diff --git a/Cargo.lock b/Cargo.lock
index 22ad1ee7..feac1f6a 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -4481,9 +4481,9 @@ checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20"
 
 [[package]]
 name = "tokenizers"
-version = "0.19.1"
+version = "0.21.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e500fad1dd3af3d626327e6a3fe5050e664a6eaa4708b8ca92f1794aaf73e6fd"
+checksum = "9ecededfed68a69bc657e486510089e255e53c3d38cc7d4d59c8742668ca2cae"
 dependencies = [
  "aho-corasick",
  "derive_builder",
diff --git a/Cargo.toml b/Cargo.toml
index 8c4a4726..84e581c9 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -24,7 +24,7 @@ hf-hub = { version = "0.3.2", features = ["tokio", "online"], default-features =
 metrics = "0.23"
 nohash-hasher = "0.2"
 num_cpus = "1.16.0"
-tokenizers = { version = "0.19.1", default-features = false, features = ["onig", "esaxx_fast"] }
+tokenizers = { version = "0.21.0", default-features = false, features = ["onig", "esaxx_fast"] }
 tokio = { version = "1.25", features = ["rt", "rt-multi-thread", "parking_lot", "sync", "signal"] }
 tracing = "0.1"
 serde = { version = "1.0", features = ["serde_derive"] }
diff --git a/backends/candle/src/lib.rs b/backends/candle/src/lib.rs
index d3c8f831..44566fdb 100644
--- a/backends/candle/src/lib.rs
+++ b/backends/candle/src/lib.rs
@@ -383,25 +383,6 @@ impl CandleBackend {
                     FlashQwen2Model::load(vb, &config, model_type).s()?,
                 ))
             }
-            #[cfg(feature = "cuda")]
-            (Config::ModernBert(config), Device::Cuda(_)) => {
-                if cfg!(feature = "flash-attn")
-                    && dtype == DType::F16
-                    && &std::env::var("USE_FLASH_ATTENTION")
-                        .unwrap_or("True".to_string())
-                        .to_lowercase()
-                        == "true"
-                {
-                    return Err(BackendError::Start(
-                        "ModernBert does not support flash attention".to_string(),
-                    ));
-                }
-
-                tracing::info!("Starting ModernBert model on {:?}", device);
-                Ok(Box::new(
-                    ModernBERTModel::load(vb, &config, model_type).s()?,
-                ))
-            }
         };
 
         Ok(Self {
diff --git a/backends/candle/src/models/modernbert.rs b/backends/candle/src/models/modernbert.rs
index 9e1a1f74..afdef81f 100644
--- a/backends/candle/src/models/modernbert.rs
+++ b/backends/candle/src/models/modernbert.rs
@@ -255,7 +255,6 @@ impl ModernBertAttention {
                 let attn_weights = query_layer.matmul(&key_layer.t()?)?;
                 let attn_weights = (attn_weights * self.softmax_scale)?;
                 let attn_weights = attn_weights.add(attention_mask)?;
-
                 let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
                 attn_weights.matmul(&value_layer.contiguous()?)
             }?;
@@ -277,7 +276,7 @@ struct ModernBertEncoderLayer {
 
 impl ModernBertEncoderLayer {
     pub fn load(vb: VarBuilder, index: usize, config: &ModernBertConfig) -> Result<Self> {
-        let attn_norm = if index > 0 {
+        let attn_norm = if index != 0 {
             Some(LayerNorm::load(
                 vb.pp("attn_norm"),
                 config.hidden_size,
@@ -316,20 +315,23 @@ impl ModernBertEncoderLayer {
     ) -> Result<Tensor> {
         let _enter = self.span.enter();
 
-        let mut hidden_states = hidden_states.clone();
+        let residual = hidden_states.clone();
 
-        if let Some(attn_norm) = &self.attn_norm {
-            hidden_states = attn_norm.forward(&hidden_states, None)?;
-        }
+        let attn_norm = if let Some(attn_norm) = &self.attn_norm {
+            attn_norm.forward(hidden_states, None)?
+        } else {
+            hidden_states.clone()
+        };
+
+        let attn_outputs = self.attn.forward(&attn_norm, attention_mask, cos, sin)?;
+
+        let hidden_states = residual.add(&attn_outputs)?;
 
