Can this skill help with GPU memory issues?

Yes, it provides implementation patterns for 4-bit and 8-bit quantization, as well as automatic device mapping to optimize model placement across CPU and GPU.

What are Auto classes in this context?

Auto classes (like AutoModel or AutoTokenizer) are HuggingFace features that automatically retrieve the correct architecture and configuration based on the model name or path provided.

Does it support fine-tuning on custom datasets?

Absolutely. The skill includes best practices for the HuggingFace Trainer API and parameter-efficient methods like LoRA to adapt models to your specific data.

Which machine learning tasks does this skill support?

It supports a broad range of tasks including text classification, summarization, question answering, NER, image classification, object detection, and automatic speech recognition.

HuggingFace Transformers

Name: HuggingFace Transformers
Author: eyadsibai

byeyadsibai

Data Science & ML

Implements pre-trained machine learning models for NLP, vision, and audio tasks using the HuggingFace ecosystem.

About

The Transformers skill empowers developers to integrate state-of-the-art machine learning capabilities into their applications using the HuggingFace library. It provides expert guidance on selecting the right pre-trained models, configuring inference pipelines for tasks like text generation and object detection, and implementing advanced memory management techniques such as 4-bit quantization. Whether you are fine-tuning a model with QLoRA or deploying a quick sentiment analysis tool, this skill ensures best practices for performance, accuracy, and resource efficiency.

Key Features

Optimized model loading using AutoModel and AutoTokenizer classes
Advanced memory management via 4-bit/8-bit quantization and device mapping
Implementation patterns for PEFT strategies like LoRA and QLoRA
Precise control over text generation parameters for varied creative outputs
0 GitHub stars
Comprehensive pipeline configuration for NLP, Vision, and Audio tasks

Use Cases

Deploying large language models on consumer hardware using quantization
Building domain-specific NLP tools for classification, NER, or summarization
Fine-tuning pre-trained models on custom datasets using the Trainer API

About

Key Features

Optimized model loading using AutoModel and AutoTokenizer classes
Advanced memory management via 4-bit/8-bit quantization and device mapping
Implementation patterns for PEFT strategies like LoRA and QLoRA
Precise control over text generation parameters for varied creative outputs
0 GitHub stars
Comprehensive pipeline configuration for NLP, Vision, and Audio tasks

Use Cases

Deploying large language models on consumer hardware using quantization
Building domain-specific NLP tools for classification, NER, or summarization
Fine-tuning pre-trained models on custom datasets using the Trainer API