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CUDA · Fine-tuning
Skills
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| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 1 | Set up the NVIDIA "Build an Agent" DevX workshop as a working JupyterLab environment from INSIDE a locked-down OpenShell/NemoClaw sandbox, and hand the user the token URL + access commands. | brevdev/ | 146 | — | ~5.2k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 2 | A skill your agent uses when the user wants to set up LLM training for the first time, or when traininghub is not yet installed/configured in the current environment. | Red-Hat-AI-Innovation-Team/ | 100 | — | ~959 | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 3 | Guides users through LLM post-training with Training Hub, including installation, algorithm selection (SFT, OSFT, LoRA), hyperparameter tuning, troubleshooting OOM errors, interpreting loss curves… | Red-Hat-AI-Innovation-Team/ | 100 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | 3 days ago |
| 4 | Guide users through Cosmos3 supervised fine-tuning (SFT) post-training: preparing the example dataset and Wan2.2 VAE, converting the base checkpoint to DCP, launching distributed training (paired… | NVIDIA/ | 560 | — | ~2.7k | Automated safety check: Pass | Unknown | yesterday |
| 5 | Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). | wshobson/ | 40k | — | ~2k | Automated safety check: Pass | MIT | 6 days ago |
| 6 | Finetune a pretrained KERMT encoder on a labeled CSV. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.6k | 1 repo | ~4.1k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 7 | Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. | NVIDIA/ | 3.6k | 1 repo | ~1.5k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 8 | NV-Tesseract AD Diffusion — diffusion-based anomaly detection and fine-tuning for multivariate time series. | NVIDIA/ | 3.6k | — | ~2.9k | Automated safety check: Notes | Apache-2.0 | 2 days ago |
| 9 | NV-Tesseract Forecasting — transformer-based multivariate time series forecasting with DARR (context-enhanced kNN retrieval), interpretability, and fine-tuning. | NVIDIA/ | 3.6k | — | ~3.2k | Automated safety check: Notes | Apache-2.0 | 2 days ago |
| 10 | Generate inorganic material structures using MatterGen, a diffusion-based generative model. | learningmatter-mit/ | 176 | — | ~1.8k | Automated safety check: Pass | MIT | 3 days ago |
| 11 | Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, PEFT + SP input re-gather, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM… | NVIDIA/ | 3.6k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | 2 days ago |
| 12 | A skill your agent uses when training or debugging a neural net in PyTorch — the forward/loss/backward/step loop and its silent bugs, mixed precision (AMP), AdamW/LR schedules, DDP/FSDP/ZeRO… | ericrisco/ | 180 | — | ~3.4k | Automated safety check: Pass | MIT | yesterday |
| 13 | 13.Llama Cpp Operate, configure, benchmark, and troubleshoot llama.cpp across CPU, Metal, CUDA, HIP/ROCm, Vulkan, SYCL, and hybrid or multi-GPU systems. | magnus919/ | 115 | — | ~2.3k | Automated safety check: Pass | MIT | yesterday |