“models”的搜索结果

Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.

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• Convert microbiome abundance tables into Waypoint inputs
• Generate sample vectors for clustering and visualization
• Configure further training with sample labels

Structure Avalonia applications with ReactiveUI and Zafiro patterns for ViewModels, the objects that manage screen state and logic. Build multi-step wizards, organize navigation, report command progress, and connect screen layouts to their supporting logic.

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• Design ViewModels for screen state and logic
• Build multi-step setup wizards
• Organize navigation and discover UI sections

Turn descriptions of 3D shapes and CAD tasks into FreeCAD Python scripts. Create models with adjustable dimensions, automate repeated operations with macros, convert meshes and solids, or build custom dialogs and workbench tools.

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• Create 3D models from written descriptions
• Build models with adjustable dimensions
• Automate repeated CAD operations with macros

Apply psychology and behavioral science to marketing decisions. The skill selects relevant thinking models, explains customer behavior, and suggests ethical changes to pricing, messaging, purchase journeys, and campaign priorities.

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• Understand why customers hesitate to buy
• Review pricing plans and how prices are presented
• Simplify signup and purchase journeys

Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.

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• Preparing reward-based training for GLM or Qwen models
• Overlapping response generation and training
• Training agents that use tools across multiple turns

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

Compress LLM weights using HQQ quantization, which stores weights with fewer bits without calibration datasets. Follow workflows for precision settings, model saving, inference integration, and LoRA fine-tuning of quantized models.

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• Compressing LLMs without calibration datasets
• Serving quantized models with vLLM or HuggingFace Transformers
• Comparing bit widths and inference backends

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.

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• 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.

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• 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.

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• 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.

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• 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

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.

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

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.

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

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.

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

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

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.

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