-        let hidden_states = self
-            .attn
-            .forward(&hidden_states, attention_mask, cos, sin)?;
         let mlp_output = self
             .mlp
             .forward(&self.mlp_norm.forward(&hidden_states, None)?)?;
 
-        hidden_states.broadcast_add(&mlp_output)
+        hidden_states.add(&mlp_output)
     }
 }
 
@@ -714,22 +716,17 @@ impl ModernBertModel {
             };
 
             let pooled_embeddings = match self.pool {
-                // CLS pooling
                 Pool::Cls => outputs.i((.., 0))?,
-                // Last token pooling is not supported for this model
                 Pool::LastToken | Pool::Splade => unreachable!(),
-                // Mean pooling
                 Pool::Mean => {
                     if let Some(ref attention_mask) = attention_mask {
                         let mut attention_mask = attention_mask.clone();
 
                         if let Some(pooled_indices) = pooled_indices {
-                            // Select values in the batch
                             attention_mask = attention_mask.index_select(&pooled_indices, 0)?;
                             input_lengths = input_lengths.index_select(&pooled_indices, 0)?;
                         };
 
-                        // Mask padded values
                         outputs = outputs.broadcast_mul(&attention_mask)?;
                     }
 
@@ -742,7 +739,6 @@ impl ModernBertModel {
         };
 
         let raw_embeddings = if has_raw_requests {
-            // Reshape outputs
             let (b, l, h) = outputs.shape().dims3()?;
             let outputs = outputs.reshape((b * l, h))?;
 
