20 LLM Fine-Tuning with TRL alternatives
LLM Fine-Tuning with TRL: Fine-tune language and vision models with TRL or Unsloth — supervised, preference, and reinforcement training methods. 20 agent skills do similar work, most sharing GPU, Python and Hugging Face, ranked by how much they have in common with LLM Fine-Tuning with TRL.
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by Hugging Face
Fine-tune object detection, image classification and SAM/SAM2 segmentation models on Hugging Face Jobs cloud GPUs, with dataset validation before you spend GPU minutes.
Also: Fine-tuning, GPU, Machine Learning, Python
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by Hugging Face
Train bi-encoders, rerankers, SPLADE sparse encoders and ColBERT multi-vector models with the loss, evaluator and template each type actually needs.
Also: Fine-tuning, Machine Learning, Python
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by Hugging Face
Deploy and maintain applications on Hugging Face Spaces — SDK choice, GPU hardware, model loading, and debugging.
Also: GPU, Hugging Face, Python
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by Hugging Face
Hugging Face's official CLI skill — download, upload, and manage models, datasets, Spaces, and Jobs from the terminal.
Also: Hugging Face, Machine Learning
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by NVIDIA
Trains, distils, quantises, evaluates, exports and runs inference for RT-DETR real-time object detection in NVIDIA TAO, routing training through AutoML hyperparameter search by default.
Also: Fine-tuning, GPU
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by NVIDIA
NVIDIA's official skill for building synthetic datasets with NeMo Data Designer — describe the data you want and the agent writes a declarative generation pipeline, column by column.
Also: Fine-tuning, Python
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by Hugging Face
Read-only recipes for the Hugging Face Dataset Viewer API — resolve splits, paginate rows, run text search and row filters, and pull parquet URLs and column statistics.
Also: Machine Learning, Python
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by Hugging Face
Build Gradio web UIs and ML demos in Python with the current API — components, layouts, events, and chat interfaces.
Also: Hugging Face, Python
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by NVIDIA
Diagnoses wrong gradients in differentiable NVIDIA Warp programs by measuring first — comparing autodiff against finite differences on a shrunk reproduction before proposing any fix.
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by NVIDIA
GPU-accelerated Mean-CVaR and Mean-Variance portfolio construction with NVIDIA cuOpt: scenario generation, variance-capped SOCP allocations, efficient frontiers, backtests and rebalancing.
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by NVIDIA
Turns a plain-language weather question into a working Earth2Studio inference script — picks the AI forecast model, a compatible data source, an IO backend, and the step count.
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by Hugging Face
Choose and run the right local model with llama.cpp and GGUF — quantisation, hardware fit, and local serving.
Also: Hugging Face
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by Hugging Face
Run machine learning models directly in the browser or Node with Transformers.js — no inference server required.
Also: Hugging Face
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by AMD
AMD's official skill for benchmarking vLLM/SGLang inference and driving GPU kernel optimization with Magpie and TraceLens trace analysis.
Also: GPU
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by NVIDIA
Inspects the host's hardware, OS, CUDA driver and existing tooling, recommends one Holoscan SDK install method with a reason, then hands off to the matching install skill.
Also: GPU
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by NVIDIA
Runs NVIDIA's NV-Reason-CXR-3B chest X-ray reasoning model through a documented wrapper, locally on GPU or via the public Hugging Face Space, and returns the model's full reasoning trace as engineering evidence.
Also: GPU
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by NVIDIA
NVIDIA's official skill for deploying and operating Nemotron Speech (Riva) NIMs — ASR, text-to-speech and translation, cloud-hosted or self-hosted on your own GPUs.
Also: GPU
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by NVIDIA
NVIDIA's official skill for DALI's imperative dynamic-mode API — write GPU data loading as ordinary Python, or migrate an existing pipeline-mode graph across.
Also: GPU
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by NVIDIA
NVIDIA's official onboarding skill for CUDA-Q — installs the platform, writes your first quantum kernel, picks a GPU simulator and routes you to real QPU hardware.
Also: GPU
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by NVIDIA
NVIDIA's own guidance for moving pandas workloads onto GPU DataFrames with cuDF and dask-cuDF — when it pays off, and how to keep results identical.
Also: GPU
