Observed Signal · May 2, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Inference: Generating New Text with MicroGPT (C#)
Chapter 12 of a MicroGPT course demonstrates inference and text-generation using a trained transformer implemented in C#. It provides a sampling loop that uses a KV cache, temperature-scaled logits, softmax sampling, and repeats token-by-token until a BOS token or max length. The chapter shows example outputs (plausible new names) after 10,000 training steps, explains temperature effects on randomness, and gives practical run instructions (dotnet run -c Release). It also offers experiments (add transformer blocks, increase sequence length, change normalisation or nonlinearity), performance optimisation notes (avoid LINQ in hot paths, SIMD vectorisation, zero-allocation hot paths), a glossary of core terms, references (e.g., Attention Is All You Need, Adam, RMSNorm), and credits (Andrej Karpathy, Martin Skuta, Jonas Ara, Gary Jackson, Claude/Anthropic).
Provides a practical, implementation-level tutorial on LLM inference and performance optimisations; useful foundational material for engineers but not industry-shifting.
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Key Takeaways & Evidence Grounding
- The chapter demonstrates a sampling loop that generates new names from a trained MicroGPT model implemented in C#.
- The provided inference code uses a KV cache via model.CreateKvCache(), temperature scaling (example Temperature = 0.5), softmax sampling, and repeats until a BOS token or maximum sequence length.
- After 10,000 training steps the example model typically generates plausible-sounding names; a full training run on a modern CPU in Release mode typically takes 5–15 minutes.
- The chapter includes experiments to try (add transformer blocks, increase sequence length, vary temperature, remove RMSNorm, swap activation) and performance optimisation suggestions (replace LINQ in hot paths, SIMD vectorisation, zero-allocation hot paths).
- References and credits cite foundational work (Vaswani et al. 2017 Transformer paper), Karpathy's microgpt, and contributors including Andrej Karpathy, Martin Skuta, Jonas Ara, and Gary Jackson; Claude (Anthropic) assisted in course drafting.
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