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MarkTechPost
marktechpost.com > 09/12/2026 > implementation-of-machine-learning-workflows-with-nvidia-cuml-rapids-gpu-benchmarking-explainability-clustering-and-model-inference

Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference

19+ hour, 27+ min ago   (198+ words) We benchmark scikit-learn and cuML implementations of PCA, K-Means, nearest neighbors, logistic regression, random forests, and DBSCAN. We generate datasets on the GPU, synchronize CUDA operations for fair timing, and record the speedup each accelerated algorithm achieves. We also compare…...

MarkTechPost
marktechpost.com > 09/11/2026 > anthropic-adds-plugin-evals-to-claude-code-6-grader-types-a-no-plugin-baseline-and-a-ci-gate-for-skills

Anthropic Adds Plugin Evals to Claude Code: 6 Grader Types, a No-Plugin Baseline, and a CI Gate for Skills

2+ day, 4+ min ago   (308+ words) Deployable: Yes. It runs on Claude Code v2.1.269 or later against any directory with a plugin.json or.claude-plugin/plugin.json manifest, or a skills-directory plugin. Every eval run and judge grader is a real model call billed to your plan…...

MarkTechPost
marktechpost.com > 08/20/2026 > auditing-preference-biases-and-fine-tuning-language-models-with-direct-preference-optimization-on-anthropic-hh-rlhf-using-trl-and-lora

Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization on Anthropic HH-RLHF Using TRL and LoRA

3+ week, 3+ day ago   (648+ words) This tutorial provides an end-to-end workflow for fine-tuning language models using Direct Preference Optimization (DPO). We demonstrate how to audit the Anthropic HH-RLHF dataset for structural and length-based biases, implement a robust training pipeline using TRL and LoRA, and evaluate…...