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Use when the user asks to change the VLM provider or model, adjust temperature or max_tokens, enable or disable thinking mode, or troubleshoot VLM-related behavior in config.yaml.\n---\n\n# VLM Configuration\n\nThe VLM (Vision Language Model) is configured in `config.yaml` under the `vlm` section.\n\n## Config Location\n\n`config.yaml` (in the project root)\n\n## Current Config Structure\n\n```yaml\nvlm:\n  provider: \"nvidia\"\n  model: \"nvidia\u002Fising-calibration-1-35b-a3b\"\n  api_key_env: \"NVIDIA_API_KEY\"\n  temperature: 0.2\n  max_tokens: 32768\n  enable_thinking: false\n```\n\n## Supported Models (NVIDIA API)\n\n| Model | Provider | Notes |\n|-------|----------|-------|\n| `nvidia\u002Fising-calibration-1-35b-a3b` | NVIDIA | Default, fine-tuned for calibration |\n| `google\u002Fgemma-4-31b-it` | Google | General-purpose 31B |\n| `meta\u002Fllama-3.2-90b-vision-instruct` | Meta | Most capable |\n| `meta\u002Fllama-3.2-11b-vision-instruct` | Meta | Balanced |\n| `microsoft\u002Fphi-3.5-vision-instruct` | Microsoft | Lightweight |\n\n## Environment Variables\n\nThe VLM uses `NVIDIA_API_KEY` (same key as the main LLM).\n\n## To Change VLM\n\n1. 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Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[589,592],{"name":590,"slug":591,"type":15},"CI\u002FCD","ci-cd",{"name":577,"slug":578,"type":15},"2026-07-14T05:25:59.97109",{"slug":595,"name":595,"fn":596,"description":597,"org":598,"tags":599,"stars":580,"repoUrl":581,"updatedAt":605},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[600,601,602],{"name":590,"slug":591,"type":15},{"name":577,"slug":578,"type":15},{"name":603,"slug":604,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":607,"name":607,"fn":608,"description":609,"org":610,"tags":611,"stars":580,"repoUrl":581,"updatedAt":619},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[612,615,616],{"name":613,"slug":614,"type":15},"Debugging","debugging",{"name":603,"slug":604,"type":15},{"name":617,"slug":618,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":621,"name":621,"fn":622,"description":623,"org":624,"tags":625,"stars":580,"repoUrl":581,"updatedAt":632},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[626,629],{"name":627,"slug":628,"type":15},"Best Practices","best-practices",{"name":630,"slug":631,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":634,"name":634,"fn":635,"description":636,"org":637,"tags":638,"stars":580,"repoUrl":581,"updatedAt":646},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[639,642,645],{"name":640,"slug":641,"type":15},"Machine Learning","machine-learning",{"name":643,"slug":644,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":648,"name":648,"fn":649,"description":650,"org":651,"tags":652,"stars":580,"repoUrl":581,"updatedAt":659},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[653,656],{"name":654,"slug":655,"type":15},"QA","qa",{"name":657,"slug":658,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":661,"name":661,"fn":662,"description":663,"org":664,"tags":665,"stars":580,"repoUrl":581,"updatedAt":670},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[666,667],{"name":577,"slug":578,"type":15},{"name":668,"slug":669,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":672,"name":672,"fn":673,"description":674,"org":675,"tags":676,"stars":580,"repoUrl":581,"updatedAt":684},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[677,680,681],{"name":678,"slug":679,"type":15},"Code Review","code-review",{"name":603,"slug":604,"type":15},{"name":682,"slug":683,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":686,"name":686,"fn":687,"description":688,"org":689,"tags":690,"stars":580,"repoUrl":581,"updatedAt":693},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[691,692],{"name":654,"slug":655,"type":15},{"name":657,"slug":658,"type":15},"2026-07-14T05:25:54.928983",{"slug":695,"name":695,"fn":696,"description":697,"org":698,"tags":699,"stars":580,"repoUrl":581,"updatedAt":702},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[700,701],{"name":513,"slug":514,"type":15},{"name":590,"slug":591,"type":15},"2026-07-30T05:29:03.275638",496]