'lms' 검색 결과

Get guidance on adapting LLMs with Unsloth, including implementation, API usage, and debugging. Covers LoRA and QLoRA, methods for adjusting models with fewer trainable parameters, with a focus on training speed and memory use.

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• Learning how to fine-tune models with Unsloth
• Exploring LoRA and QLoRA for efficient training
• Implementing features using Unsloth APIs

Reduce GPU memory use by loading language models in 8-bit or 4-bit formats with bitsandbytes and HuggingFace Transformers. Includes configuration examples, QLoRA fine-tuning of compressed models, and optimizers that reduce memory needed during training.

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• Load larger models within limited GPU memory
• Compare 8-bit and 4-bit model settings
• Fine-tune a model with QLoRA

Get guidance on adapting large language models with Axolotl. Covers YAML configuration files, LoRA and QLoRA methods for training fewer parameters, distributed training settings, dataset formats, and debugging training code.

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• Write YAML settings for model fine-tuning
• Explore training with LoRA or QLoRA
• Configure training across multiple GPUs

Set up LLM serving with structured responses, including JSON and outputs restricted to specified patterns. Covers reusing shared prompt calculations, tool calling, and multi-turn conversations through SGLang, with examples for server setup and application integration.

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• Extract information into JSON with a defined schema
• Build agents that call tools
• Reuse shared prompts across chat requests

Fine-tune large language models with LoRA and QLoRA while limiting GPU memory use. Guides training settings and adapter management, using small sets of task-specific weights that can be saved, switched, or merged into a base model.

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• Fine-tuning an LLM with limited GPU memory
• Choosing LoRA settings and training layers
• Switching task-specific adapters on one base model

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.

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

Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill explains model selection, batch processing, and memory-saving settings, with Python examples for connecting image understanding to language models.

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• Generating captions for multiple images
• Building a question-answering system for images
• Searching an image collection with text

Extend the amount of text a language model can process at once. Compares RoPE, YaRN, ALiBi, and position interpolation, with implementation examples, additional training guidance, and memory optimization approaches for long document processing.

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• Extend an existing model to handle longer inputs
• Compare methods for representing token positions
• Plan additional training with long documents

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.

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

Configure large language model training with NVIDIA Megatron-Core across multiple GPUs. This skill helps choose how to split computation, set training parameters, monitor GPU efficiency, and address memory limits or unstable training.

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• Planning multi-GPU training for a LLaMA-style model
• Distributing MoE model experts across GPUs
• Tuning training throughput and GPU utilization

Create, edit, and reorganize Mastra documentation with guidance matched to the page type. Find the relevant rules for writing, links, navigation, shared components, diagrams, and redirects when reviewing pages or changing their structure.

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• Write or revise Mastra documentation pages
• Review sidebars and page organization
• Check redirects when moving or deleting pages

Audit and improve content intended for citation in AI answers. Review evidence, author information, and structure, generate revised content and structured metadata, and keep a local record of observed citations across AI services.

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• Audit existing articles for evidence and credibility gaps
• Revise article structure and references for AI answers
• Record citations observed across AI services

Prepare trained machine learning models for production with deployment, monitoring, and retraining workflows. Build RAG systems that retrieve documents for answers, and integrate language model APIs with retries, fallback providers, and cost tracking.

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• Deploying a trained model to production
• Setting up model monitoring and retraining workflows
• Building a RAG pipeline for document-based answers

Find official NemoClaw documentation for installation, configuration, operation, and troubleshooting. Get source-linked guidance tailored to the selected operating system, agent variant, and inference provider, with optional documentation MCP setup when requested.

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• Find installation prerequisites and first-sandbox instructions
• Look up model and inference provider configuration
• Find documented recovery steps for sandbox problems

Build AI features for Dart and Flutter applications with the Genkit Dart SDK. Provides code and documentation for conversational agents, reusable processing flows, prompt files, model integrations, interactive interfaces, and debugging through recorded execution traces.

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• Adding a conversational AI agent to a Flutter app
• Building AI processing flows in Dart
• Managing prompts in separate files

Deploy AI models on GKE with suitable GPUs or TPUs and model servers. Generate deployment configuration, adjust automatic scaling, and tune inference—the process of producing model responses—for memory use, throughput, and response time.

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• Deploying Gemma or Llama on GKE
• Choosing GPUs or TPUs for model serving
• Configuring automatic scaling for LLM requests

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.

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

Audit content for AI search by checking clear answers, supporting sources, author and brand information, and crawler access. Receive a readiness report with prioritized improvements and specific suggestions for rewriting passages.

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• Reviewing content for Google AI Overviews and other AI search
• Rewriting passages into clear, self-contained answers
• Checking crawler access for search and model training separately

Track how often ChatGPT and Perplexity cite a brand using Profound data. Review citation rates, 30-day trends, prompts where the brand appears or is absent, competing brands cited alongside it, and alerts for sudden changes.

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• Review a brand's citation rate and 30-day trend
• Find prompts where a brand appears or is absent
• Identify competing brands cited for the same prompts

Build MCP servers, which give AI agents tools for external services, using Python or TypeScript. The skill guides tool design, input validation, error handling, code review, and evaluations based on realistic tasks.

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• Build an MCP server for an external API
• Design tools around complete agent workflows
• Review input validation and error handling

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.

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• 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

Helps extend how much text a language model can process at once. Covers choosing RoPE, YaRN, ALiBi, or position interpolation, implementing position information, fine-tuning on longer documents, and managing memory during inference.

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• Extend an existing model to handle longer documents
• Compare context extension methods
• Implement position information in attention layers

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

Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill guides model selection, batch processing, and memory optimization, with examples for understanding images without training for each specific task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate captions for multiple images
• Build a question-answering system for images
• Search an image collection using text