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Generate model responses in defined formats for data extraction and classification. Use JSON schemas, Pydantic data models, regular expressions, or fixed choices to produce structured results that other programs can parse and validate.

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• Extracting company or product details from text
• Classifying reviews into predefined labels
• Turning profile text into structured fields

Use SAM to separate objects from images by marking points or drawing boxes, without training a model for each task. Covers automatic region generation, refining selections, preparing annotation data, and exporting models for deployment.

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• Extract an object with a transparent background
• Build an interactive image annotation tool
• Generate region masks for vision model training

Find language models on Hugging Face Hub and run them locally with llama.cpp. The skill helps compare GGUF model files, choose compressed versions that fit available memory, and prepare commands for a local chat server or command-line session.

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• Find a model that fits local RAM or GPU memory
• Compare available GGUF files and sizes
• Prepare a local chat server command

Use the hf command-line tool to find, download, and upload models and datasets on Hugging Face Hub. The guide also covers repository management, hosted computing resources, local cache maintenance, and automation options.

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• Find and download models or datasets
• Upload local files to the Hub
• Create repositories and manage branches or tags

Guide language model training with TRL using example answers, preferred response pairs, or reward scores. The skill covers data preparation, method selection, reward model training, evaluation, and adjustments for memory limits or unstable training.

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• Train a language model to follow instructions using example answers
• Train on preferred and rejected responses with DPO
• Build a reward model and run an RLHF training pipeline

Set up vLLM to serve large language models through an OpenAI-compatible API or process prompts in batches. The skill covers deployment, performance monitoring, and quantization, which reduces model memory requirements.

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• Deploying an OpenAI-compatible language model API
• Generating text from large batches of prompts
• Serving large models with limited GPU memory

Set up large language model pretraining with TorchTitan and PyTorch. Configure training across multiple GPUs, combine four ways of splitting the workload, enable Float8 calculations on H100 hardware, and manage checkpoints for saving and resuming training.

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• Pretraining Llama 3.1 8B on a single node
• Launching training across multiple nodes with SLURM
• Configuring Float8 training for H100 GPUs

Track machine learning experiments in W&B, record training settings and metrics, and compare results in dashboards. Automate searches for training settings and keep version histories for models and datasets.

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• Comparing accuracy across training runs
• Monitoring training progress in dashboards
• Searching for better training settings

Set up and optimize large language model inference, or response generation, on NVIDIA GPUs. Use quantization, batch processing, and multiple GPUs to improve serving performance, and expose models through a chat completion API.

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• Serving a language model through a chat completion API
• Processing many prompts in batches
• Configuring quantization to reduce memory use

Evaluate large language models on shared tests of knowledge, reasoning, and code generation with lm-evaluation-harness. Save scores and individual predictions, compare models in tables, and track performance across saved stages of model training.

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• Evaluating models on MMLU and GSM8K
• Building comparison tables for multiple models
• Tracking scores across training checkpoints

Train language models on pairs of preferred and rejected answers without a separate reference model. The skill guides training setup, model-specific configurations, parameter tuning, and troubleshooting for unstable training or memory limits.

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• Train a base model using preferred and rejected answer pairs
• Further tune an instruction-following model
• Adjust training settings for math and coding tasks

Build similarity search with Qdrant, a database for numerical representations of content. Store and retrieve vectors, filter results by metadata, connect retrieval to AI answers, and configure memory usage and deployment for larger workloads.

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• Retrieving reference documents for AI answers
• Filtering similarity search by category or date
• Building real-time recommendations

Fine-tune large language models with LoRA and QLoRA, methods that train small additions to a base model while limiting GPU memory use. Configure training, save task-specific adapters, switch between them, and merge them for deployment.

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• Fine-tuning a language model with limited GPU memory
• Choosing LoRA settings for a training task
• Switching between task-specific adapters

Create Python workflows for generating images from text, restyling existing pictures, and filling selected regions. Using HuggingFace Diffusers, the skill explains composition controls, style adapters, generation settings, and ways to reduce memory use.

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• Generate images from written descriptions
• Restyle an existing picture as a watercolor
• Fill a masked area of a photo

Prepare text for language models by splitting it into tokens, small units represented by numbers. The skill covers batch processing, training custom vocabularies, tracking positions in the original text, and adjusting input lengths.

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• Preparing large text collections for language models
• Training a custom vocabulary from text files
• Mapping model predictions back to original text

Adapt existing PyTorch training scripts to use multiple GPUs or machines with limited code changes. The skill guides configuration, launch commands, mixed precision, which uses lower-precision numbers, and DeepSpeed or FSDP settings for distributing training work and memory.

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• Move a single-GPU training script to multiple GPUs
• Configure mixed precision training
• Set up DeepSpeed or FSDP to reduce memory use

Analyze and modify refusal behavior in open-weight language models without retraining. Use OBLITERATUS to select methods suited to available hardware, compare approaches, and evaluate changes in refusal rates and response quality.

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• Studying how language models produce refusals
• Testing refusal-related weight changes without retraining
• Selecting models and methods for available hardware

Build document search with Chroma, a database that stores text as numerical representations of meaning. Save and update documents, filter by metadata, and connect retrieval to AI applications using LangChain or LlamaIndex.

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• Build document search based on meaning
• Prototype AI answers grounded in retrieved documents
• Store and reload document data locally

Run machine learning training and predictions on cloud GPUs, then publish models as APIs that other applications can call. The skill guides Python-based setup, automatic scaling, persistent storage, parallel processing, and scheduled jobs.

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• Run machine learning jobs without managing GPU servers
• Publish a model as an automatically scaling API
• Process large batches of data in parallel

Use AudioCraft to generate music, sound effects, and environmental audio from text descriptions. Follow implementation examples for model selection, melody and reference audio guidance, stereo output, batch generation, and saving audio files.

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• Generating music from a text description
• Creating sound effects and environmental audio
• Generating music guided by a melody or reference audio

Run ComfyUI workflows to generate images, video, audio, and 3D content. Prepare models and processing nodes, adjust generation settings, create batches of variations, monitor progress, and download results from a local server or Comfy Cloud.

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• Generate images from text with Stable Diffusion or Flux
• Transform an image or replace a selected area
• Create batches with different generation settings

Generate complete songs locally from lyrics and tags describing mood, instruments, or genre. The skill guides setup, input formatting, memory-saving settings, and MP3 output using the open-source HeartMuLa music models.

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• Turn original lyrics into a complete song
• Try different moods and instruments with tags
• Generate music locally or offline

Train and evaluate RuView models using Wi-Fi measurements for pose estimation, retrieval, and adaptation to different environments. Follow laptop or GCloud GPU workflows, then package and publish trained models to Hugging Face.

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• Train pose estimation without cameras or labels
• Train pose estimation with paired camera and Wi-Fi data
• Adapt models to different rooms and environments

Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.

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• Adding newly available router models to chat-ui
• Writing concise descriptions of model capabilities
• Updating production and development model entries together