Results for “hgb”

Estimates the memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub, using HTTP Range requests without downloading weights.

Trains and fine-tunes vision models for object detection, image classification, and segmentation using Hugging Face Transformers on cloud GPUs, with automatic dataset validation and Hub persistence.

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

Estimates GPU memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub using HTTP Range requests, without downloading weights locally.

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends for deployment with vLLM or HuggingFace Transformers.

Estimates memory requirements for running Hugging Face models, including optional KV cache, using HTTP range requests without downloading weights.

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Search the Hugging Face Hub for llama.cpp-compatible GGUF models, select the right quantization, and run them locally with llama-cli or llama-server.

Execute QA test plans on Holoscan Sensor Bridge hardware by reading a test document, filtering tests by setup, running automatable tests with pass/fail evaluation, and producing a structured report.

Create, deploy, and debug machine learning applications on Hugging Face Spaces using Gradio, Docker, or Static SDKs, with support for ZeroGPU and dedicated hardware.

Discover, select, and run Holoscan Sensor Bridge example applications on a connected devkit over SSH, filtering by platform, board type, and sensors.

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware, with backend selection between vLLM, Transformers, and accelerate.

Resolves, generates, and operates on media assets (audio, images, icons, logos, voice, color grades, LUTs) for HyperFrames projects, using a local cache and the HeyGen CLI for free-usage catalog search and TTS.

Manage Hugging Face Hub resources: download/upload models, datasets, spaces; manage repos, buckets, collections, discussions, and cache; run SQL queries on datasets; authenticate and manage tokens.

Selects the correct SageMaker serving container image URI for HuggingFace model deployments, prioritizing HuggingFace-curated Deep Learning Containers over generic alternatives.

Runs batch processing and high-performance computing (HPC) workloads on Google Kubernetes Engine (GKE), including job queues, parallel processing, and MPI workloads.

Operate Hugging Face Hub repositories, models, datasets, and Spaces via the hf CLI, including downloads, uploads, authentication, and compute jobs.

This skill identifies adult childhood cancer survivors with isolated growth hormone deficiency and prior hypothalamic‑pituitary axis radiation exposure who require retesting to confirm persistence of GHD. Triggers include clinician questions such as “Should I retest for GHD in this adult survivor with childhood GHD and radiation history?” or “Is repeat GHD evaluation indicated in this patient?”

HuggingFace hf CLI: search/download/upload models, datasets.

Recommends low-dose GH replacement in patients with cured acromegaly and documented GHD in the absence of known contraindications. Triggers include managing a cured acromegaly patient with documented growth hormone deficiency.

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends and integrations with HuggingFace Transformers, vLLM, and PEFT/LoRA.

Manages a local GitHub knowledge base and searches GitHub via the gh CLI, covering repos, issues, and pull requests.

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.

Manage Hugging Face Hub resources via the `hf` CLI: download and upload models, datasets, and spaces; manage buckets, cache, collections, discussions, and inference endpoints; run SQL queries on datasets.

Access the Human Metabolome Database (HMDB, 220K+ metabolites), searching by name, HMDB ID, or structure to retrieve chemical properties, biomarker data, NMR/MS reference spectra, and associated pathways. Use when identifying a human metabolite, looking up its biomarker or disease associations, matching NMR/MS spectra, or running metabolomics annotation. Part of the AlterLab Academic Skills suite.

Provides Apple Human Interface Guidelines for designing input methods across Apple platforms, including gestures, Apple Pencil, keyboards, game controllers, pointers, Digital Crown, eye tracking, spatial interactions, focus systems, remotes, motion sensors, and nearby interactions.

Suggests testing HPA axis functionality before and after starting GH replacement in patients not receiving glucocorticoid replacement with apparently normal pituitary-adrenal function. Triggers include initiating GH replacement in a GHD patient who is not on glucocorticoids and has normal adrenal function.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

llama.cpp local GGUF inference + HF Hub model discovery.

Hash Indexes

Prüft Frist, Form, Zuständigkeit, Rechtsweg und Sofortmaßnahmen im Betreuungsrecht und liefert eine Fristen- und Risikoampel mit Sofortschritten.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.