Results for “benchmark”

Write reliable, maintainable Go tests using table-driven tests, subtests, benchmarks, fuzzing, and golden files following TDD methodology.

Optimize AI agents through automated evolution cycles using LLM-driven mutation of prompts, skills, and memory against measurable benchmarks.

Create new skills, modify and improve existing skills, and measure skill performance through iterative evaluation and benchmarking.

Access AI-ready drug discovery datasets, benchmarks, and molecular oracles from Therapeutics Data Commons for therapeutic machine learning and pharmacological prediction.

Provides startup metrics frameworks and stage-specific benchmarks for SaaS, Marketplace, Consumer, and B2B models, including investor-ready dashboard generation.

Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.

Measure performance baselines, detect regressions before and after PRs, and compare stack alternatives using browser, API, and build benchmarks.

Write reliable Go tests using table-driven tests, subtests, benchmarks, fuzzing, and coverage, following TDD with idiomatic patterns.

Automates security and compliance checks using OPA policies, Trivy vulnerability scanning, AWS CIS benchmark verification, and Kubernetes remediation scripts.

Audits Docker host and daemon configuration against the CIS Docker Benchmark, generating compliance reports with pass/fail/warn results and remediation steps.

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

Benchmarks inference latency and computational runtime of transformer models and MLX operations across Apple Silicon and NVIDIA GPU backends, with configurable input lengths and batch sizes.

Benchmarks a text-to-text transformer across GLUE, SuperGLUE, CNN/Daily Mail, SQuAD, and WMT, reporting GLUE average, BLEU, ROUGE-2-F, and Exact Match scores.

Benchmarks medical AI agents on synthetic EHR tasks, measuring success rates for data retrieval, temporal aggregation, and threshold-based conditional logic with exact-match scoring.

Queries Hugging Face benchmark leaderboards to find the best AI models for a task, filters by device constraints, and returns a ranked comparison table with scores.

Reduce per-token latency on Jetson vLLM servers by appending speculative decoding configuration, with guidance on when to enable and how to benchmark the improvement.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) using standardized prompts and metrics. Supports HuggingFace, vLLM, and API backends.

Benchmarks LLM bias by scoring responses to automatically generated open-ended questions across sensitive attributes, producing a composite fitness score from 0 to 5.

Calculates SaaS health metrics like ARR, MRR, churn, LTV, and CAC from raw business numbers, benchmarks them against industry standards, and provides prioritized actionable advice.

Evaluates social bias in question-answering models using the BBQ benchmark, measuring accuracy and a bias score across ambiguous and disambiguated contexts to reveal reliance on stereotypes.

Apply CIS Docker Benchmark v1.8.0 security best practices to harden Docker containers for production, covering daemon configuration, image building, runtime controls, and auditing.

Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution on local Docker, Slurm HPC, or cloud platforms.

Solves single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including constraint handling, Pareto front analysis, and benchmark problems.

Benchmarks large language models on symbolic music understanding and instruction following using text-based ABC notation, covering syntax parsing, error detection, segment-level reasoning, and sequence-level musical analysis.

Plan, configure, and chain Nemotron model customization steps into single-step or multi-step pipelines for curation, translation, fine-tuning, RL alignment, benchmarking, checkpoint conversion, optimization, and evaluation.

Solve single and multi-objective optimization problems using NSGA-II/III, MOEA/D, and other evolutionary algorithms with customizable operators, constraint handling, and benchmark problems.

Access AI-ready drug discovery datasets and benchmarks from Therapeutics Data Commons, covering ADME, toxicity, drug-target interactions, and molecular generation with standardized splits and evaluation metrics.

Quantifies the minimum accuracy gap needed between two models for a sampled micro-benchmark to reliably preserve their ranking, using the MDAD metric from Yauney et al. (2025).

Benchmarks large audio-language models against adversarial audio attacks using the CAA dataset, computing WER, ROUGE-L, cosine similarity, and coherence scores to assess robustness in conversational settings.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Evaluate LLM outputs systematically — benchmarks, automated metrics, human preference, and regression tracking

Coordinates quality assurance workflows by routing testing tasks to specialized sub-skills for API testing, performance benchmarking, test analysis, tool evaluation, and process optimization.

Benchmarks long-context language models across 20 sub-tasks spanning 3k–200k tokens, covering retrieval, reasoning, summarization, and instruction understanding, with exact-match accuracy as the primary metric.

Import object detection models from HuggingFace or NVIDIA NGC into a DeepStream pipeline with automated ONNX download, TensorRT engine build, custom parser, multi-stream benchmark, and PDF report generation.