philschmid/deep-learning-pytorch-huggingface resource

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1392 stars · 262 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebook tutorials and examples for deep learning with PyTorch and Hugging Face libraries like Transformers, Datasets, TRL, and Accelerate. It covers fine-tuning LLMs (FLAN-T5, Llama, Falcon, Gemma), quantization, DPO alignment, embedding models for RAG, and inference techniques.

Use cases

• learn how to fine-tune LLMs with Hugging Face TRL
• instruction-tune Llama 2 with QLoRA
• fine-tune an embedding model for RAG
• quantize open LLMs with GPTQ
• scale distributed training with FSDP and DeepSpeed
• align LLMs with DPO
• run efficient LLM inference and benchmarks

When to choose

• you want hands-on, up-to-date notebooks for LLM fine-tuning with PyTorch and Hugging Face
• you need practical examples of LoRA, QLoRA, DPO, or GPTQ quantization
• you are learning distributed training with DeepSpeed or FSDP

When to avoid

• you need a production-ready training framework rather than tutorials
• you work outside the PyTorch/Hugging Face ecosystem
• you need non-LLM deep learning topics like computer vision

Facets

learning-resource · maturity active

machine-learning deep-learning llm-training llm-inference rag deep-learning large-language-models machine-learning tutorials python cross-platform pytorch hugging-face transformers jupyter-notebooks fine-tuning lora deepspeed dpo quantization example-code gpu

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