Hugging Face · For data scientists

Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.

Orchestra-Research/AI-Research-SKILLs

Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.

Orchestra-Research/AI-Research-SKILLs

Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.

Orchestra-Research/AI-Research-SKILLs

Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.

huggingface/skills

Routes a sentence-transformers training task to the right model type and required reference docs and example scripts, covering bi-encoders, rerankers, sparse and multi-vector models.

huggingface/skills

Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.

huggingface/skills

A skill your agent uses when adding or migrating non-thumbnail images for a Hugging Face Blog post.

huggingface/blog

Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al.

JimLiu/science-skills

Guide for adding a new model to the Archon engine. An agent skill from areal-project/AReaL.

areal-project/AReaL

Complete agent-ready workflow for Qwen-family MTP or nextn GGUF conversion and release.

R6410418/Jackrong-llm-finetuning-guide

Shows how to load, train and use fast Hugging Face tokenizers, with BPE, WordPiece and Unigram models, padding, truncation and alignment tracking.

Orchestra-Research/AI-Research-SKILLs

Indexes research papers on the Hugging Face Hub from arXiv, links them to models and datasets, claims authorship and generates markdown research articles from templates.

huggingface/skills

Trains or fine-tunes language and vision models with TRL or Unsloth on Hugging Face Jobs cloud GPUs, then converts the results to GGUF.

huggingface/skills

Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods.

Orchestra-Research/AI-Research-SKILLs

Finds llama.cpp-compatible GGUF models on the Hugging Face Hub, picks a quantization for your hardware and launches them with llama-cli or llama-server.

huggingface/skills

Trains and fine-tunes object detection, image classification and SAM or SAM2 segmentation models on Hugging Face Jobs cloud GPUs and saves the results to the Hub.

huggingface/skills

Prioritize and run Modal conference-deadline agents on high-ROI conferences only.

huggingface/ai-deadlines

Add a new HuggingFace-supported VAE to the stage1 tokenizer pipeline.

End2End-Diffusion/diffusion-bench

A skill your agent uses when adding support for a new model to VeOmni.

ByteDance-Seed/VeOmni

Integrate new evaluation datasets into FlagEvalMM as benchmark tasks.

flageval-baai/FlagEvalMM

Builds reusable command line scripts that fetch, enrich or process data from the Hugging Face API, aimed at chained, repeated or automated tasks.

huggingface/skills

Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs.

waybarrios/opencode-power-pack

Logs and visualizes ML training metrics with Trackio, firing alerts for issues like loss spikes, and syncing a live dashboard to a Hugging Face Space.

huggingface/skills

Benchmarks code generation models with the BigCode Evaluation Harness across HumanEval, MBPP, MultiPL-E and other suites using pass@k metrics.

Orchestra-Research/AI-Research-SKILLs

Produces ranked, evidence-grounded next steps when an ML experiment has stalled, drawing on a Leeroopedia knowledge base or on fetched docs and issues.

Leeroo-AI/superml

Adds distributed and mixed-precision training to a PyTorch script with a few Accelerate lines, then launches it on one GPU, many GPUs or DeepSpeed and FSDP setups.

Orchestra-Research/AI-Research-SKILLs

Explores Hugging Face datasets through the read-only Dataset Viewer API: list splits, preview and page through rows, search, filter, and fetch parquet links and statistics.

huggingface/skills

Fetches Hugging Face paper pages as markdown and reads paper metadata through the papers API when you share a paper URL, an arXiv link or an arXiv ID.

huggingface/skills

Uses Ray Data to read, transform and write large datasets across a cluster for ML training and batch inference, with streaming execution and optional GPU steps.

Orchestra-Research/AI-Research-SKILLs

Best practices for multi-step Python tasks including data analysis, HuggingFace datasets, token counting, and any task requiring state across multiple python() calls.

A-EVO-Lab/a-evolve

Checks training code, configs and math against documented framework behavior before an expensive run, citing a knowledge base or official docs for every claim.

Leeroo-AI/superml

Finds top models for a task from official Hugging Face benchmark leaderboards, filters them to what fits your hardware, and returns a comparison table with scores.

huggingface/skills

Builds and publishes a Gradio demo on Hugging Face Spaces for a LoRA, with the pipeline, UI and settings chosen to match that LoRA's task and model card.

huggingface/skills

Guides causal experiments on PyTorch models with pyvene, such as causal tracing, activation patching and interchange intervention training, to test how a model works.

Orchestra-Research/AI-Research-SKILLs

Loads large language models in 8-bit or 4-bit with bitsandbytes so they fit smaller GPUs, and sets up QLoRA fine-tuning on a 4-bit base model.

Orchestra-Research/AI-Research-SKILLs

Scales PyTorch, TensorFlow and Hugging Face training from a single GPU to multi-node clusters with Ray Train, including Ray Tune sweeps and checkpoint recovery.

Orchestra-Research/AI-Research-SKILLs

Generates text embeddings locally with the sentence-transformers library for RAG, semantic search, clustering and similarity, with model picks for general, multilingual and legal text.

Orchestra-Research/AI-Research-SKILLs

Explains three ways to speed up LLM inference: draft-model speculative decoding, Medusa heads and lookahead decoding with Jacobi iteration, and when each one fits.

Orchestra-Research/AI-Research-SKILLs

Guides reinforcement-learning research with torchforge, Meta's PyTorch-native library that keeps RL algorithms apart from infrastructure, including GRPO math-reasoning runs.

Orchestra-Research/AI-Research-SKILLs

Trains LLMs with reinforcement learning using verl, from ByteDance's Seed team, with GRPO, PPO and other algorithms and swappable training and rollout backends.

Orchestra-Research/AI-Research-SKILLs

Loads pre-trained Hugging Face Transformers models for text, vision and audio tasks, runs inference with pipelines and fine-tunes on custom datasets.

davila7/claude-code-templates

Search all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face).

taishi-i/awesome-japanese-nlp-resources

Explains how to train a model to be harmless with self-critique, revision and AI-generated preference feedback, with Hugging Face and TRL code for each stage.

Orchestra-Research/AI-Research-SKILLs

Guides GRPO reinforcement-learning fine-tuning of language models with TRL, centered on designing reward functions for formats, verifiable tasks and reasoning.

Orchestra-Research/AI-Research-SKILLs

Guide to using Mamba selective state-space models for linear-time sequence modeling, from the Mamba block and pretrained checkpoints to Mamba-2 and speed comparisons.

Orchestra-Research/AI-Research-SKILLs

Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.

NVIDIA/skills

Entry point for hosting a model on Amazon SageMaker: asks a few questions, picks a deployment pathway and hands off to the specialist skills.

huggingface/skills

Reference for post-training language models with TRL: which trainer and dataset format to use for SFT, DPO, GRPO, KTO and reward models, and how to add LoRA.

huggingface/skills