1656 个 ・ 集成工具: Docker · 按人气排序
Deploy language model APIs and run batch generation with vLLM. Configure GPU memory use, quantization—lower precision model storage—and distribution across GPUs, then test response times and monitor how many requests the server handles.
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Use Phoenix to record execution traces, detailed records of an AI application's operations, and investigate problems. Evaluate answers on datasets, compare prompts and models, and set up monitoring through a self-hosted server.
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Evaluate code generation models with HumanEval, MBPP, and multilingual benchmarks. The skill guides test execution and model comparisons using pass@k, an estimate of whether at least one of k generated solutions passes the tests.
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Guides you through running large language models on CPUs, Apple Silicon, and AMD or Intel GPUs. Covers choosing GGUF model files, reducing memory through quantization, and starting an OpenAI-compatible chat server.
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Guide large language model training with OpenRLHF using human preference feedback. Covers training method selection, distributed GPU setup, reward model training, and memory tuning, with examples for PPO, GRPO, and DPO.
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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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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.
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Set up reinforcement learning, training guided by rewards, for language models with slime. The skill covers Megatron-LM training, SGLang response generation, data preparation, asynchronous execution, and custom workflows for agents that use tools across multiple turns.
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Build, test, and format CubeSandbox components using make targets and a Docker builder. Also covers the web dashboard’s development workflow, API type synchronization, and troubleshooting for missing services, outdated build tools, and environment requirements.
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Create Dockerfiles, automated build and deployment pipelines, Kubernetes configurations, and infrastructure templates. The skill also prepares deployment checks, rollback procedures, and incident response runbooks to support application releases and production operations.
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Write and review Gradio demos for Hugging Face ZeroGPU. Configure GPU time limits and dependencies, and troubleshoot errors caused by separate worker processes, shared state, concurrent requests, or CUDA package installation.
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Build and maintain machine learning apps on Hugging Face Spaces. Choose a suitable app framework and computing hardware, load models, deploy files, and check logs and outputs to diagnose build or runtime problems.
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Build and run Omnigent’s server and Postgres together with Docker Compose on a laptop or existing Docker host. Use the supplied configuration and troubleshooting guidance to rebuild images, investigate startup problems, or adapt deployment to another platform.
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Prepare and coordinate Octop Python package releases, including version updates, a Chinese changelog, and a pull request. After merging, GitHub Actions handles release tags, package and container publishing, and branch synchronization.
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Get help understanding and configuring Octop, a self-hosted AI assistant. The skill explains features, points to relevant documentation, and supports model switching, messaging connections, skill management, scheduled tasks, backups, and upgrades.
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Inspect and manage Tencent Cloud resources with tccli, its command line tool. Covers servers, networking, DNS, certificates, permissions, monitoring, and application deployment, with supporting scripts for login and identifying the current cloud instance.
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Write and review Apache SeaTunnel unit tests and E2E tests that check complete jobs using Testcontainers. It provides concrete rules for container setup, waiting, resource cleanup, and assertions to address intermittent failures and resource leaks.
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Develop and troubleshoot Apache Beam's YAML SDK. Understand its source files, run data processing pipelines locally or on Dataflow, test changes, and investigate provider issues or mismatched record fields.
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Build and test Apache Beam with Gradle, the system that manages its project tasks. Find relevant configuration files, inspect library dependencies, run formatting checks, publish locally, and investigate build failures.
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Navigate Apache Beam contributions from finding an issue to submitting a pull request, a proposed code change, and responding to reviews. Covers environment setup, tests, formatting, licensing requirements, and closer evaluation of changes to core components.
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Investigate a Sandcastle GitHub issue, classify the report, check for duplicates or existing fixes, and attempt reproduction with Docker. Return a minimal reproduction script, observed results, and a likely cause, noting any environment limitations.
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Run OpenClaw tests and reproduce bugs on remote machines through Crabbox. Choose a suitable execution provider, verify realistic user workflows, and report the actual provider, environment ID, logs, and timing.
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Set up Git workflows for Gitee, Coding.net, 极狐 GitLab, and CNB with platform-specific connection and authentication examples. Choose branching rules, standardize Chinese commit messages, and adapt automated testing and deployment configurations to your team's workflow.
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Design an OpenRig agent team around a project goal. Create team and agent specifications, role guidance, and collaboration rules, then validate the setup and diagnose problems with startup instructions or agent coordination.
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