Results for “kilosort4”

Quantize LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss using bitsandbytes. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers.

Operate, troubleshoot, secure, upgrade, and automate Kubernetes clusters and workloads safely across upstream Kubernetes, k3s, RKE2, MicroK8s, k0s, Talos, OpenShift/OKD, kind, Minikube, Rancher-managed clusters, EKS, AKS, and GKE. Use when a task involves kubectl, Kubernetes APIs, Pods, Deployments, StatefulSets, Services, Ingress or Gateway API, CRDs, RBAC, NetworkPolicy, storage, scheduling, autoscaling, cluster lifecycle, or the bundled agent-first k8s-cli.

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

Quantize large language models to 4-bit with minimal accuracy loss using GPTQ, enabling deployment of 70B+ models on consumer GPUs with 4× memory reduction and 3-4× faster inference.

Reference for configuring and using LangChain4j vector stores, covering setup, search, filtering, and ingestion.

Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.

Quantize large language models to 4-bit using activation-aware weight quantization, achieving ~3x speedup with minimal accuracy loss for deployment on limited GPU memory.

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

Lint and format Python code for Megatron-LM using ruff, black, isort, pylint, and mypy, with commands for autoformatting and import ordering.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Provides guidance on enabling and optimizing GKE Cluster Autoscaler, including Node Auto Provisioning, troubleshooting scale-up/down issues, and best practices for capacity management.

Train, evaluate, distill, quantize, export, and run inference for PyTorch-based TAO image classification models with support for multiple backbones.

BISON v2.0 — Conviction Holder (Hardened). Top 10 assets by volume. All signals are score contributors — no hard gates. Scanner enters via create_position internally (Wolverine pattern). RatchetStop exits. Thesis exit REMOVED. v2.0: every hard gate converted to score contributor, ensureExecutionAsTaker=false, conviction-scaled margin 25-37%.

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.

Calculate risk-based position sizes for long stock trades using fixed fractional, ATR-based, or Kelly Criterion methods with portfolio constraints.

v4.1.0 reward function redesign to fix overtrading and DSR dominance

Author or debug Kubernetes manifests with distinct probes, right-sized resources, autoscaling, and safe reversible rollouts.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Kinetics-400: A Large Video Understanding Dataset

Quantize large language models to 4-bit precision using activation-aware weight quantization, reducing memory footprint and speeding up inference with minimal accuracy loss.

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.

Runs NCBI BLAST+ 2.17.0 sequence searches from the command line: makeblastdb (with -parse_seqids), blastn/blastp/blastx/tblastn with tabular -outfmt 6/7 for parsing, correct -task choice (megablast vs blastn vs blastn-short), -taxids/-negative_taxids taxonomic scoping, and -mt_mode multithreading; plus a DIAMOND blastp --ultra-sensitive path for large protein searches. Warns that -max_target_seqs is a heuristic keep-count, not a top-N best-hits filter. Use when the user wants command-line BLAST, makeblastdb, a local BLAST database, blastn/blastp/blastx/tblastn searches, or DIAMOND protein search. For the Bio.Blast web NCBIWWW API prefer alterlab-biopython; for quick one-liner database lookups prefer alterlab-gget. Part of the AlterLab Academic Skills suite.

Dense, machine-readable API reference for PyFixest — high-dimensional fixed-effects OLS/WLS/IV and Poisson (feols, fepois, feglm), clustered/robust standard errors, R-style formula syntax, and post-estimation. Use when writing or debugging Python fixed-effects regressions with the pyfixest package.

Assesses organizational readiness for post-quantum cryptography migration per NIST FIPS 203/204/205 standards, performs cryptographic inventory scanning, evaluates hybrid TLS configurations, and validates CRYSTALS-Kyber and CRYSTALS-Dilithium readiness.

Training Data Lifecycle Management (v5.4.2)

Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

Checks on-chain agent trustworthiness, discovers agents for tasks, profiles agents, looks up wallet/identity records, contacts or dispatches agents, and reads or writes hosted metadata via the 8K4 Protocol (ERC-8004).

Score and rank options contracts for any ticker using a multi-factor model covering liquidity, IV attractiveness, Greeks balance, and risk/reward. Returns scored option chains with the best contracts highlighted.

Create C4 architecture diagrams at context, container, and component levels with Mermaid syntax, including templates and styling tips.

Industry and market research. Market sizing, structure, competition, regulation, supply chains, pricing, M&A. Produces a single compact, source-cited research report with Porter's Five Forces, HHI / CR4, PESTLE, and trade flows. Supports UK, US, EU, Australia, and global scope, with multi-geography comparison.

SHARK v3.0 — SM Conviction + Liquidation Cascade Hunter. Consolidated from v1.0's 8-cron pipeline into a single scanner. 4-gate entry: SM concentration (30+ traders, 5%+) → top 5 trader alignment → price momentum → funding structure. Score 8+ to enter. DSL manages all exits. No thesis exit. DSL exit managed by plugin runtime via runtime.yaml.

High-fidelity ASCII/Braille rendering via the Risomorphism-1911 pipeline — edge-aware downsampling, presets, quality gates, and eikon mirror workflows

Returns structured prediction-market data for Polymarket, Kalshi, Limitless, Opinion, Predict.Fun, dFlow, and UMA oracle via a local API, covering markets, cross-venue search, leaderboards, smart money, wallet analytics, identity clustering, and resolution status.