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