start of the nightly window, start-yoyo.sh boots the Elastic Compute #1 VM and waits up to 90 minutes for vLLM to signal readiness. Once the inference server is live, jennifer-datagraph-rebuild.sh processes three document streams from the operator deployment: meeting transcript markdown files, agent research YAML and markdown files, and contact source JSON records. For each document, the script calls POST :9080/v1/chat/completions through the Doorman, which routes the payload to the 32B Think model on the Elastic Compute VM. The model returns a structured JSON array of named entities — people, companies, projects, accounts, and locations — constrained by a JSON Schema grammar so the output is machine-parseable without post-processing. The script then calls POST :9081/v1/graph/mutate on service-content to write those entities into LadybugDB. A local ledger of processed document hashes ensures each document is processed exactly once across multiple nightly runs.

At the end of Phase 1, vLLM stops and the GPU is released.

[edit]Phase 2 — Adapter training

corpus-threshold.py runs at the start of Phase 2. It counts JSONL tuples in two corpus buckets — engineering-pointsav (SFT tuples drawn from cross-cluster engineering commits) and apprenticeship-pointsav (DPO pairs drawn from the apprenticeship routing substrate). When either bucket reaches 50 tuples, the script writes a training-pending marker file and, if the SLM_YOYO_WEIGHTS_GCS_BUCKET environment variable is set, syncs the relevant corpus directory to the configured GCS bucket.

On the Elastic Compute VM, lora-training.sh polls the training-pending directory every 30 seconds. When a marker appears, it claims the marker with an atomic rename (appending .claimed), pulls the corpus from GCS, and runs QLoRA using the peft, bitsandbytes, and trl libraries.

[edit]What QLoRA is

QLoRA (Quantised Low-Rank Adaptation) is a parameter-efficient fine-tuning method that loads a base model in 4-bit NF4 quantisation and trains a small set of additional weight matrices — called an adapter — rather than updating the full model. For a 7B-parameter model like OLMo 3 7B Think, 4-bit quantisation reduces the GPU footprint from roughly 14 GB (in bfloat16) to approximately 6 GB, leaving adequate headroom on the 24 GB L4 for the training loop itself. The adapter targets seven linear projection layers: q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj, and down_proj. Training runs for two epochs with rank 16 (r=16), alpha 32 (lora_alpha=32), a maximum sequence length of 512 tokens, and gradient checkpointing enabled to manage activation memory.

The training configuration is intentionally conservative. The goal is to shift the base model toward the vocabulary, formatting patterns, and structural conventions that appear in the engineering and apprenticeship corpora — not to retrain the model on a general task. Two epochs over hundreds of tuples is sufficient for this narrow shift.

[edit]The two corpus streams

Engineering tuples are SFT (supervised fine-tuning) pairs drawn from actual commit diffs, commit messages, and review briefs across all clusters in the workspace. They teach the model the precise technical vocabulary and structural patterns used in the engineering workflow: how diffs are described, how review comments are phrased, and how implementation decisions are documented.

Apprenticeship tuples are DPO (direct preference optimisation) pairs produced by the apprenticeship routing substrate. Each pair consists of a shadow response (the model's unguided output) and a verdict response (the preferred formulation confirmed by the operator). DPO training on these pairs moves the model toward the preferred response distribution without requiring explicit labels for every token.

[edit]Adapter output and publication

When training completes, the adapter is saved to /data/weights/adapters/<tenant>/<role>/v<N>/ on the Elastic Compute VM. The adapter directory contains the LoRA weight files and tokenizer configuration — total size is typically 1 to 3 GB. lora-training.sh then signals adapter-publish.service, which uploads the adapter directory to the configured GCS bucket. The adapter is subsequently available to the workspace Doorman for loading as an inference-time weight overlay on the base model via adapter composition. The marker file is renamed to .completed when all steps succeed.

[edit]Adapter training versus continued pre-training

The nightly LoRA process is adapter training. It produces a weight delta — a few gigabytes of parameters — that the base model loads at inference time. It runs in approximately two hours on a single L4 GPU and operates over hundreds to low thousands of training tuples. The base model itself is not modified.

Continued pre-training (CPT) is a distinct operation at a fundamentally different scale. CPT would produce a new base model checkpoint by training on 50 to 100 billion tokens across 8 to 32 H100-class GPUs for one to four weeks. The cost per CPT cycle runs to tens of thousands of dollars. CPT is operator-triggered, never automated, and never scheduled as part of the nightly pipeline. The first-cut CPT target is Q1 2027, contingent on corpus volume and operator decision. Until that decision is made, all nightly training is adapter-only.

[edit]Current status

The nightly pipeline code is complete. The workspace language model service passes 177 of 177 tests. The Packer image rebuild that bakes the training Python stack (peft, bitsandbytes, trl) into the Elastic Compute VM is the next intended operator action. Once that image is deployed, lora-training.service on the Elastic Compute VM will be enabled with systemctl enable --now lora-training.service. Until the image is rebuilt, the training phase runs in marker-only mode: corpus-threshold.py writes and dispatches the GCS marker, but lora-training.sh is not yet active on the runtime VM image.

[edit]See also

• service-slm-graph-store-migration — the DataGraph rebuild that forms Phase 1 of this pipeline
• service-slm — the service that orchestrates both phases