Add shimmy - Rust inference server for NLP

[Michael-A-Kuykendall]

Adds shimmy to the Rust > Natural Language Processing section.

Project Details:

• Repository: https://github.com/Michael-A-Kuykendall/shimmy
• Stars: 2,918 ⭐
• Language: Rust
• Category: NLP Inference Server

Why Shimmy enhances awesome-machine-learning:

Natural Language Processing Focus:

• LLM Inference Serving: Production-ready server for large language models
• OpenAI API compatibility: Seamless integration with existing NLP workflows
• Modern Model Formats: GGUF and SafeTensors support for latest NLP models
• Hot Model Swapping: Dynamic model updates for NLP experimentation
• Text Generation: Optimized for conversational AI and text completion tasks

Rust ML Ecosystem Benefits:

• Pure Rust Implementation: No Python dependencies for ML inference serving
• Performance: Rust's speed and memory safety for production NLP workloads
• Resource Efficiency: Lightweight deployment compared to Python-based solutions
• Cross-Platform: Consistent NLP inference across Linux, macOS, Windows
• Cloud Native: Single binary deployment perfect for containerized ML workflows

Technical ML Features:

• Transformer model support (GPT, LLaMA, ChatML architectures)
• Efficient token processing and generation
• Batched inference for throughput optimization
• GPU acceleration support for large models
• RESTful API for easy ML pipeline integration

Production ML Use Cases:

• NLP microservices and API development
• Chatbot and conversational AI backends
• Text generation and completion services
• Multi-tenant NLP model serving
• Edge AI deployment for NLP applications

Positioning in Rust ML: Shimmy fills a critical gap in the Rust ML ecosystem by providing production-ready NLP inference serving. While projects like rust-bert focus on model implementations and tokenizers handle text processing, Shimmy bridges the deployment gap with a complete inference server solution.

This addition strengthens the Rust NLP toolkit by providing developers with a robust, dependency-light option for deploying NLP models in production environments.