Results for “likert-scale”

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends for deployment with vLLM or HuggingFace Transformers.

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

Build and evaluate machine learning models using scikit-learn for classification, regression, clustering, dimensionality reduction, and preprocessing.

Build, optimize, and execute quantum circuits on IBM Quantum hardware or local simulators using Qiskit, including transpilation, primitives, and algorithm libraries.

Evaluates LLM performance using BLEU, ROUGE metrics and LLM-as-judge. Use for model testing.

Lints native Knowledge Vault markdown against the capture-llm-wiki schema, checking frontmatter, wikilinks, and citation hygiene, and writes a report.

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

Quantize LLMs to 8/4/3/2/1-bit precision without calibration data, using multiple backends and HuggingFace/vLLM integration.

Compress large language models using knowledge distillation from teacher to student models, covering temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

Create data-driven charts with Vega-Lite and Vega, covering bar, line, scatter, heatmap, area, radar, and word cloud visualizations from structured data arrays.

Expert en informatique quantique avancée (Qiskit, Cirq, algorithms, error correction, DZ research)

Scales reinforcement learning with PufferLib — high-throughput parallel training (PuffeRL), vectorized environments, and native multi-agent systems achieving 2-10x speedups over standard implementations. Use when scaling RL to millions of steps per second, running vectorized or multi-agent setups, building custom PufferEnv tasks, or integrating game environments (Atari, Procgen, NetHack, PettingZoo). For standard single-agent algorithm implementations (PPO/SAC/DQN) or quick prototyping prefer alterlab-stable-baselines3. Part of the AlterLab Academic Skills suite.

Evaluate and rank agent results by metric or LLM judge for an AgentHub session.

Master enterprise-grade Scala development with functional programming, distributed systems, and big data processing. Expert in Apache Pekko, Akka, Spark, ZIO/Cats Effect, and reactive architectures. Use PROACTIVELY for Scala system design, performance optimization, or enterprise integration.