“tune”的搜索结果

Build a repeatable process for Microsoft monthly security updates, from reviewing advisories and ranking vulnerabilities to testing, staged deployment, and verification. Helps teams set response deadlines based on risk and report remaining unpatched systems.

中文简介正在准备中。当前显示英语简介。

• Establishing a monthly Microsoft security update workflow
• Prioritizing vulnerabilities by exploitation risk
• Planning pilot and production deployment stages

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

中文简介正在准备中。当前显示英语简介。

• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Combine capabilities from multiple fine-tuned LLMs without retraining using mergekit. Choose a merging method, configure how models are blended, and evaluate the resulting model on tasks such as math, code generation, and conversation.

中文简介正在准备中。当前显示英语简介。

• Combining math, coding, and conversation capabilities
• Blending two models at different ratios
• Comparing variants made with different merging methods

Support startup AI strategy by comparing API use, fine-tuning—additional training of an existing model—and in-house development. Assess three-year costs, regulatory risk, self-hosting breakeven points, and hiring priorities to inform leadership decisions.

中文简介正在准备中。当前显示英语简介。

• Compare API use, fine-tuning, and in-house development
• Assess regulatory risks for an AI use case
• Calculate when self-hosting becomes cost-effective

Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.

中文简介正在准备中。当前显示英语简介。

• Choose a deployable model and supported hardware
• Estimate hourly deployment costs before approval
• Check deployment progress and test predictions

Prepare to fine-tune open models or Gemini models on Agent Platform. Check the environment, convert and validate training data, choose supported models and settings, and estimate tuning costs for open models.

中文简介正在准备中。当前显示英语简介。

• Checking whether an Agent Platform tuning environment is ready
• Converting and validating training datasets
• Choosing models and training settings for a task

Guides adapting OpenPI robot models to custom datasets and serving them to produce actions from observations. Covers JAX and PyTorch training, checkpoint conversion, and troubleshooting for normalization statistics and GPU memory.

中文简介正在准备中。当前显示英语简介。

• Fine-tune OpenPI models on custom robot datasets
• Convert JAX checkpoints to PyTorch
• Serve policies for ALOHA, DROID, or LIBERO

Guides fine-tuning and evaluation of OpenVLA-OFT robot models that turn images and language into actions. Covers LIBERO simulation, ALOHA deployment, LoRA adaptation that updates a small set of parameters, and troubleshooting action scaling or configuration mismatches.

中文简介正在准备中。当前显示英语简介。

• Evaluate pretrained models in LIBERO
• Fine-tune robot action models with LoRA
• Deploy server-client inference for ALOHA

Helps fine-tune large language models with LoRA and QLoRA, methods that train small sets of additional parameters. Covers memory-conscious training, configuration choices, saving adapters, and switching task-specific adapters on a shared base model.

中文简介正在准备中。当前显示英语简介。

• Fine-tune a model with limited GPU memory
• Choose LoRA settings for a specific task
• Switch task-specific adapters on one base model

Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.

中文简介正在准备中。当前显示英语简介。

• Build a pipeline to transform and aggregate large datasets
• Investigate slow Spark jobs and uneven data distribution
• Tune memory settings and data partitions

Create a WebGL background with a perspective grid, fading lines, and sparse floating particles. Add slow forward motion and gentle mouse-responsive camera movement, then tune density, color, and depth to support foreground content.

中文简介正在准备中。当前显示英语简介。

• Create a tech-style background with depth
• Add gentle mouse-responsive camera movement
• Tune fading grid lines and sparse particles

Build and tune enemy behavior for action games, including targeting, movement, attacks, retreats, and boss phases. Organize decisions into explicit states, make attacks readable to players, and check behavior with repeatable tests and browser encounters.

中文简介正在准备中。当前显示英语简介。

• Implementing enemy detection, pursuit, and retreat
• Tuning attack warnings and combat spacing
• Debugging boss phases and interrupted actions

Design, implement, tune, and test action combat for web games with clear attack and defense timing. Define reliable hit outcomes, give defensive moves distinct purposes, and verify that visual feedback matches combat results in repeatable scenarios.

中文简介正在准备中。当前显示英语简介。

• Tuning attack, guard, and dodge timing
• Checking hit detection and combat feedback
• Designing boss phases and defensive choices

Add shafts of sunlight to Three.js or similar 3D scenes. Shape rays with roofs and foliage, align them with the sun, and tune rendering effects so foreground surfaces keep their contrast while processing costs remain measurable.

