diff --git a/Makefile b/Makefile
index 1af63144..99a5ff59 100644
--- a/Makefile
+++ b/Makefile
@@ -1,11 +1,11 @@
 integration-tests:
-	cargo test --release
+	cargo test
 
 cuda-integration-tests:
-	cargo test -F text-embeddings-backend-candle/cuda -F text-embeddings-backend-candle/flash-attn -F text-embeddings-router/candle-cuda --release
+	cargo test -F text-embeddings-backend-candle/cuda -F text-embeddings-backend-candle/flash-attn -F text-embeddings-router/candle-cuda --profile release-debug
 
 integration-tests-review:
-	cargo insta test --review --release
+	cargo insta test --review
 
 cuda-integration-tests-review:
-	cargo insta test --review --features "text-embeddings-backend-candle/cuda text-embeddings-backend-candle/flash-attn text-embeddings-router/candle-cuda" --release
+	cargo insta test --review --features "text-embeddings-backend-candle/cuda text-embeddings-backend-candle/flash-attn text-embeddings-router/candle-cuda" --profile release-debug
diff --git a/backends/candle/src/layers.rs b/backends/candle/src/layers/mod.rs
similarity index 100%
rename from backends/candle/src/layers.rs
rename to backends/candle/src/layers/mod.rs
diff --git a/backends/candle/src/lib.rs b/backends/candle/src/lib.rs
index 4af5b704..a7a5dcf0 100644
--- a/backends/candle/src/lib.rs
+++ b/backends/candle/src/lib.rs
@@ -12,7 +12,7 @@ use crate::compute_cap::{
 };
 use crate::models::{
     BertConfig, BertModel, DistilBertConfig, DistilBertModel, JinaBertModel, JinaCodeBertModel,
-    JinaCodeConfig, JinaConfig, Model, NomicBertModel, NomicConfig,
+    Model, NomicBertModel, NomicConfig,
 };
 #[cfg(feature = "cuda")]
 use crate::models::{
@@ -30,17 +30,28 @@ use text_embeddings_backend_core::{
     Backend, BackendError, Batch, Embedding, Embeddings, ModelType, Predictions,
 };
 
