Hugging Face Transformers
huggingface/transformers
The standard Python library for pretrained NLP/vision/audio models on Hugging Face. GitHub: huggingface/transformers (~164k stars).
GitHub repository
Overview
Hugging Face Transformers is the open-source project at huggingface/transformers on GitHub (https://github.com/huggingface/transformers). The standard Python library for pretrained NLP/vision/audio models on Hugging Face. Category on LimeDock: ai-powered. Approximate community size: ~164k GitHub stars. Repo (copy/paste): https://github.com/huggingface/transformers This LimeDock Directories page explains what it is and how teams use it in plain English — so searches for the GitHub project can land on a practical guide.
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Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/huggingface/transformers GitHub path: huggingface/transformers
How to open it: 1. Copy this URL: https://github.com/huggingface/transformers 2. Clone or follow the README install for your OS / stack. 3. Start with the smallest example in their docs before production use.
How to use it
**Simple example** You need to run or fine-tune an open model in Python, not just call a chat API.
1. `pip install transformers` in a venv. 2. Run a pipeline example (sentiment, summarization). 3. Swap in a model ID from the Hub that fits your license needs.
Example prompts
- “Run a Transformers summarization pipeline on this text.”
- “Explain Transformers vs calling OpenAI for our use case.”
- “Pick a small model for CPU inference.”
Use cases and examples
- Evaluate huggingface/transformers for your stack
- Onboard a teammate to Hugging Face Transformers with a shared checklist
- Compare Hugging Face Transformers against tools you already pay for
- Capture lessons in your internal wiki after a pilot
Prerequisites
- Python
- GPU optional
- License review for model weights
Tips
- Start with pipelines before custom trainer loops.
- Watch model card licenses.