Topic · AI & LLM Engineering
Best fine-tuning skills, page 5
Fine-tuning skills, ranked
Ranked by score. Sort bymost stars,trending,newest,recently updated
| # | Skill | Repository | Stars | Used in | Tokens | Auto-check | Licence | Updated |
|---|---|---|---|---|---|---|---|---|
| 193 | 193.Peft Fine-tune large LLMs with LoRA on limited GPU memory. An agent skill from Luciole-Studio/Misaka-Agent. | Luciole-Studio/ | 158 | 1 repo | ~3k | Automated safety check: Pass | MIT | yesterday |
| 194 | [omh] Fine-tuning a model on your own data -- SFT, DPO, RLVR or a LoRA adapter: decide first whether prompting or retrieval already closes the gap, choose the method from the data you have, and… | rlaope/ | 3.2k | — | ~2.4k | Automated safety check: Pass | MIT | today |
| 195 | Full PAIDF AnomalyGen pipeline — fine-tune on a new anomaly dataset, generate synthetic anomaly images (SDG), evaluate quality (nnscore), and search per-sample (guidance, cropratio) parameters. | NVIDIA/ | 3.5k | — | ~4.9k | Automated safety check: Notes | Apache-2.0 | today |
| 196 | Check progress for a detached KERMT run (pretrain, finetune, or any kermtrundetached invocation). | NVIDIA/ | 3.5k | 1 repo | ~1.8k | Automated safety check: Pass | Apache-2.0 | today |
| 197 | A skill your agent uses when validating an existing prognostic risk signature on an external bulk expression cohort with survival outcomes, producing risk scores, Kaplan-Meier curves, risk… | aipoch/ | 2k | — | ~3.2k | Automated safety check: Pass | MIT | 22 days ago |
| 198 | A skill your agent uses when running prognostic survival analysis on a clinical cohort with time-to-event data to estimate univariate and multivariable Cox proportional hazards models, export result… | aipoch/ | 2k | — | ~2.7k | Automated safety check: Pass | MIT | 22 days ago |
| 199 | AI/LLM defensive security reference: prompt-injection defense, OWASP LLM Top 10 defensive mapping, MCP and agentic tool-call hardening, training-data poisoning detection, model-output validation and… | modu-ai/ | 1.2k | — | ~4.5k | Automated safety check: Pass | Apache-2.0 | today |
| 200 | Preserve + publish a finished RL (SkyRL/GRPO) training checkpoint after the job terminates (completed at maxsteps OR early-stopped/scancelled) on an HPC cluster (Jupiter/Leonardo/Perlmutter). | open-thoughts/ | 301 | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | 10 days ago |
| 201 | Launch, relaunch, or sweep STANDARD (non-agentic) SkyRL RL on CINECA Leonardo — GRPO on math/reasoning datasets (gsm8k, MATH/aime) and on-policy distillation (OPD, teacher→student) — via raw sbatch… | open-thoughts/ | 301 | — | ~3.2k | Automated safety check: Pass | Apache-2.0 | 10 days ago |
| 202 | 202.Sft Launch Launch SFT via python -m hpc.launch --jobtype sft on any cluster (JSC Jupiter GH200, CINECA Leonardo A100, TACC Vista GH200), with EITHER backend — LLaMA-Factory (default) or axolotl (--sftbackend… | open-thoughts/ | 301 | — | ~2.9k | Automated safety check: Pass | Apache-2.0 | 10 days ago |
| 203 | Recommend and customize Megatron Bridge library and benchmark recipes for a user's model, GPU count, hardware, sequence length, and pretrain/SFT/PEFT goal. | NVIDIA/ | 3.5k | — | ~4.1k | Automated safety check: Pass | Apache-2.0 | today |
| 204 | Runs standard or fixed-channel softmax finetuning of NV-Segment-CT VISTA3D on CT NIfTI image/label datasets, with optional MONAI-native MLflow tracking and checkpoint evidence. | NVIDIA/ | 3.5k | — | ~4.2k | Automated safety check: Notes | Apache-2.0 | today |
| 205 | A skill your agent uses when operating PAIDF Curation and Retrieval or NVIDIA Cosmos Curator pipelines (split, filter, caption, embed, dedup, shard, image annotate) or PAIDF Data Mining… | NVIDIA/ | 3.5k | — | ~3.5k | Automated safety check: Pass | Apache-2.0 | today |