中文简介正在准备中。当前显示英语简介。

• Add sunbeams through a tree canopy
• Show sunlight passing through roof gaps
• Create golden-hour architectural scenes

Implement and tune Three.js game cameras so players, threats, and objectives stay readable. Covers smooth following, isometric views, zoom and orbit limits, obstruction handling, target lock-on, camera shake, touch controls, and checks for regressions.

中文简介正在准备中。当前显示英语简介。

• Tuning follow cameras and isometric framing
• Keeping players visible around walls and tall props
• Adding lock-on behavior and temporary camera shake

Review existing interface animations and tune their timing, movement, scale, blur, and easing—the way speed changes. It matches values to their purpose and checks opening, closing, hover behavior, and staggered sequences against shared motion tokens.

中文简介正在准备中。当前显示英语简介。

• Reviewing existing interface animations
• Adjusting modal opening and closing speeds
• Fixing delays in staggered list animations

Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

中文简介正在准备中。当前显示英语简介。

• Reduce memory use in an AgentDB application
• Tune search speed while preserving accuracy
• Plan settings for millions of vectors

Fine-tune NV-Segment-CT VISTA3D on CT images and region labels in NIfTI format. Choose standard training or fixed-class softmax training, save model checkpoints and evaluation records, and optionally track training with MLflow.

中文简介正在准备中。当前显示英语简介。

• Fine-tuning with paired CT images and region labels
• Checking the training workflow with a smoke run
• Training mutually exclusive classes with softmax

Used for finetuning NV-Generate-CTMR MR-Brain v1 for T1, T2, FLAIR, SWI, or MRA data from a NIfTI datalist. Not for clinical or production data approval.

中文简介正在准备中。当前显示原文说明。

Plan domain or language adaptation for Nemotron Speech recognition. Compare word boosting, language models, and fine-tuning based on available audio, accuracy targets, and budget, then route data preparation, training, evaluation, and deployment to the appropriate sub-skills.

中文简介正在准备中。当前显示英语简介。

• Choose an approach for recognizing specialist terminology
• Plan adaptation for accents, noise, or another language
• Estimate audio data, GPU, time, and cost requirements

Tune adaptive behavior in an instrumented NeMo Relay application using runtime data. Configure tool parallelism, prompt caching, and state storage, then compare changes against a baseline to check latency, correctness, and failure rates.

中文简介正在准备中。当前显示英语简介。

• Evaluate adaptive behavior using runtime telemetry
• Tune tool parallelism against a baseline
• Evaluate prompt-cache planning

Deploy and tune DOCA's supplied ZTR RTTCC algorithm on BlueField-3 to adjust RoCE-v2 traffic under congestion. Get guidance on sample integration, build-time variants, parameter changes, and diagnosing cases where the loaded algorithm appears inactive.

中文简介正在准备中。当前显示英语简介。

• Integrating ZTR RTTCC into the supplied DOCA PCC sample
• Choosing a multipath or other build-time variant
• Investigating inactive congestion control or parameter failures

Analyze and tune AssemblyAI model choice, audio delivery, polling, Streaming v3 turns, concurrency, and downstream work. Use when pursuing measured latency or throughput goals. Trigger with "tune AssemblyAI" or "AssemblyAI latency".

中文简介正在准备中。当前显示原文说明。

Build, deploy, run, monitor, tune, and stop Simulink Real-Time applications. Use when the user wants to build a Simulink Real-Time model, load/start/stop an `.mldatx` on a target, set stop time or start options, drive root inports/playback/parameter stimulation, inspect installed or running apps, tune parameters with `getparam`/`setparam`, read ad hoc signal values with `getsignal`, manage parameter sets, control recording, or choose between file logging, live streaming, and file-log import. Covers `slbuild`, `install`, `load`, `start`, `stop`, `setStopTime`, `tg.Stimulation`, `startRecording`/`stopRecording`, `tg.FileLog.import`, `slrealtime.exportRun`, `getparam`/`setparam`, `getsignal`, and installed app management for Speedgoat targets and Simulink Real-Time Linux devices.

中文简介正在准备中。当前显示原文说明。