Tag

#peft

Articles tagged "peft" — 6 entries.

Article №34 fine-tuning NeMo ~18.5 hours wall (50 T²PO steps + three evals)
Frontier Scout

T²PO on Spark — When the Training Pool Says 28/32 and Held-out Says 9/158

T²PO's two deltas on the Phase 6 ClawGym harness: mean turns 5.00 → 4.61, task_complete 154/158, but the per-assertion ceiling stays flat at 47.7%. The strongest training-side step (45) is the worst held-out checkpoint — pool saturation lies on a single Spark.

uses fieldkit.capabilitiesfieldkit.evalfieldkit.training

Article №33 fine-tuning NeMo ~9 hours wall (34 GRPO steps + two evals)
Frontier Scout

ClawGym GRPO on Spark — Closing the Loop the SFT Adapter Couldn't

Phase 5 SFT taught the agent to keep working but never to stop. 34 GRPO steps with a shaped reward unlearn the failure mode — same model, same base, same LoRA-init, but task_complete climbs 0/158 → 154/158, mean turns drop 12 → 5, and per-assertion still inches up +3.1 pp.
Article №32 fine-tuning NeMo ~3 days end-to-end (mostly waiting on rollouts)
Frontier Scout

ClawGym on Spark — A 7B Base, A LoRA Adapter, and the +15 pp the Adapter Earned

ClawGym shipped only a .github profile, so we built the substrate ourselves — persona task synth, sandbox harness, 200-task corpus, LoRA SFT, matched-base eval. The adapter earns +3.8 pp task pass and +15.0 pp per-assertion against its own base. The diagnostic is the lift.

uses fieldkit.nim

Article №25 fine-tuning NeMo Customizer ~2 hours wall — 4 min LoRA training, 4 min race, the rest writing
Machine that Builds Machines

Distilling the Architect — A 3B LoRA Trained on the Agent's Own Trajectory

A4's 50-iter trajectory becomes training data for a Qwen2.5-3B LoRA proposer. Holding out 8 iters, the 3B mode-collapses onto d_model=768 (the trajectory's most-frequent keep) and matches 0 / 8 exact; the 8B at T=0.5 matches 4 / 8 of its own past picks.
Article №14 fine-tuning Hugging Face PEFT + Qwen2.5-3B-Instruct ~45 minutes end-to-end — 5 min corpus via NIM 8B, 69 s training, 3 min benchmark, plus a 6 GB base-model download
Second Brain

LoRA on Your Own Q&A — What 231 Pairs Actually Teach a 3B Model

231 own-voice Q&A pairs, a rank-16 LoRA, 69 s of training on a GB10 Spark. The adapter won't memorize your exact numbers, but it will take a model that refuses 61% of questions about your work and turn it into one that answers all of them in your voice. For facts you still need RAG.

uses fieldkit.eval

Upcoming fine-tuning NeMo Customizer + Nemotron Nano 9B v2 planned ~4 hours per sweep
LLM Wiki

LoRA on Nemotron Nano — Fine-tuning a 9B Without Blowing Unified Memory

A planned walk through LoRA fine-tuning on Nemotron Nano 9B with NeMo Customizer: rank and alpha sweeps, a tiny domain corpus, and the memory accounting that keeps a PEFT run from tripping the Spark's 128 GB unified-memory wall.