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+
+# Candle
+
+[Candle](https://github.com/huggingface/candle) is a machine learning framework providing native Rust implementations of Transformers models. It natively supports [safetensors](https://huggingface.co/docs/safetensors/en/index) to load Transformers models directly.
+
+```rust
+/// load model config
+let config: Config = 
+    serde_json::from_reader(std::fs::File::open(config_filename)?)?;
+
+/// load safetensors and memory-maps them
+let vb = unsafe {
+    VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)?
+};
+
+/// materialize tensors from VarBuilder into model class
+let model = Model::new(args.use_flash_attn, &config, vb)?;
+```
+
+## Transformers integration
+
+1. The [hf-hub](https://github.com/huggingface/hf-hub) crate checks your local [Hugging Face cache](../installation#cache-directory) for a model. If it isn't there, it downloads model weights and configs from the Hub.
+2. [VarBuilder](https://github.com/huggingface/candle/blob/f526033db7ea880c7189628a2dc00e3e2008a9e7/candle-nn/src/var_builder.rs#L38) lazily loads the safetensor files. It maps state-dict key names to Rust structs representing model layers. This mirrors how Transformers organizes its weights.
+3. Candle parses `config.json` to extract model metadata and instantiates the matching Rust model class with weights from `VarBuilder`.
+
+## Resources
+
+- [Candle](https://github.com/huggingface/candle) documentation