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 # Using the hub
+
+Install the [`hf-hub`](https://github.com/huggingface/hf-hub) crate:
+
+```bash
+cargo add hf-hub
+```
+
+Then let's start by downloading the [model file](https://huggingface.co/bert-base-uncased/tree/main).
+
+
+```rust
+# extern crate candle_core;
+# extern crate hf_hub;
+use hf_hub::api::sync::Api;
+use candle_core::Device;
+
+let api = Api::new().unwrap();
+let repo = api.model("bert-base-uncased".to_string());
+
+let weights = repo.get("model.safetensors").unwrap();
+
+let weights = candle_core::safetensors::load(weights, &Device::Cpu);
+```
+
+We now have access to all the [tensors](https://huggingface.co/bert-base-uncased?show_tensors=true) within the file.
+
+You can check all the names of the tensors [here](https://huggingface.co/bert-base-uncased?show_tensors=true)
+
+
+## Using async 
+
+`hf-hub` comes with an async API.
+
+```bash
+cargo add hf-hub --features tokio
+```
+
+```rust,ignore
+# This is tested directly in examples crate because it needs external dependencies unfortunately:
+# See [this](https://github.com/rust-lang/mdBook/issues/706)
+{{#include ../../../candle-examples/src/lib.rs:book_hub_1}}
+```
+
+
+## Using in a real model.
+
+Now that we have our weights, we can use them in our bert architecture:
+
+```rust
+# extern crate candle_core;
+# extern crate candle_nn;
+# extern crate hf_hub;
+# use hf_hub::api::sync::Api;
+# 
+# let api = Api::new().unwrap();
+# let repo = api.model("bert-base-uncased".to_string());
+# 
+# let weights = repo.get("model.safetensors").unwrap();
+use candle_core::{Device, Tensor, DType};
+use candle_nn::Linear;
+
+let weights = candle_core::safetensors::load(weights, &Device::Cpu).unwrap();
+
+let weight = weights.get("bert.encoder.layer.0.attention.self.query.weight").unwrap();
+let bias = weights.get("bert.encoder.layer.0.attention.self.query.bias").unwrap();
+
+let linear = Linear::new(weight.clone(), Some(bias.clone()));
+
+let input_ids = Tensor::zeros((3, 768), DType::F32, &Device::Cpu).unwrap();
+let output = linear.forward(&input_ids).unwrap();
+```
+
+For a full reference, you can check out the full [bert](https://github.com/LaurentMazare/candle/tree/main/candle-examples/examples/bert) example.
+
+## Memory mapping
+
+For more efficient loading, instead of reading the file, you could use [`memmap2`](https://docs.rs/memmap2/latest/memmap2/)
+
+**Note**: Be careful about memory mapping it seems to cause issues on [Windows, WSL](https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/5893)
+and will definitely be slower on network mounted disk, because it will issue more read calls.
+
+```rust,ignore
+{{#include ../../../candle-examples/src/lib.rs:book_hub_2}}
+```
+
+**Note**: This operation is **unsafe**. [See the safety notice](https://docs.rs/memmap2/latest/memmap2/struct.Mmap.html#safety).
+In practice model files should never be modified, and the mmaps should be mostly READONLY anyway, so the caveat most likely does not apply, but always keep it in mind.
+
+
+## Tensor Parallel Sharding
+
+When using multiple GPUs to use in Tensor Parallel in order to get good latency, you can load only the part of the Tensor you need.
+
+For that you need to use [`safetensors`](https://crates.io/crates/safetensors) directly.
+
+```bash
+cargo add safetensors
+```
+
+
+```rust,ignore
+{{#include ../../../candle-examples/src/lib.rs:book_hub_3}}
+```