Build with MiniCPM5-1B: A Minimal LLM Inference Engine Written in Rust🦀

Developer @XinyuBaoXYB created tiny-llm, a pure Rust inference engine for MiniCPM5-1B, built to explore the fundamentals of Transformer and LLM inference.

Instead of hiding the complexity behind high-level frameworks, tiny-llm makes the inference process visible and understandable:
⚡ CPU-only inference — no GPU required
🧩 Pure Rust implementation with a single-file core
🔍 No KV cache — keeping the inference pipeline simple and transparent
📊 TUI visualization for attention maps, tensor shapes, logits, and execution timing

The project implements essential Transformer components:
🔹 Token Embedding
🔹 RoPE positional encoding
🔹 Multi-head Attention with GQA
🔹 SwiGLU MLP
🔹 RMSNorm
🔹 Residual connections
🔹 Autoregressive token generation

tiny-llm works like an “LLM anatomy lab”, allowing developers to trace how input text is transformed into tokens, processed through transformer layers, and turned into generated output.

A great example of open-source developers making LLM internals more accessible through hands-on implementation. 🚀

Explore the project:
🔗 https://github.com/AspadaX/tiny-llm

Model:
🤗 Hugging Face: https://huggingface.co/openbmb/MiniCPM5-1B