qiskit-community
qgss-2026
A repository hosting graded exercises (labs) for the Qiskit Global Summer School 2026, with new labs added via releases.
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FR-AI / ANALYSISWhy it is worth attention
It provides a self-contained, code-driven example of combining RoPE and MoE—two advanced transformer techniques—in an accessible notebook format for experimentation and learning.
Who it is for
- Machine learning practitioners exploring MoE architectures
- Researchers experimenting with RoPE variants
- Students learning to implement custom transformer components
- Hobbyists interested in small-scale GPT training
Use cases
- Educational reference for implementing RoPE in transformers
- Starting point for prototyping sparse MoE for language models
- Quick experimentation with phase-aware MoE routing mechanisms
- Benchmarking small GPT training pipelines in a notebook environment
Strengths
- Single executed notebook ensures reproducibility of the experiment end-to-end
- Combines two modern techniques (RoPE and sparse MoE) in a compact implementation
- Small model size makes it feasible to run on limited hardware
- Code-style presentation aids understanding of both architectural choices
Considerations
- Limited documentation beyond the notebook itself
- Small model scale may not reflect real-world performance or behavior
- No evaluation metrics or comparison baselines provided
Description
A single executed notebook building and training a small GPT with RoPE and a phase-aware sparse MoE experiment
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