s/candle/candle_core/g

candle-book/src/inference/hub.md

@@ -10,17 +10,17 @@ Then let's start by downloading the [model file](https://huggingface.co/bert-bas
 
 
 ```rust
-# extern crate candle;
+# extern crate candle_core;
 # extern crate hf_hub;
 use hf_hub::api::sync::Api;
-use candle::Device;
+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::safetensors::load(weights, &Device::Cpu);
+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.
@@ -48,7 +48,7 @@ cargo add hf-hub --features tokio
 Now that we have our weights, we can use them in our bert architecture:
 
 ```rust
-# extern crate candle;
+# extern crate candle_core;
 # extern crate candle_nn;
 # extern crate hf_hub;
 # use hf_hub::api::sync::Api;
@@ -57,10 +57,10 @@ Now that we have our weights, we can use them in our bert architecture:
 # let repo = api.model("bert-base-uncased".to_string());
 # 
 # let weights = repo.get("model.safetensors").unwrap();
-use candle::{Device, Tensor, DType};
+use candle_core::{Device, Tensor, DType};
 use candle_nn::Linear;
 
-let weights = candle::safetensors::load(weights, &Device::Cpu).unwrap();
+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();