'models' 검색 결과

Reduce the memory needed to run large language models with GPTQ quantization, which stores model weights at lower precision. Covers loading compressed models, quantizing your own models, choosing execution backends, and connecting to memory-efficient fine-tuning.

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• Run a language model within limited GPU memory
• Load a pre-quantized model for text generation
• Quantize and save your own trained model

Helps configure and troubleshoot DeepSpeed for training large models across multiple devices. Covers memory optimization, parallel training, and DeepNVMe storage transfers, with examples for tuning data movement and configuring models that divide work among specialized components.

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• Configuring distributed training for large models
• Tuning tensor transfers to NVMe storage
• Setting up models with specialized expert components

Let a NanoClaw container agent call local models running in Atomic Chat. The skill adds an MCP server, a connection that exposes model listing and text generation as tools, while Claude continues to coordinate the work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Delegate part of Claude's work to a local model
• List models available in Atomic Chat
• Generate an answer with a specified local model

Maintain an application's supported language models by checking provider availability, adding models, and keeping documentation, live tests, and model-specific request settings aligned. It also checks provider model identifiers and candidates for the recommended list.

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• Add support for a specific language model
• Check whether listed models are still available
• Update provider model identifiers

Apply behavioral science to marketing pages, pricing, ads, and customer journeys. Use a catalog of over 70 mental models to diagnose barriers to action and develop concrete recommendations suited to the audience and goal.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Diagnosing why a landing page gets few sign-ups
• Reviewing pricing tiers and discount presentation
• Improving email and ad messaging

Plan and analyze A/B tests, prepare tabular data, and train and evaluate prediction models. Estimate intervention effects from observational data, track model experiments with MLflow, and translate statistical findings into business decisions.

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• Planning A/B test sample sizes and analyzing results
• Preparing tabular data for prediction models
• Comparing model performance and recording experiments

Set up OpenWork for a team, from creating an organization and inviting members to sharing AI models and skills. It covers dashboard setup and terminal setup, with human ownership of accounts and shared access to provider keys.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create an OpenWork organization and invite teammates
• Share AI models without distributing provider keys to members
• Create a skill and share it with a team

Create, edit, and validate SDFormat files that describe robots and simulation worlds. Check coordinate frames, joints, geometry, and physical properties, review models in CAD Viewer, and report assumptions and compatibility risks before simulator handoff.

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• Create reusable robot models or simulation worlds
• Review coordinate frames, joint axes, and inertial properties
• Validate SDF files and inspect models in CAD Viewer

Add support for a new language model in MNN, from downloading and analyzing it to mapping model fields, comparing intermediate results, and exporting files. Follow staged tests, with additional paths for image, audio, and unusual architectures.

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• Adding a new text model to MNN
• Exporting a model from HuggingFace or ModelScope to MNN
• Checking intermediate outputs against the original model

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.

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

Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing a large language model into a smaller model
• Training a specialized model from teacher-generated examples
• Comparing distillation methods and training settings

Reduce LLM generation delays with speculative decoding, which proposes tokens ahead of verification. This skill compares small draft models, Medusa, and Lookahead Decoding, with implementation examples, tuning guidance, and deployment using vLLM.

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• Reducing response delays in chatbots
• Speeding up code generation
• Choosing a small model to draft candidate tokens

Guides large language model reinforcement learning with miles, including MoE models that route work to specialized components. Covers low-precision training, matching training and inference behavior, and accelerating training response generation with draft models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up reinforcement learning for large MoE models
• Configuring FP8 or INT4 training
• Checking consistency between training and inference

Compress large language model weights to four bits with AWQ, a method that uses model activation patterns to limit quality loss. Follow workflows for quantization, loading compressed models, and serving them with vLLM using less GPU memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running large language models with limited GPU memory
• Quantizing a model with domain-specific text
• Serving an AWQ model with vLLM

Implement Mamba, a model architecture that processes sequences through an internal state with computation scaling linearly with input length. Covers pretrained text generation, Mamba-1 and Mamba-2 configuration, speed comparisons with Transformers, and GPU memory troubleshooting.

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• Explore models for long sequences or streaming applications
• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations

Build Django web applications and REST APIs with Django REST Framework. This skill guides data models, input validation, views, authentication, permissions, and tests, while improving how applications retrieve related records from the database.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a REST API with Django
• Design data models and relationships
• Reduce database queries for related records

Find and run local AI models with llama.cpp. Choose GGUF model files and quantization levels that fit available memory, launch a command line session or local server, and test an OpenAI-compatible API on your computer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find models to run on a local computer
• Choose quantization for available memory
• Launch and test a local OpenAI-compatible API

Manage Hugging Face Hub through the hf command line tool. Download and upload models or datasets, organize cloud storage, manage authentication and local caches, run scheduled jobs, and deploy or maintain model inference services.

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• Download or publish models and datasets
• Sync training checkpoints to cloud storage
• Inspect and clean the local cache

Create game assets through fal.ai, including sprites, textures, 3D models, sound effects, music, voices, and video clips. Guides consistent visual variations, file conversion, model selection, and generation records so assets can be traced and regenerated.

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• Create transparent sprites and icons for a mod
• Generate seamless textures and material maps
• Build and rig a 3D character from an image

Review domain code for classes that hold data while business logic lives elsewhere. Using domain-driven design patterns, diagnose problems and refactor objects to keep business rules, state changes, and validation together, with before-and-after diffs.

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• Reviewing domain models against DDD patterns
• Finding classes that only hold data
• Moving business rules into domain objects

Create or integrate detailed 3D models with smooth outlines and believable edges for close-up views. Prepare lower-detail versions for distant viewing, prioritize loading, and measure rendering costs in the actual scene.

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• Creating a detailed 3D model for close-up views
• Replacing visibly faceted curves with smoother geometry
• Preparing detailed architectural assets for rendering

Check whether Kiln’s base models still support fine-tuning, the process of adapting a model with additional training. Compare configured identifiers and Fireworks’ allowlist with provider information, then report unsupported entries and recommend specific updates.

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• Audit support for fine-tuning base models in Kiln
• Check whether configured fine-tuning identifiers remain valid
• Find stale entries in the Fireworks allowlist

Audit Kiln’s model list against provider APIs and catalogs to identify removed models and upcoming retirements. Get findings organized by provider, then mark affected entries as deprecated after reviewing and approving the proposed changes.

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• Audit Kiln’s model list for retired entries
• Identify models approaching their retirement dates
• Check model availability for a single provider

Write and review R analysis scripts for a sewage research project using its established conventions. Estimate requested regression models, add specifications, and save LaTeX tables, figures, and model objects for use in the research paper.

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• Estimate models of sewage spills and property prices
• Add specifications to an existing regression analysis
• Create descriptive plots and maps