“code models”的搜索结果

Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".

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帮助编写和检查PyTorch模型与训练代码，涵盖数据加载、实验可复现性、训练状态保存与恢复，并提供改善GPU显存占用和运行速度的方法。

• 编写与审查PyTorch训练代码
• 排查实验结果不一致的原因
• 改善加载速度与GPU显存占用

依据browser-use参考资料，协助编写Python浏览器自动化代码，涵盖安装、智能体与浏览器配置、模型连接、自定义工具、运行监控及费用跟踪。

• 用Python自动执行浏览器操作
• 配置智能体与浏览器认证
• 添加自定义工具和执行钩子

Animate 3D objects in Three.js using timed keyframes, bone movement, and shape changes. Play animations stored in GLTF models, control playback speed and looping, blend actions, and create motion through code.

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• Playing animations included in GLTF models
• Blending idle, walk, and run animations
• Animating facial expressions and bones

Get started with omo through a guided feature tour and workflow introduction. Optional setup migration and past-session analysis connect its features to existing work habits, while memory tools record useful preferences for future sessions.

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• Learning omo's features and basic workflow
• Migrating settings from another coding agent
• Reviewing past agent sessions for work patterns

Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.

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• Training and evaluating molecular property prediction models
• Building molecule generation workflows
• Learning representations of protein sequences and structures

Turn an OpenAPI specification, a document describing an API, into an application using the chosen framework. Generate request handlers, data models, authentication, validation, tests, and setup instructions, with database access code where applicable.

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• Build an API application from an existing specification
• Generate data models and request handlers together
• Prepare tests and setup documentation for a new project

Guide the design and review of software that handles personal data using GDPR engineering practices. Covers minimal data collection, access controls, logging, encryption, retention, deletion, and checks for code changes and infrastructure.

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• Reviewing APIs and data models that handle personal data
• Checking logs for sensitive information
• Designing retention and user data deletion processes

Review Entity Framework Core code and get improvements for database models, queries, saving changes, and migrations, which track database schema changes. Covers performance, security, testing, and how database access code is organized.

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• Review database models and relationships
• Improve slow queries and avoid repeated database calls
• Check migrations before production deployment

Analyze Modbus industrial network traffic to identify unauthorized commands, write operations, and unusual polling intervals. Compare activity with normal traffic patterns and allowed operations, then report anomalies by type and severity.

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• Investigating suspicious Modbus traffic
• Establishing normal polling patterns
• Defining allowed Modbus function codes

Build Avalonia screen logic, navigation, and step-by-step wizards using Zafiro and ReactiveUI. The skill guides ViewModels, which manage screen state and behavior, plus commands, automatic section discovery, and connections between views and their logic.

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• Organizing screen state and commands in Avalonia
• Building a wizard with multiple steps
• Setting up section-based navigation

Create interactive 3D scenes for websites, including product views and scroll-driven animations. Covers choosing Three.js, React Three Fiber, or Spline, preparing lightweight models, showing loading progress, and adapting experiences for mobile devices.

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• Building an interactive 3D product configurator
• Adding 3D scenes to a portfolio
• Animating models and cameras as visitors scroll

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

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• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Train language models with SimPO using pairs of preferred and rejected answers. Configure preference training without a separate reference model, adapt settings for base or instruction models, and address unstable training, memory limits, or lost capabilities.

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• Training a base model with preferred and rejected answers
• Further tuning an instruction model with preference data
• Adjusting training settings for math and code tasks

Plan and configure large language model training with OpenRLHF to better match human preferences. Covers training method selection, distributed GPU setup, inference acceleration with vLLM, and troubleshooting memory or training stability issues.

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• Choosing a method for training models on human preferences
• Setting up PPO training with a reward model
• Configuring GRPO training without a critic model

Deploy language models with vLLM through an OpenAI-compatible API or offline batch processing. Tune response speed, throughput, and GPU memory use with quantization, which reduces numerical precision, and execution across multiple GPUs.

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• Deploy an OpenAI-compatible language model API
• Generate text from many prompts in a batch
• Serve quantized models with limited GPU memory

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

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• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

Build image-based conversations with LLaVA, an AI model that combines image understanding with language. Covers setup, model selection, image descriptions, follow-up questions, and memory-saving options for running models on a GPU.

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• Building a chatbot that answers questions about images
• Generating image descriptions and captions
• Exploring a scene through follow-up questions

Learn how GPT language models work by preparing text, training a small model, and generating samples. Covers CPU experiments, custom datasets, further training of pretrained GPT-2 models, and reproducing GPT-2 with multiple GPUs.

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• Train a small model on Shakespeare using a CPU
• Prepare custom text for model training
• Further train a pretrained GPT-2 model

Configure LLM inference and serving on NVIDIA GPUs with TensorRT-LLM. Covers quantization, which reduces numerical precision, batching requests during generation, and distributing models across GPUs to improve processing capacity and response times.

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• Setting up an LLM server on NVIDIA GPUs
• Configuring FP8 or INT4 quantization
• Distributing large models across multiple GPUs

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

Use LitGPT to explore language model implementations and train models on custom data. Covers pretrained model loading, LoRA fine-tuning that updates a small set of parameters, pretraining, and preparing trained models for deployment.

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• Studying language model architecture through readable code
• Fine-tuning a model on custom instruction data
• Configuring LoRA training for a single GPU

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