AI TRAINING
Hugging Face 101: Open Models for Engineers
Confidently navigate Hugging Face Hub, deploy open models, and select the right model for real business tasks.
What it covers
This hands-on course introduces engineers to the Hugging Face ecosystem from the ground up. Participants learn to search, evaluate, and run open-source models using the Transformers library, deploy inference endpoints, and publish interactive demos with Spaces. By the end, attendees can make informed model-selection decisions aligned with business constraints such as latency, cost, and data privacy.
What you'll be able to do
- Load and run any model from Hugging Face Hub using Transformers pipelines in under 10 lines of Python
- Fine-tune a pre-trained text or vision model on a custom dataset using the Trainer API or PEFT/LoRA
- Deploy a model as a live REST endpoint using Hugging Face Inference Endpoints and call it from an application
- Build and publish a shareable Gradio demo on Hugging Face Spaces within a single session
- Evaluate and compare open models against business criteria (accuracy, latency, privacy, licensing) to make a justified selection
Topics covered
- Navigating the Hugging Face Hub: search, filters, model cards, and leaderboards
- Using the Transformers library: pipelines, tokenizers, and model loading
- Fine-tuning pre-trained models with Trainer API and PEFT/LoRA
- Deploying models via Hugging Face Inference Endpoints
- Building and publishing interactive demos with Gradio and Spaces
- Reading and writing model cards for documentation and governance
- Comparing open models vs. proprietary APIs on cost, latency, and privacy
- Quantisation basics: running models efficiently with bitsandbytes and GGUF
Delivery
Typically delivered as a 2-3 day in-person or live-virtual bootcamp with a 70/30 hands-on to instruction ratio. Each session includes guided lab notebooks hosted on Google Colab or a pre-configured cloud environment. Participants receive access to a shared Hugging Face organisation for collaboration. A take-home capstone project (deploying a task-specific model end-to-end) is included. Remote delivery works well; in-person adds value for team alignment discussions around model selection.
What makes it work
- Pairing each concept with a real internal use case so participants immediately see business relevance
- Establishing a shared team Space and model registry during training to build collaborative habits from day one
- Including a model-selection rubric workshop so engineers can justify open-model choices to non-technical stakeholders
- Following up with a 2-week async check-in where participants share their capstone results and blockers
Common mistakes
- Pulling the largest available model by default without checking inference cost, latency, or licence compatibility
- Skipping model cards and leaderboard context, leading to poor model-task fit in production
- Treating Hugging Face Inference Endpoints as a production-grade scalable solution without understanding cold-start and rate-limit constraints
- Ignoring quantisation options and attempting to run 7B+ parameter models on CPU, causing frustration and abandonment
When NOT to take this
Teams that have already standardised on a single proprietary LLM API (e.g., OpenAI GPT-4o) with no plans to self-host or fine-tune — the open-model tooling overhead adds complexity without payoff for them.
Providers to consider
Sources
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