Tag

#vllm

Articles tagged "vllm" — 7 entries.

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 №31 inference Foundation ~3 hours of measurement · ~one line of patch
Frontier Scout

Pass@k After the Seventh Patch — Three Shapes ESamp Takes on Spark

Patches were six. The Pass@k harness surfaced a seventh — a one-line slice in the residual tap that only fires once batches shrink mid-run. Once cleared, ESamp takes three shapes: flat on saturated cells, lifting both rates on instruct headroom, and +6.67pp pass@8 on the unsaturated reasoning cell.

uses fieldkit.evalfieldkit.capabilities

Article №30 inference Foundation ~2 hours of patching · ~30 minutes of measuring
Frontier Scout

Two Patches Were Six — ESamp Lands at 97.4% on a Patched Spark

Article #2 closed at two patches. Applying them surfaced six — including the silent return-shape adapter that broke the consumer's port. Once cleared, ESamp lands at 97.4% of baseline on patched Qwen 2.5 7B, within 1.4 pp of the paper's reference.

uses fieldkit.evalfieldkit.capabilities

Article №29 inference Foundation ~2 hours — most of it watching vLLM 0.20 build inside an NGC PyTorch container; the runtime+drift diagnosis that follows is the short, sharp half
Frontier Scout

Test-Time Distilling on Spark — Same Compute Envelope, Wider Semantic Reach

ESamp adds a tiny test-time-trained probe to vLLM that converts decoding from lexical resampling into semantic exploration. The runtime is vLLM-native — and that is a Spark catalog-gap story before it is a benchmark.

uses fieldkit.evalfieldkit.capabilities

Article №05 inference NIM ~2 hours first install, ~2 minutes every restart after
Foundations

Your First NIM on a DGX Spark — What 24.8 Tokens Per Second Doesn't Tell You

First-contact notes on NVIDIA's DGX-Spark-specific Llama 3.1 8B NIM. 9.4 GB image, ~108 s warm-cache cold-start, 24.8 tok/s steady, OpenAI-compatible on :8000 — and a confidently wrong Python one-liner that clarifies what small-model FP8 buys and what it costs.

uses fieldkit.nim

Upcoming inference NIM ~30 min read
LLM Wiki

RaguTeam at SemEval-2026 Task 8: Meno and Friends in a Judge-Orchestrated LLM Ensemble for Faithful Multi-Turn Response Generation — Spark reproduction notes

Reproducing the RaguTeam SemEval-2026 T8 winning system on a DGX Spark — judge-orchestrated 7-LLM ensemble (Qwen3-4B-FP8 + Meno-Lite-0.1 7B local + remote members) with Qwen3-32B judge, then extracting the pattern into `fieldkit.ensemble` + `fieldkit.judge`.