diff --git a/ext/candle/Cargo.toml b/ext/candle/Cargo.toml
index 7725950..7cdda12 100644
--- a/ext/candle/Cargo.toml
+++ b/ext/candle/Cargo.toml
@@ -7,7 +7,6 @@ edition = "2021"
 crate-type = ["cdylib"]
 
 [dependencies]
-anyhow = { version = "1", features = ["backtrace"] }
 candle-core = "0.4.1"
 candle-nn = "0.4.1"
 candle-transformers = "0.4.1"
diff --git a/ext/candle/src/model/config.rs b/ext/candle/src/model/config.rs
index 8bcc668..7bb8e4a 100644
--- a/ext/candle/src/model/config.rs
+++ b/ext/candle/src/model/config.rs
@@ -5,12 +5,23 @@ extern crate intel_mkl_src;
 extern crate accelerate_src;
 
 use candle_transformers::models::jina_bert::{BertModel, Config};
-
-use anyhow::Error as E;
-use anyhow::Result;
 use candle_core::{Device, DType, Module, Tensor};
 use candle_nn::VarBuilder;
+use core::result::Result;
 use tokenizers::Tokenizer;
+use magnus::Error;
+
+pub fn wrap_std_err(err: Box<dyn std::error::Error + Send + Sync>) -> Error {
+  Error::new(magnus::exception::runtime_error(), err.to_string())
+}
+
+pub fn wrap_candle_err(err: candle_core::Error) -> Error {
+  Error::new(magnus::exception::runtime_error(), err.to_string())
+}
+
+pub fn wrap_hf_err(err: hf_hub::api::sync::ApiError) -> Error {
+  Error::new(magnus::exception::runtime_error(), err.to_string())
+}
 
 pub struct ModelConfig {
     device: Device,
@@ -29,56 +40,60 @@ impl ModelConfig {
         }
     }
 
-    pub fn build_model_and_tokenizer(&self) -> anyhow::Result<(BertModel, tokenizers::Tokenizer)> {
+    pub fn build_model_and_tokenizer(&self) -> Result<(BertModel, tokenizers::Tokenizer), Error> {
         use hf_hub::{api::sync::Api, Repo, RepoType};
         let model_path = match &self.model_path {
             Some(model_file) => std::path::PathBuf::from(model_file),
-            None => Api::new()?
+            None => Api::new()
+                .map_err(wrap_hf_err)?
                 .repo(Repo::new(
                     "jinaai/jina-embeddings-v2-base-en".to_string(),
                     RepoType::Model,
                 ))
-                .get("model.safetensors")?,
+                .get("model.safetensors")
+                .map_err(wrap_hf_err)?
         };
         let tokenizer_path = match &self.tokenizer_path {
             Some(file) => std::path::PathBuf::from(file),
-            None => Api::new()?
-                .repo(Repo::new(
+            None => Api::new()
+              .map_err(wrap_hf_err)?
+              .repo(Repo::new(
                     "sentence-transformers/all-MiniLM-L6-v2".to_string(),
                     RepoType::Model,
                 ))
-                .get("tokenizer.json")?,
+                .get("tokenizer.json")
+                .map_err(wrap_hf_err)?
         };
         // let device = candle_examples::device(self.cpu)?;
         let config = Config::v2_base();
-        let tokenizer = tokenizers::Tokenizer::from_file(tokenizer_path).map_err(E::msg)?;
-        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_path], DType::F32, &self.device)? };
-        let model = BertModel::new(vb, &config)?;
+        let tokenizer = tokenizers::Tokenizer::from_file(tokenizer_path).map_err(wrap_std_err)?;
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_path], DType::F32, &self.device).map_err(wrap_candle_err)? };
+        let model = BertModel::new(vb, &config).map_err(wrap_candle_err)?;
         Ok((model, tokenizer))
     }
 
-    pub fn embedding(&self, input: String) -> anyhow::Result<Tensor> {
+    pub fn embedding(&self, input: String) -> Result<Tensor, Error> {
         let config = ModelConfig::build();
         let (model, tokenizer) = config.build_model_and_tokenizer()?;
         return self.compute_embedding(input, model, tokenizer);
     }
 
-    fn compute_embedding(&self, prompt: String, model: BertModel, mut tokenizer: Tokenizer) -> Result<Tensor, E> {
+    fn compute_embedding(&self, prompt: String, model: BertModel, mut tokenizer: Tokenizer) -> Result<Tensor, Error> {
         let start: std::time::Instant = std::time::Instant::now();
         // let prompt = args.prompt.as_deref().unwrap_or("Hello, world!");
         let tokenizer = tokenizer
             .with_padding(None)
             .with_truncation(None)
-            .map_err(E::msg)?;
+            .map_err(wrap_std_err)?;
         let tokens = tokenizer
             .encode(prompt, true)
-            .map_err(E::msg)?
+            .map_err(wrap_std_err)?
             .get_ids()
             .to_vec();
-        let token_ids = Tensor::new(&tokens[..], &self.device)?.unsqueeze(0)?;
+        let token_ids = Tensor::new(&tokens[..], &self.device).map_err(wrap_candle_err)?.unsqueeze(0).map_err(wrap_candle_err)?;
         println!("Loaded and encoded {:?}", start.elapsed());
         let start: std::time::Instant = std::time::Instant::now();
-        let result = model.forward(&token_ids)?;
+        let result = model.forward(&token_ids).map_err(wrap_candle_err)?;
         println!("{result}");
         println!("Took {:?}", start.elapsed());
         return Ok(result);