diff --git a/backends/candle/tests/common.rs b/backends/candle/tests/common.rs
index 0c069a47..71cfdc8b 100644
--- a/backends/candle/tests/common.rs
+++ b/backends/candle/tests/common.rs
@@ -197,7 +197,7 @@ pub fn load_tokenizer(model_root: &Path) -> Result<Tokenizer> {
             // We are forced to clone since `Tokenizer` does not have a `get_mut` for `pre_tokenizer`
             let mut m = m.clone();
             m.set_prepend_scheme(PrependScheme::First);
-            tokenizer.with_pre_tokenizer(PreTokenizerWrapper::Metaspace(m));
+            tokenizer.with_pre_tokenizer(Some(PreTokenizerWrapper::Metaspace(m)));
         } else if let PreTokenizerWrapper::Sequence(s) = pre_tokenizer {
             let pre_tokenizers = s.get_pre_tokenizers();
             // Check if we have a Metaspace pre tokenizer in the sequence
@@ -222,9 +222,9 @@ pub fn load_tokenizer(model_root: &Path) -> Result<Tokenizer> {
                     }
                     new_pre_tokenizers.push(pre_tokenizer);
                 }
-                tokenizer.with_pre_tokenizer(PreTokenizerWrapper::Sequence(Sequence::new(
+                tokenizer.with_pre_tokenizer(Some(PreTokenizerWrapper::Sequence(Sequence::new(
                     new_pre_tokenizers,
-                )));
+                ))));
             }
         }
     }
diff --git a/backends/candle/tests/snapshots/test_modernbert__mini_batch.snap b/backends/candle/tests/snapshots/test_modernbert__mini_batch.snap
new file mode 100644
index 00000000..2c902545
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_modernbert__mini_batch.snap
@@ -0,0 +1,2308 @@
+---
+source: backends/candle/tests/test_modernbert.rs
+expression: embeddings_batch
+---
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diff --git a/backends/candle/tests/snapshots/test_modernbert__mini_single.snap b/backends/candle/tests/snapshots/test_modernbert__mini_single.snap
new file mode 100644
index 00000000..6cce986f
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_modernbert__mini_single.snap
@@ -0,0 +1,772 @@
+---
+source: backends/candle/tests/test_modernbert.rs
+expression: embeddings_single
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+  - 1.0374448
+  - 0.28939614
+  - -0.37948802
+  - 0.14916803
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+  - 0.2032562
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+  - 0.19193758
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+  - 0.17719606
+  - 0.25270763
+  - -0.043502476
+  - 0.3512094
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+  - 0.16744027
+  - 0.7090984
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+  - 0.13033624
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+  - 0.52960044
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+  - 0.75653404
+  - 0.7847978
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+  - 0.03424729
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+  - 0.28994456
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+  - 0.28897327
+  - 0.23776725
+  - 0.14170568
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+  - 0.43958607
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diff --git a/backends/candle/tests/test_modernbert.rs b/backends/candle/tests/test_modernbert.rs
new file mode 100644
index 00000000..e87796a9
--- /dev/null
+++ b/backends/candle/tests/test_modernbert.rs
@@ -0,0 +1,137 @@
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+
+#[test]
+#[serial_test::serial]
+fn test_mini() -> Result<()> {
+    let model_root = download_artifacts("answerdotai/ModernBERT-base", None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        &model_root,
+        "float32".to_string(),
+        ModelType::Embedding(Pool::Mean),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("mini_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("mini_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+        ],
+        [0].to_vec(),
+        [1].to_vec(),
+    );
+
+    let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
+    let pooled_embeddings = SnapshotEmbeddings::from(pooled_embeddings);
+    let raw_embeddings = SnapshotEmbeddings::from(raw_embeddings);
+
+    assert_eq!(embeddings_batch[0], pooled_embeddings[0]);
+    assert_eq!(raw_embeddings.len(), 6);
+
+    Ok(())
+}
+
+#[test]
+#[serial_test::serial]
+fn test_mini_pooled_raw() -> Result<()> {
+    let model_root = download_artifacts("sentence-transformers/all-mpnet-base-v2", None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        &model_root,
+        "float32".to_string(),
+        ModelType::Embedding(Pool::Cls),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 2, 3].to_vec(),
+        [1, 4, 5].to_vec(),
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, raw_embeddings) = sort_embeddings(backend.embed(input_batch)?);
+    let pooled_embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("mini_batch_pooled", pooled_embeddings_batch, &matcher);
+
+    let raw_embeddings_batch = SnapshotEmbeddings::from(raw_embeddings);
+    insta::assert_yaml_snapshot!("mini_batch_raw", raw_embeddings_batch, &matcher);
+
+    // Check that the first token of each raw embeddings member is the same as the cls pooling ones
+    assert_eq!(pooled_embeddings_batch[0], raw_embeddings_batch[0]);
+    assert_eq!(pooled_embeddings_batch[1], raw_embeddings_batch[7]);
+    assert_eq!(pooled_embeddings_batch[2], raw_embeddings_batch[15]);
+    assert_eq!(raw_embeddings_batch.len(), 22);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("mini_single_pooled", embeddings_single, &matcher);
+
+    assert_eq!(pooled_embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(pooled_embeddings_batch[2], embeddings_single[0]);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        vec![],
+        [0].to_vec(),
+    );
+
+    let (_, raw_embeddings) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(raw_embeddings);
+    insta::assert_yaml_snapshot!("mini_single_raw", embeddings_single, &matcher);
+
+    assert_eq!(raw_embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(raw_embeddings_batch[15], embeddings_single[0]);
+    assert_eq!(embeddings_single.len(), 7);
+
+    Ok(())
+}
diff --git a/router/src/lib.rs b/router/src/lib.rs
index e01ede28..488e78b1 100644
--- a/router/src/lib.rs
+++ b/router/src/lib.rs
@@ -147,13 +147,13 @@ pub async fn run(
             .build()
             .unwrap();
         match tokenizer.get_post_processor() {
-            None => tokenizer.with_post_processor(template),
+            None => tokenizer.with_post_processor(Some(template)),
             Some(post_processor) => {
                 let post_processor = Sequence::new(vec![
                     post_processor.clone(),
                     PostProcessorWrapper::Template(template),
                 ]);
-                tokenizer.with_post_processor(post_processor)
+                tokenizer.with_post_processor(Some(post_processor))
             }
         };
     }