transformers
Category: data-science-and-ml · License: Apache-2.0 license · Version: 1.3
Tags: Hugging Face Transformers, LLMs, NLP, fine-tuning, deep learning
When to use it
Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Use when working with AutoModel, pipelines, tokenizers, or TrainingArguments—not for general ML outside the Transformers library.Example requests
Ask your agent in plain language, for example:- “How do I run sentiment analysis on a list of tweets with a pretrained model from the Hub?”
- “I’m fine-tuning bert-base-uncased on my CSV of clinical notes (train.csv, val.csv, columns ‘text’ and ‘label’, 5 classes) with Trainer, and I get ‘RuntimeError: CUDA out of memory’ at the first step with batch size 32 on a 16GB GPU. Here’s my TrainingArguments(output_dir=‘out’, per_device_train_batch_size=32, num_train_epochs=3, fp16=True). What should I change?”
- “I have about 2,000 recorded interviews as WAV files and I need written transcripts for them, ideally using an open model that runs on my own lab GPU. Can you set that up?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole data-science-and-ml category with /plugin install data-science-and-ml@research-agent-skills.