'lms' 검색 결과

Build LLM serving workflows that produce JSON or other required formats. The skill covers reusing computation for shared prompts, tool calls, and ongoing conversations, with examples for server setup and structured output.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting article details into JSON
• Building agents that call tools
• Reusing shared prompts across conversations

Guides LLM training with verl using rewards to improve model responses. Covers data preparation, reward functions, GRPO and PPO configuration, distributed GPU training, and troubleshooting memory shortages or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train math reasoning models with GRPO and answer-based rewards
• Configure PPO training with a separate value-estimation model
• Run large-model training across multiple GPU nodes

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

Guides LLM fine-tuning with Axolotl, from YAML configuration and dataset formats to distributed training across GPUs. Covers LoRA and QLoRA, methods that train on preferences, multimodal support, and troubleshooting using documentation references and code examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare YAML settings for LLM fine-tuning
• Explore LoRA, QLoRA, and preference training options
• Check dataset formats and input handling

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Configure large language model training across multiple NVIDIA GPUs with Megatron-Core. The skill helps choose how to split models and workloads, set training parameters, monitor performance, and address memory shortages or low GPU utilization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning distributed training for a LLaMA-style model
• Distributing MoE expert networks across GPUs
• Tuning GPU utilization and training throughput

Guides PyTorch model training and fine-tuning, from choosing architectures to configuring optimizers and learning rates. Provides troubleshooting steps for unstable loss and memory shortages, plus methods for improving GPU throughput and comparing experiments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose model architectures for the available data type and scale
• Configure optimizers and learning-rate schedules
• Investigate loss spikes, NaNs, and GPU memory shortages

Helps reduce GPU memory use by loading large language models in 4-bit or 8-bit formats, a technique called quantization. Covers bitsandbytes configuration, memory checks, QLoRA fine-tuning, and training with memory-saving optimizers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load a larger model within available GPU memory
• Compare 4-bit and 8-bit quantization settings
• Fine-tune a quantized model with QLoRA

Compress large language models to 4-bit weights to reduce GPU memory needs and accelerate text generation. Covers loading compressed models, converting your own models, choosing settings, and preparing memory-efficient fine-tuning with transformers and PEFT.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run language models with limited GPU memory
• Convert your own model to 4-bit weights
• Compare compression settings for quality and speed

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Get guidance on fine-tuning language models with Unsloth, including LoRA and QLoRA methods for training with fewer resources. The skill points to documentation for understanding APIs, implementing training code, debugging problems, and exploring speed and memory optimizations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring faster training with lower memory use
• Learning about LoRA and QLoRA fine-tuning
• Checking Unsloth features and APIs

Apply HQQ quantization, which stores language model weights using fewer bits without calibration examples. Covers precision settings, inference backends, saving compressed models, deployment with HuggingFace Transformers or vLLM, and further training with LoRA.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing a model without calibration data
• Comparing 4-bit and 2-bit quantization settings
• Running quantized models with HuggingFace Transformers or vLLM

Design and improve prompts for language models, then test their accuracy and consistency. Create reusable templates, JSON output schemas that define response structure, evaluation criteria, and test cases while considering token use and model differences.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing prompts for a new AI application
• Improving inconsistent model responses
• Defining JSON response formats

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

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility

Write and debug code for further training large language models with TRL. Match training methods to dataset formats, configure supervised or preference-based training, and set up reward functions through Python or the command line.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a language model on example responses
• Training with preferred and rejected response pairs
• Debugging GRPO reward functions and batch settings

Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan AWS hosting for a language model
• Choose a serving route for an embedding model
• Plan asynchronous inference for image generation

Create a film-style opening frame or chapter card as a single HTML file. Combine warm light leaks, grain texture, large serif titles, and optional scratches or film perforations for documentaries and personal short films.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design an opening frame for a documentary
• Create a title frame for a personal short film
• Build chapter cards for a video

Build MCP servers, which let AI agents use external APIs and services through tools. The skill guides tool design, authentication, input validation, implementation, testing, and evaluation using TypeScript or Python.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connecting an external API to an AI agent through MCP
• Building an MCP server in TypeScript or Python
• Designing tool inputs, responses, and error messages

Analyze actual GA4 visits from AI services such as ChatGPT and Perplexity. Compare traffic trends, landing pages, and conversions, then identify page patterns and specific content changes that may improve visibility in AI search.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Measuring website visits from AI services
• Comparing AI traffic trends and conversions
• Finding common patterns in pages receiving AI traffic

Create short films entirely in code, using styles such as oil painting or Ukiyo-e. The skill guides style selection and production, including science and product explainers built from real source materials.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a short film in an oil painting or Ukiyo-e style
• Make science explainers using satellite imagery
• Explain product structures using CAD files

Choose AI tools for literature searches, PDF conversion, citation-backed questions, and review writing. The skill also installs and runs supported tools through a shared launcher, with workflows that connect paper retrieval, conversion, and analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing tools for a literature review
• Converting research PDFs to Markdown
• Asking citation-backed questions across PDFs

Turn mathematical claims into Lean4 proofs, checked by a theorem prover. A five-phase workflow researches existing results, designs the proof, tests candidate lemmas, implements it, and reports remaining proof gaps and any custom axioms.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Formalizing a mathematical claim in Lean4
• Finding reusable results in Mathlib
• Testing candidate lemmas for counterexamples

Get guidance on fine-tuning language models with Unsloth, including LoRA and QLoRA methods for training with fewer resources. The skill points to documentation for understanding APIs, implementing training code, debugging problems, and exploring speed and memory optimizations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring faster training with lower memory use
• Learning about LoRA and QLoRA fine-tuning
• Checking Unsloth features and APIs