Expanding a bit the README

candle-core/src/lib.rs

@@ -1,3 +1,38 @@
+//! ML framework for Rust
+//!
+//! ```rust
+//! use candle::{Tensor, DType, Device};
+//! # use candle::Error;
+//! # fn main() -> Result<(), Error>{
+//!
+//! let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
+//! let b = Tensor::zeros((3, 4), DType::F32, &Device::Cpu)?;
+//!
+//! let c = a.matmul(&b)?;
+//! # Ok(())}
+//! ```
+//!
+//! ## Features
+//!
+//! - Simple syntax (looks and like PyTorch)
+//! - CPU and Cuda backends (and M1 support)
+//! - Enable serverless (CPU) small and fast deployments
+//! - Model training
+//! - Distributed computing (NCCL).
+//! - Models out of the box (Llama, Whisper, Falcon, ...)
+//!
+//! ## FAQ
+//!
+//! - Why Candle?
+//!
+//! Candle stems from the need to reduce binary size in order to *enable serverless*
+//! possible by making the whole engine smaller than PyTorch very large library volume
+//!
+//! And simply *removing Python* from production workloads.
+//! Python can really add overhead in more complex workflows and the [GIL](https://www.backblaze.com/blog/the-python-gil-past-present-and-future/) is a notorious source of headaches.
+//!
+//! Rust is cool, and a lot of the HF ecosystem already has Rust crates [safetensors](https://github.com/huggingface/safetensors) and [tokenizers](https://github.com/huggingface/tokenizers)
+
 mod backprop;
 mod conv;
 mod cpu_backend;