Observability

Machine that Builds Machines

Orionfold/II-Medical-8B-GGUF on Spark — five medical-reasoning variants, MedMCQA mini-eval, ChatML reasoning format

Five GGUF variants of Intelligent-Internet/II-Medical-8B (Qwen3-8B + DAPO reasoning recipe) measured on a DGX Spark. Q5_K_M lands at 36.4 tok/s, 5.45 GB, and 52% on a MedMCQA n=50 mini-eval — above F16. First reasoning recipe in the series.

uses fieldkit.quantfieldkit.publishfieldkit.evalfieldkit.lineage

Machine that Builds Machines

Orionfold/SecurityLLM-GGUF on Spark — five cyber variants, CyberMetric mini-eval, MCQ letter scoring

Five GGUF variants of ZySec-AI/SecurityLLM measured on a DGX Spark — Q4_K_M scores 40% on CyberMetric MCQ at 47.7 tok/s and 4.1 GB; the smaller variants matched or beat F16's 34%. Third vertical card; zero fieldkit source changes.

uses fieldkit.quantfieldkit.publishfieldkit.evalfieldkit.lineage

Machine that Builds Machines

Orionfold/Saul-7B-Instruct-v1-GGUF on Spark — five legal variants, LegalBench mini-eval, four-axis measurement card

Five GGUF variants of Equall/Saul-7B-Instruct-v1 measured on a DGX Spark — Q5_K_M scores 72% on LegalBench (n=50, contains) at 20 tok/s and 4.8 GB. Each card carries perplexity, sustained tok/s, thermal envelope, and a 5-task LegalBench subset score.

uses fieldkit.quantfieldkit.publishfieldkit.evalfieldkit.lineage

Machine that Builds Machines

Orionfold/finance-chat-GGUF on Spark — five variants, FinanceBench mini-eval, four-axis measurement card

Five GGUF variants of AdaptLLM/finance-chat measured on a DGX Spark — Q8_0 perplexity-matches F16 losslessly, Q4_K_M ships at 31 tok/s. Each card carries perplexity, sustained tok/s, thermal envelope, and FinanceBench accuracy.

uses fieldkit.quantfieldkit.publishfieldkit.evalfieldkit.lineage

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

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

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

Frontier Scout

AutoResearchBench on Spark — Two NIMs, One Bench, Two Failure Modes

Two Spark-tuned NIMs run AutoResearchBench's three Deep-Research example questions. Llama-3.1-8B crashes by turn 5-6 on its 8K context; Nemotron-Nano-9B-v2 finishes cleanly at 128K. Both score 0% Accuracy@1 — for completely different reasons.

uses fieldkit.nimfieldkit.evalfieldkit.capabilities

Machine that Builds Machines

Was the Agent Researching, or Flailing? An Observability Pass on the Trajectory

A8 said the LoRA mode-collapsed because the trajectory was thin. This puts numbers on it: 6 of 13 knobs ever touched, 72% of proposals repeated a prior pair, and the proposer's k=5 history window is the structural cause.

Second Brain

Ragas, Reranked — What 44 Held-Out Questions Say About the Second Brain Stack

A Ragas-style harness written in 200 lines of stdlib Python, run locally on the DGX Spark, against four variants of the Second Brain RAG chain. Naive RAG scores 3.30 / 5. Rerank RAG scores 4.27. LoRA+RAG is a surprise — it does not beat naive. Retrieval is where the points come from.

uses fieldkit.eval