'hardware' 검색 결과

Analyze firmware from routers, cameras, and other IoT devices, from extracting internal files to running services in an emulator. Follow a staged workflow to investigate vulnerabilities and reproduce findings with firmware analysis and testing tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing router or camera firmware
• Inspecting firmware update packages
• Running firmware without physical hardware

Export an approved fine-tuned model into a format suited to its hardware and runtime. Choose weight packaging and quantization, which reduces numerical precision, then compare outputs before and after export to check for regressions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exporting a checkpoint approved for deployment
• Choosing a model format for a target GPU or laptop
• Comparing outputs before and after export

Investigate recurring SGLang automated test failures by comparing logs, commit history, and GPU environments. Narrow down code changes or machine-specific problems, optionally reproduce failures remotely, and summarize the evidence in a structured report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding the change behind a recurring test failure
• Checking whether failures depend on a GPU or runner
• Reproducing a test failure on a remote GPU server

Add a model page to the SGLang Cookbook using shared templates. Organize verified launch settings and measured benchmarks, create configuration files and documentation, and update navigation, vendor cards, and page validation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add documentation for a newly supported model
• Organize verified launch recipes by hardware
• Publish measured speed and accuracy results

Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running one-off generation or batch jobs from a terminal
• Checking a new model's basic operation
• Inspecting accepted inputs before running a pipeline

Audit Secure Boot settings, revoked boot components, and EFI startup files on Linux and Windows. The skill helps identify missing protections and signs of bootkits, malicious software that runs before the operating system.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing Secure Boot after a bootkit advisory
• Checking revocation updates across managed devices
• Investigating suspected bootkit infections

Forecast sales, demand, or sensor readings with Google's pretrained TimesFM model, without custom training. Check hardware and dataset memory needs, include external factors such as prices or holidays, and obtain forecasts with uncertainty ranges.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Forecasting future sales or demand from CSV data
• Including prices and holidays in sales forecasts
• Forecasting multiple stores' time series in batches

Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing an LLM for CPU-only use
• Running a local model on Apple Silicon
• Comparing model size and quality across quantization levels

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Turn audio and video into text with Whisper, including Japanese and other supported languages. This skill covers transcription, English translation, timestamped subtitles, model selection, and batch processing, with guidance on hardware needs and recognition limitations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Transcribing recorded meetings
• Creating subtitles for videos
• Translating multilingual recordings into English text

Run language models locally with llama.cpp on CPUs, Apple Silicon, or AMD and Intel GPUs. The skill guides model setup, memory-saving GGUF format choices, interactive chat, and serving models through an OpenAI-compatible API.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a language model on a CPU-only computer
• Setting up local chat on an Apple Silicon Mac
• Choosing model quantization to fit available memory

Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running large models with limited GPU memory
• Compressing a model with domain-specific sample text
• Serving AWQ models with vLLM

Review DXF and STEP/STP files before ordering from SendCutSend.com. Compare measured geometry with current official material and service specifications, then report upload issues, manufacturing constraints, missing information, and specific edits needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check a DXF before ordering laser cutting
• Review flange dimensions and bend radii
• Check material and secondary service compatibility

Guides migration of existing AI inference workloads to self-hosted GKE, Google’s managed Kubernetes service. Covers requirements, GPU sizing, deployment configuration, model storage, validation, and traffic cutover using gcloud and kubectl.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrating an existing Cloud Run AI service to GKE
• Planning a self-hosted replacement for Gemini API inference
• Sizing GPUs and preparing deployment configuration

Investigate interruptions affecting GPU and TPU workloads on GKE using maintenance labels, monitoring queries, logs, and node scheduling restrictions. The skill helps determine whether host maintenance caused a disruption and recommends workload protection measures when maintenance is confirmed or expected.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating unexpected GPU or TPU workload interruptions
• Checking upcoming host maintenance on GKE nodes
• Tracking maintenance interruptions with PromQL

Reserve remote Android devices on Google's Developer Device Platform and connect through ADB, Android's device control tool. Check session status and expiration, extend usage time, or cancel sessions and release resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find and reserve a remote Android device
• Connect to a reserved device through ADB
• Check session status and expiration

Run model training, fine-tuning, inference, and batch jobs on Modal’s cloud GPUs. Estimate hardware needs and cost, generate Python launch scripts, run or deploy workloads, monitor logs, and retrieve results without managing remote servers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test CUDA code without a local GPU
• Run model training or LoRA fine-tuning on cloud GPUs
• Deploy a model inference API or vLLM service

Turn a broad robotics research direction into ranked ideas with concrete benchmarks and simulation plans. The workflow combines literature review, novelty checks, and external critique while accounting for hardware access, safety, evaluation metrics, and failure cases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore research topics in manipulation or navigation
• Plan simulation studies before using physical robots
• Check novelty and feasibility of robotics ideas

Explore hardware and electronic design configurations by repeatedly running programs, measuring results, and adjusting parameters. Track constraints and every trial, then report the best configuration found within the time and iteration limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tuning cache sizes and processor configuration
• Reducing circuit area while meeting timing constraints
• Comparing compiler flags for runtime or code size

Convert language models to GGUF and reduce their size through quantization, which stores model weights at lower precision. Guides cover compression choices, local inference on CPUs and Apple Silicon, and integration with tools such as Ollama.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a language model on a CPU-only computer
• Choosing model compression for available memory
• Configuring Metal acceleration on Apple Silicon

Explore training workflows that reduce harmful model responses using a written set of principles. The skill covers self-critique, revised training examples, and RLAIF, reinforcement learning from AI feedback, with implementation examples and hardware guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing principles for model safety training
• Creating training examples through self-critique and revision
• Planning reinforcement learning with AI-rated responses

Guides you through running large language models on CPUs, Apple Silicon, and AMD or Intel GPUs. Covers choosing GGUF model files, reducing memory through quantization, and starting an OpenAI-compatible chat server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a local LLM on an Apple Silicon Mac
• Trying LLM inference on a CPU-only machine
• Choosing a model format that fits available memory

Evaluate Hugging Face Hub models using inspect-ai or lighteval, with local inference or a provider-backed option. Choose an inference engine, start with a small test, and switch engines when model compatibility requires it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run benchmark evaluations on a local GPU
• Choose an evaluation framework and inference engine
• Check a small sample before a full evaluation

Build and publish a browser demo for a supplied LoRA, a model adaptation for image or video tasks. Tailor the Gradio interface to its inputs and follow the model’s recommended settings on Hugging Face Spaces.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Share a trained LoRA through a browser demo
• Build an interface tailored to image editing or video generation
• Apply recommended trigger words and generation settings