InternLM
Official@internlm · China
Offers specialized infrastructure for model integration, repository maintenance, and cross-platform communication analysis within high-performance computing environments.
Agent Skills by InternLM
Showing 15 vetted skills indexed across 2 GitHub repositories.
InternLMdocker-build
Build and push CUDA 13.0 or 12.8 LMDeploy Docker images to an inner registry.
InternLMagent-browser
Automate headless browser navigation, interaction, and data extraction with Puppeteer.
InternLM03_task6
Extract and cross-check Slack messages to draft executive summaries.
InternLM03_task1
Automate meeting scheduling by integrating email and calendar APIs.
InternLMself-improvement
Log errors, corrections, and feature requests to structured markdown files.
InternLMvideo-frames
Extract frames or short clips from video files using ffmpeg.
InternLM03_task4
Extract and reconcile Slack messages to draft project status reports.
InternLM03_task3
Extract and analyze Slack messages to identify commitments, conflicts, risks, and deliverability issues.
InternLM03_task2
Extract action items, deadlines, and requests from Slack messages.
InternLM03_task5
Prioritize support messages, route issues to teams, and draft replies.
InternLMcode-navigation
Generate a repo-wide directory tree of LMDeploy's major subsystems.
InternLMsupport-new-model
Integrate new LLMs or VLMs into LMDeploy's PyTorch backend.
InternLMcheck-env
Validate conda, Python, and CUDA setup for LMDeploy environments.
InternLMresolve-review
Fetch GitHub PR review comments, apply fixes, run pre-commit linting, and stage changes.
InternLMsubmit-pr
Automate GitHub pull request creation for LMDeploy branches with titles and bodies.
Frequently Asked Questions About InternLM
FAQPage SchemaWhat specific tasks can be performed using InternLM capabilities?▼
InternLM enables repository-wide code navigation, integration of new models into PyTorch backends, and automated GitHub pull request management. Additionally, it facilitates data extraction from Slack for project reporting, video frame processing via ffmpeg, and headless browser interaction for web-based data retrieval.
Which personas benefit most from these technical capabilities?▼
These capabilities are designed for machine learning engineers, backend developers, and technical project managers. Engineers utilize the model integration and repository maintenance features, while project managers leverage the communication analysis and message reconciliation functions to track team commitments and project status.
What are the primary prerequisites for deploying these environments?▼
Deployment requires a configured environment with Conda, a compatible runtime, and CUDA support for hardware acceleration. Users must also ensure proper authentication tokens are available for GitHub and Slack integrations to enable the respective repository management and message extraction functions.