使用 TRL（Transformer Reinforcement Learning）或 Unsloth，搭配 Hugging Face Jobs 基礎設施來訓練或微調語言與視覺模型。涵蓋 SFT、DPO、GRPO 及獎勵模型訓練方法，以及用於本地部署的 GGUF 轉換。
此技能旨在協助開發者與 AI 代理在 Hugging Face Jobs 雲端 GPU 基礎設施上訓練或微調語言與視覺模型。它整合了 TRL（Transformer Reinforcement Learning）與 Unsloth 函式庫，支援 SFT、DPO、GRPO 及獎勵模型等最先進的訓練策略。除了雲端訓練外，還包含將模型轉換為 GGUF 格式以進行本地部署的指引。技能文件提供全面的指導，涵蓋 TRL Jobs 套件的使用、符合 PEP 723 格式的 UV 腳本、資料集準備與驗證、硬體選擇、成本估算、透過 Trackio 進行監控、Hugging Face Hub 認證、模型選擇與排行榜參考，以及模型持久化。此技能特別適用於需要雲端 GPU 訓練、GGUF 轉換，或當使用者提到要在沒有本地 GPU 的情況下於 Hugging Face Jobs 上進行訓練的任務。
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-llm-trainerInstalled? Explore more 程式設計與開發 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →
使用 TRL（Transformer Reinforcement Learning）或 Unsloth，搭配 Hugging Face Jobs 基礎設施來訓練或微調語言與視覺模型。涵蓋 SFT、DPO、GRPO 及獎勵模型訓練方法，以及用於本地部署的 GGUF 轉換。包含 TRL Jobs 套件、符合 PEP 723 格式的 UV 腳本、資料集準備與驗證、硬體選擇、成本估算、Trackio 監控、Hub 認證、模型選擇/排行榜及模型持久化等方面的指引。適用於涉及雲端 GPU 訓練、GGUF 轉換，或當使用者提到要在沒有本地 GPU 的情況下於 Hugging Face Jobs 上進行訓練的任務。
This repo contains 5 individual skills — each has its own dedicated page.
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
Send and manage iMessages via BlueBubbles, including attachments, tapbacks, edits, replies, and groups.
Control Eight Sleep pods (status, temperature, alarms, schedules).
BluOS CLI (blu) for discovery, playback, grouping, and volume.
Create, search, and manage Bear notes via grizzly CLI.
Capture frames or clips from RTSP/ONVIF cameras.
Search GIF providers with CLI/TUI, download results, and extract stills/sheets.