'hf' 검색 결과

Create and review scientific figures with Matplotlib, Seaborn, or Plotly. The skill covers panel layouts, uncertainty, missing data, accessible color choices, and exported file checks, helping prepare figures for papers, presentations, or web publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create multi-panel figures for a paper
• Show uncertainty and missing observations clearly
• Review palette contrast and color-independent cues

Look up biomedical relationships in ARAX using explicit entity types and tightly bounded paths. Inspect direct links or paths through one intermediate entity, along with returned publications and source records, to identify candidates for further research.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking recorded drug–gene relationships
• Exploring paths between a drug and a disease
• Inspecting publications and original data sources

Find scientific datasets, AI models, methodology posts, and interactive demos through the Hugging Science catalog. Compare candidates for a research task and verify their licenses, input formats, access requirements, and supported ways to run them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding models for protein or genome analysis
• Selecting datasets for scientific machine learning
• Comparing resource licenses and runtime requirements

Turn routine resume duties into concise bullets that show real contributions. Use focused questions to uncover achievements, verify numbers and individual responsibilities, and describe scope or concrete changes when reliable performance figures are unavailable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning duty lists into achievement bullets
• Describing contributions without performance metrics
• Clarifying personal contributions to team results

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

Convert GitHub resource links into proxy URLs for cloning repositories and downloading files when access is slow. Covers release attachments, raw files, repository archives, and Gist files, with a Python helper for URL conversion.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Cloning a repository when GitHub access is slow
• Downloading release attachments through a proxy
• Fetching raw files or repository archives

Build and run evaluations for AI applications using Phoenix. Start with observed failures, create checks in code or use a language model as a judge, run experiments, and validate automated judgments against human labels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning observed failures into evaluation criteria
• Building code-based or language-model evaluators
• Evaluating retrieval and answer faithfulness in RAG

Create and run evaluations in Arize using an LLM as the judge. Define scoring criteria, connect them to actual data fields, evaluate recorded operations or experiment results, and set up continuous scoring for new data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check answers for unsupported claims
• Score experiment results for correctness
• Continuously evaluate new production records

Choose SGLang Diffusion settings for a specific model, GPU, and memory budget. Compare generation speed, memory use, and output quality to select command-line options, including CPU offloading, multiple GPUs, caching, and reduced precision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing generation settings for a specific model and GPU
• Reducing GPU memory use to fit a memory budget
• Comparing performance settings across multiple GPUs

Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Measuring diffusion denoising latency
• Ranking slow operations from profiler traces
• Checking whether existing optimizations address a bottleneck

Verify file splits, function moves, module extractions, and renames by reproducing changes from a base commit and comparing files byte for byte. Generate repeatable proof scripts and reports that expose changes beyond relocation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Splitting a refactor into preparation, movement, and follow-up commits
• Generating repeatable proof scripts for code moves
• Verifying every provable commit in a branch

Analyze crude oil, gold, and copper using supply and demand, inventories, futures prices, and seasonal patterns. Combine these factors with macroeconomic conditions to produce research reports showing market direction, confidence, and specific risks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess crude oil supply and demand
• Review gold drivers such as real rates and the dollar
• Study copper inventories and economic signals

Build and review Bash scripts for automation and CI/CD, the pipelines that test and deliver software. Add input validation, error handling, safe file operations, tests, and platform checks to make script behavior easier to verify.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing error handling and input validation in Bash scripts
• Adding automated shell script tests to CI/CD
• Checking Linux and macOS portability

Guides language model training with GRPO in TRL, comparing several responses and rewarding better results. Covers dataset preparation, scoring rules for correctness and format, training configuration, monitoring, and saving models for later use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training models to follow JSON or XML formats
• Designing rewards for math and coding tasks
• Adjusting training to available GPU memory

Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting attention patterns and intermediate values
• Testing output changes by replacing internal activations
• Analyzing attention head contributions to predictions

Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models using the same benchmark tests
• Run large evaluations on a Slurm cluster
• Evaluate safety and image understanding

Improve prompts through test cases and baseline comparisons, measure how well AI answers use retrieved sources, and check agent configurations. Includes Python tools for prompt analysis, retrieval quality reports, and token estimates, with costs calculated from supplied rates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare prompt revisions against a baseline
• Measure answer quality against retrieved sources
• Check agent tool settings and loop risks

Create a Chinese interview preparation guide for a focused machine learning or LLM topic, with formula derivations, PyTorch implementations, comparison tables, and 25 questions across three difficulty levels. The workflow reviews the content and renders it as HTML.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing for an interview on a specific machine learning topic
• Studying formula derivations alongside PyTorch implementations
• Organizing interview questions by difficulty

Document how a research or coding session evolved after the work is complete. The skill extracts decisions, experiments, failed approaches, and claims into ara/ files, distinguishing user statements from AI suggestions and actions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Record the reasons behind research decisions
• Preserve failed experiments and their lessons
• Distinguish user decisions from AI suggestions

Investigate how transformer language models produce predictions with TransformerLens. This skill guides inspection of intermediate values and attention patterns, plus experiments that replace internal values to test their effects and identify components involved in model behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting attention patterns and intermediate values
• Testing output changes by replacing internal activations
• Analyzing attention head contributions to predictions

Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models on the same benchmark tasks
• Running a small evaluation sample before a full benchmark
• Running large evaluations on a Slurm cluster

Adapt large language models to specific tasks through fine-tuning, or additional training. Prepares and validates datasets, configures LoRA or QLoRA to train a small subset of parameters, and supplies training settings, evaluation scripts, and deployment optimization guidance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing and validating JSONL training data
• Choosing LoRA or QLoRA for available GPU memory
• Configuring training and monitoring overfitting

Track machine learning experiments with Trackio by recording training measurements, viewing live dashboards, and flagging diagnostic events. Retrieve metrics and alerts through command-line queries, with JSON output for automated monitoring and experiment review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording training loss and accuracy
• Flagging loss spikes or stalled training
• Monitoring cloud training through a Space dashboard

Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying a real-time model endpoint
• Scaling a sparse workload down to zero instances
• Running long inference jobs asynchronously