「huggingface」の検索結果

Create a small random Hugging Face model that preserves the original architecture and can serve as a local Optimum Intel repository test fixture.

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Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

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Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.

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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.

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Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.

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Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.

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Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

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Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.

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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.

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HuggingFace Transformers with biomedical LMs (BioBERT, PubMedBERT, BioGPT, BioMedLM) for scientific NLP: NER (genes, diseases, chemicals), relation extraction, QA, text classification, abstract summarization. Covers loading, biomedical tokenization, inference pipelines, fine-tuning. Alternatives: spaCy en_core_sci_lg (rule-based NER), Stanza (biomedical models), NLTK.

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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. Use when the user pastes a huggingface.co link or a .safetensors URL, asks "can it run <model>", or wants a new LoRA or checkpoint available in the Studio.

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Plan, implement, or review hugging face gradio work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests hugging face gradio work.

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Apply hugging face datasets in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses hugging face datasets or its strengths fit the task.

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Papers With Code MCP tools for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, and frameworks, and inspecting benchmark leaderboards with model-size and metric filters through the public Papers With Code catalog. Use whenever the user asks to find papers, survey literature, compare research, inspect an arXiv paper, explore AI/ML taxonomy or conferences, discover benchmarks or state-of-the-art models, or mentions Papers With Code, the PwC MCP server, or paperswithcode.co/mcp.

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Papers With Code CLI (`pwc`) for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, frameworks, and benchmark leaderboards, and submitting authenticated paper edits through the public Papers With Code catalog. Use whenever the user asks to find papers, survey literature, compare research, inspect an arXiv paper, explore AI/ML taxonomy or conferences, discover benchmarks or state-of-the-art models, or mentions Papers With Code, `pwc`, or `pwc-cli`.

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Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

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Use when tools from the jupyter, duckdb, dbt, mlflow, huggingface or postgres MCP servers appear in your tool list, or before reaching for one — what each is good for, what it can damage, and the CLI fallback when it is not connected

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Personal research-paper radar. Fetches the latest papers and code repos for the user's keyword subscriptions across arXiv, Crossref (IEEE/ACM), Semantic Scholar, GitHub, HuggingFace, Papers with Code and RSS; deduplicates against history; then YOU (the assistant) expand the queries, filter for computer-science relevance, write Chinese summaries, detect hot sub-fields, and emit a Markdown + HTML digest (optionally emailed). Use when the user asks to "run my paper radar / 文献雷达", "check for new papers", "what's new in my topics", "今天有什么新论文 / 最新文献", "today's research digest", or to configure their keyword subscriptions / sources / schedule.

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公开数据集发现入口。覆盖 Kaggle / UCI / HuggingFace / 天池。题目要求"自行 查找数据"或"补充外部数据"时启用；附件已含数据时**不调用**。

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Identify and exploit vulnerabilities in the AI Supply Chain by injecting malicious models, datasets, or dependencies. Use this skill to simulate advanced persistent threats (APTs) compromising Hugging Face repositories, manipulating pre-trained weights (Model Poisoning), and exploiting insecure deserialization during model loading (e.g., Pickle files).

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Hugging Face Inference SDK patterns for TypeScript/Node.js — InferenceClient setup, chat completion, text generation, streaming, embeddings, image generation, audio transcription, translation, summarization, and Inference Endpoints

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Post-mortem analysis of a PhysicsIntern workspace run (Claude Code, Pi, Codex, or OpenCode host). Reconstructs the trajectory from the session record — JSONL file(s) for Claude/Pi/Codex, the SQLite store for OpenCode — audits methodology adherence against the workspace's own CLAUDE.md / AGENTS.md and skill/agent prompts, checks commit discipline and flag dispositions, and assesses substantive quality. Produces a thorough evidence-anchored markdown report. Use after a workspace has been worked on to identify what went well, where the methodology slipped, and what prompts to improve.

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Scaffold a PhysicsIntern research workspace in the current folder.

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Scaffold a PhysicsIntern research workspace in the current folder. Run only when the user explicitly invokes it to set up a new physics/maths research workspace.

日本語の概要は準備中です。原文の説明を表示しています。