| 206 | CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. | NVIDIA/ | 3.5k | — | ~4k | Automated safety check: Notes | Apache-2.0 | today |
| 207 | Cosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning. | NVIDIA/ | 3.5k | — | ~3.5k | Automated safety check: Notes | Apache-2.0 | today |
| 208 | Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container when no dedicated TAO model skill matches. | NVIDIA/ | 3.5k | — | ~4.9k | Automated safety check: Notes | Apache-2.0 | today |
| 209 | NV-Tesseract AD Diffusion — diffusion-based anomaly detection and fine-tuning for multivariate time series. | NVIDIA/ | 3.5k | — | ~2.9k | Automated safety check: Notes | Apache-2.0 | today |
| 210 | NV-Tesseract Forecasting — transformer-based multivariate time series forecasting with DARR (context-enhanced kNN retrieval), interpretability, and fine-tuning. | NVIDIA/ | 3.5k | — | ~3.2k | Automated safety check: Notes | Apache-2.0 | today |
| 211 | InternVideo2-CLIP L14 (TAO videoclip) for video-text retrieval, zero-shot classification, embedding extraction, LoRA fine-tuning, ONNX export, and TensorRT deployment. | NVIDIA/ | 3.5k | — | ~3.5k | Automated safety check: Notes | Apache-2.0 | today |
| 212 | DINOv3 continual self-supervised pre-training. An agent skill from NVIDIA/skills. | NVIDIA/ | 3.5k | — | ~2.2k | Automated safety check: Notes | Apache-2.0 | today |
| 213 | Masked Auto-Encoder (MAE) for self-supervised pretraining and fine-tuning. | NVIDIA/ | 3.5k | — | ~2.9k | Automated safety check: Notes | Apache-2.0 | today |
| 214 | Apply ML/AI project delivery guidance for data exploration, feasibility, experimentation, testing, responsible AI, and operating ML systems. | managedcode/ | 138 | — | ~1k | Automated safety check: Pass | MIT | 2 days ago |
| 215 | 215.Models Add any Hugging Face image or video model, checkpoint or LoRA to Guaardvark from a URL, list what is installed, and download registry models on request. | guaardvark/ | 257 | — | ~782 | Automated safety check: Pass | MIT | today |
| 216 | Builds generative AI applications on Amazon Bedrock. An agent skill from aws/agent-toolkit-for-aws. | aws/ | 2.8k | — | ~8.6k | Automated safety check: Pass | Apache-2.0 | today |
| 217 | 217.Grpo Reference for the GRPO (Group Relative Policy Optimization) algorithm. | benchflow-ai/ | 1.8k | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | 2 mo ago |
| 218 | 218.Rl Post Training Diagnostic guide for RL-based post-training of language models (GRPO, PPO, REINFORCE, DPO). | benchflow-ai/ | 1.8k | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | 2 mo ago |
| 219 | 219.Save Trajectory Save the current conversation as a trajectory JSON file in OpenAI chat completion format for analysis and fine-tuning | AgentToolkit/ | 124 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | today |
| 220 | 220.LLM Config Configure RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation | ruvnet/ | 74k | — | ~436 | Automated safety check: Notes | MIT | today |
| 221 | Prompt Black Forest Labs' FLUX.2 [klein] — the subject/environment/style/technical hierarchy it processes in order, guidance-scale and step choices, negative prompts aimed at real failure modes, and… | nodetool-ai/ | 560 | — | ~1.4k | Automated safety check: Pass | AGPL-3.0 | today |
| 222 | Train, evaluate, and export neural networks to Simulink in MATLAB. | matlab/ | 1.1k | — | ~4.9k | Automated safety check: Pass | Unknown | today |
| 223 | 223.LLM Integration LLM integration patterns for function calling, streaming responses, local inference with Ollama, and fine-tuning customization. | yonatangross/ | 290 | — | ~2.7k | Automated safety check: Pass | MIT | today |
