Huggingface Model Deployer

---
name: "Hugging Face Model Deployer"
slug: "huggingface-model-deployer"
description: "Deploys models from Hugging Face Hub to Inference Endpoints using the huggingface_hub client and REST API. Monitors endpoint health and autoscaling status and streams logs to the terminal. Supports private repos with HF_TOKEN and custom Docker containers."
github_stars: 159358
verification: "security_reviewed"
source: "https://github.com/huggingface/transformers"
category: "CI/CD Integrations"
framework: "Codex"
tool_ecosystem:
  github_repo: "huggingface/transformers"
  github_stars: 159358
---

# Hugging Face Model Deployer

Deploys models from Hugging Face Hub to Inference Endpoints using the huggingface_hub client and REST API. Monitors endpoint health and autoscaling status and streams logs to the terminal. Supports private repos with HF_TOKEN and custom Docker containers.

## Installation

Use the upstream install or setup path that matches your environment:
- uv venv .my-env
- pip install "transformers[torch]"
- uv pip install "transformers[torch]"
- git clone https://github.com/huggingface/transformers.git

Requirements and caveats from upstream:
- Transformers works with Python 3.10+, and [PyTorch](https://pytorch.org/get-started/locally/) 2.4+.
- Create and activate a virtual environment with [venv](https://docs.python.org/3/library/venv.html) or [uv](https://docs.astral.sh/uv/), a fast Rust-based Python package and project manager.
- python -m venv .my-env

Basic usage or getting-started notes:
- We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be
- py
- # venv

- Source: https://github.com/huggingface/transformers
- Extracted from upstream docs: https://raw.githubusercontent.com/huggingface/transformers/HEAD/README.md

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/huggingface-model-deployer/)