| 224 | Orchestration skill for NVIDIA Nemotron Speech (Riva) / NeMo ASR domain and language adaptation. | NVIDIA/ | 3.5k | — | ~3k | Automated safety check: Pass | Apache-2.0 | today |
| 225 | Used for finetuning NV-Generate-CTMR MR-Brain v1 for T1, T2, FLAIR, SWI, or MRA data from a NIfTI datalist. | NVIDIA/ | 3.5k | — | ~3.9k | Automated safety check: Notes | Apache-2.0 | today |
| 226 | Used for finetuning the NV-Generate-CTMR MAISI VAE from CT/MRI NIfTI datalists. | NVIDIA/ | 3.5k | — | ~3.1k | Automated safety check: Notes | Apache-2.0 | today |
| 227 | Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failurecount, pixel-intensity histogram) across… | NVIDIA/ | 3.5k | — | ~3k | Automated safety check: Pass | Apache-2.0 | today |
| 228 | 228.Nsfw Video Generate AI videos for mature creative projects using Wan 2.2 Spicy (LoRA-tuned for NSFW, top recommended), Wan 2.6, Seedance 1.5, Vidu Q3-Pro, and other models with relaxed content policies via… | LeoYeAI/ | 2.2k | — | ~4.6k | Automated safety check: Pass | MIT | 2 mo ago |
| 229 | Expert prompt engineering for FLUX.2 [klein] image generation and editing model. | AnastasiyaW/ | 154 | — | ~2.8k | Automated safety check: Pass | MIT | today |
| 230 | Plan or review LoRA and edit-training work specifically for FLUX.2 Klein or Qwen-Image-Edit, including paired datasets, trainer-version contracts, and held-out fidelity checks. | AnastasiyaW/ | 154 | — | ~4.5k | Automated safety check: Pass | MIT | today |
| 231 | Selects, deploys, and customizes AI models on Amazon SageMaker. | aws/ | 2.8k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | today |
| 232 | 232.Panel Operations On-demand procedures for the ComfyUI sidebar panel agent that are too long to sit in the system prompt. | artokun/ | 800 | — | ~5.8k | Automated safety check: Pass | MIT | 4 days ago |
| 233 | Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend. | jinzhezenggroup/ | 148 | — | ~3.1k | Automated safety check: Pass | LGPL-3.0-or-later | today |
| 234 | Shared Cosmos3 frontend that explicitly routes Cosmos Framework and Cosmos-RL, validates runtime model/video-dataset/SLURM inputs, consumes an SQSH or packaged backend image, optionally plans… | NVIDIA/ | 3.5k | — | ~5k | Automated safety check: Notes | Apache-2.0 | today |
| 235 | Практическая инженерия диффузионных моделей: архитектуры, обучение, инференс, оптимизация памяти. | AnastasiyaW/ | 154 | — | ~1.2k | Automated safety check: Pass | MIT | today |
| 236 | Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with… | pproenca/ | 215 | — | ~1.9k | Automated safety check: Pass | MIT | 1 mo ago |
| 237 | Analyze and apply AssemblyAI secret, region, consent, retention, deletion, and model-training controls. | jeremylongshore/ | 2.8k | — | ~1.1k | Automated safety check: Pass | MIT | today |
| 238 | Gate Together AI client, batch, fine-tuning, and deployment changes with offline contract tests plus a protected bounded live lane. | jeremylongshore/ | 2.8k | — | ~1k | Automated safety check: Pass | MIT | today |
| 239 | Analyze and diagnose Together AI authentication, billing, request, model, throttling, overload, batch, fine-tuning, and endpoint failures from redacted evidence. | jeremylongshore/ | 2.8k | — | ~1k | Automated safety check: Pass | MIT | today |
| 240 | Prepare, submit, monitor, and disposition a Together AI fine-tuning job using SDK v2, validated training data, explicit cost approval, and separate deployment verification. | jeremylongshore/ | 2.8k | — | ~1.1k | Automated safety check: Pass | MIT | today |
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