+/// This enum is needed to be able to differentiate between jina models that also use
+/// the `bert` model type and valid Bert models.
+/// We use the `_name_or_path` field in the config to do so. This might not be robust in the long
+/// run but is still better than the other options...
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+#[serde(tag = "_name_or_path")]
+pub enum BertConfigWrapper {
+    #[serde(rename = "jinaai/jina-bert-implementation")]
+    JinaBert(BertConfig),
+    #[serde(rename = "jinaai/jina-bert-v2-qk-post-norm")]
+    JinaCodeBert(BertConfig),
+    #[serde(untagged)]
+    Bert(BertConfig),
+}
+
 #[derive(Deserialize)]
 #[serde(tag = "model_type", rename_all = "kebab-case")]
 enum Config {
-    Bert(BertConfig),
+    Bert(BertConfigWrapper),
     XlmRoberta(BertConfig),
     Camembert(BertConfig),
     Roberta(BertConfig),
-    #[serde(rename(deserialize = "jina_bert"))]
-    JinaBert(JinaConfig),
-    #[serde(rename(deserialize = "jina_code_bert"))]
-    JinaCodeBert(JinaCodeConfig),
     #[serde(rename(deserialize = "distilbert"))]
     DistilBert(DistilBertConfig),
     #[serde(rename(deserialize = "nomic_bert"))]
@@ -76,7 +87,7 @@ impl CandleBackend {
                         "Runtime compute cap {} is not compatible with compile time compute cap {}",
                         get_runtime_compute_cap().unwrap(),
                         get_compile_compute_cap().unwrap()
-                    )))
+                    )));
                 }
                 Err(err) => {
                     tracing::warn!("Could not find a compatible CUDA device on host: {err:?}");
@@ -123,20 +134,22 @@ impl CandleBackend {
             (_, Device::Cuda(_)) => Err(BackendError::Start(
                 "`cuda` feature is not enabled".to_string(),
             )),
-            (Config::Bert(config), Device::Cpu | Device::Metal(_)) => {
-                tracing::info!("Starting Bert model on {:?}", device);
-                Ok(Box::new(BertModel::load(vb, &config, model_type).s()?))
-            }
-            (Config::JinaBert(config), Device::Cpu | Device::Metal(_)) => {
-                tracing::info!("Starting JinaBertModel model on {:?}", device);
-                Ok(Box::new(JinaBertModel::load(vb, &config, model_type).s()?))
-            }
-            (Config::JinaCodeBert(config), Device::Cpu | Device::Metal(_)) => {
-                tracing::info!("Starting JinaCodeBertModel model on {:?}", device);
-                Ok(Box::new(
-                    JinaCodeBertModel::load(vb, &config, model_type).s()?,
-                ))
-            }
+            (Config::Bert(config), Device::Cpu | Device::Metal(_)) => match config {
+                BertConfigWrapper::JinaBert(config) => {
+                    tracing::info!("Starting JinaBertModel model on {:?}", device);
+                    Ok(Box::new(JinaBertModel::load(vb, &config, model_type).s()?))
+                }
+                BertConfigWrapper::JinaCodeBert(config) => {
+                    tracing::info!("Starting JinaCodeBert model on {:?}", device);
+                    Ok(Box::new(
+                        JinaCodeBertModel::load(vb, &config, model_type).s()?,
+                    ))
+                }
+                BertConfigWrapper::Bert(config) => {
+                    tracing::info!("Starting Bert model on {:?}", device);
+                    Ok(Box::new(BertModel::load(vb, &config, model_type).s()?))
+                }
+            },
             (
                 Config::XlmRoberta(config) | Config::Camembert(config) | Config::Roberta(config),
                 Device::Cpu | Device::Metal(_),
@@ -160,56 +173,45 @@ impl CandleBackend {
             (Config::Bert(config), Device::Cuda(_)) => {
                 if cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
                     && dtype == DType::F16
-                    && ((config.position_embedding_type == PositionEmbeddingType::Absolute) | (config.position_embedding_type == PositionEmbeddingType::Alibi))
                     // Allow disabling because of flash attention v1 precision problems
                     // See: https://github.com/huggingface/text-embeddings-inference/issues/37
                     && &std::env::var("USE_FLASH_ATTENTION").unwrap_or("True".to_string()).to_lowercase() == "true"
                 {
-                    if config.position_embedding_type == PositionEmbeddingType::Alibi {
-                        tracing::info!("Starting FlashBert model on {:?}", device);
-                        Ok(Box::new(FlashBertModel::load(vb, &config, model_type).s()?))
-                    } else {
-                        tracing::info!("Starting Bert model on {:?}", device);
-                        Ok(Box::new(BertModel::load(vb, &config, model_type).s()?))
+                    match config {
+                        BertConfigWrapper::JinaBert(config) => {
+                            tracing::info!("Starting FlashJinaBert model on {:?}", device);
+                            Ok(Box::new(
+                                FlashJinaBertModel::load(vb, &config, model_type).s()?,
+                            ))
+                        }
+                        BertConfigWrapper::JinaCodeBert(config) => {
+                            tracing::info!("Starting FlashJinaCodeBert model on {:?}", device);
+                            Ok(Box::new(
+                                FlashJinaCodeBertModel::load(vb, &config, model_type).s()?,
+                            ))
+                        }
+                        BertConfigWrapper::Bert(config) => {
+                            tracing::info!("Starting FlashBert model on {:?}", device);
+                            Ok(Box::new(FlashBertModel::load(vb, &config, model_type).s()?))
+                        }
                     }
-                }
-            }
-            #[cfg(feature = "cuda")]
-            (Config::JinaBert(config), Device::Cuda(_)) => {
-                if cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
-                    && dtype == DType::F16
-                    && ((config.position_embedding_type == PositionEmbeddingType::Absolute) | (config.position_embedding_type == PositionEmbeddingType::Alibi))
-                    // Allow disabling because of flash attention v1 precision problems
-                    // See: https://github.com/huggingface/text-embeddings-inference/issues/37
-                    && &std::env::var("USE_FLASH_ATTENTION").unwrap_or("True".to_string()).to_lowercase() == "true"
-                {
-                    tracing::info!("Starting FlashJinaBertModel model on {:?}", device);
-                    Ok(Box::new(
-                        FlashJinaBertModel::load(vb, &config, model_type).s()?,
-                    ))
-                } else {
-                    tracing::info!("Starting JinaBertModel model on {:?}", device);
-                    Ok(Box::new(JinaBertModel::load(vb, &config, model_type).s()?))
-                }
-            }
-            #[cfg(feature = "cuda")]
-            (Config::JinaCodeBert(config), Device::Cuda(_)) => {
-                if cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
-                    && dtype == DType::F16
-                    && ((config.position_embedding_type == PositionEmbeddingType::Absolute) | (config.position_embedding_type == PositionEmbeddingType::Alibi))
-                    // Allow disabling because of flash attention v1 precision problems
-                    // See: https://github.com/huggingface/text-embeddings-inference/issues/37
-                    && &std::env::var("USE_FLASH_ATTENTION").unwrap_or("True".to_string()).to_lowercase() == "true"
-                {
-                    tracing::info!("Starting FlashJinaCodeBertModel model on {:?}", device);
-                    Ok(Box::new(
-                        FlashJinaCodeBertModel::load(vb, &config, model_type).s()?,
-                    ))
                 } else {
-                    tracing::info!("Starting JinaCodeBertModel model on {:?}", device);
-                    Ok(Box::new(
-                        JinaCodeBertModel::load(vb, &config, model_type).s()?,
-                    ))
+                    match config {
+                        BertConfigWrapper::JinaBert(config) => {
+                            tracing::info!("Starting JinaBertModel model on {:?}", device);
+                            Ok(Box::new(JinaBertModel::load(vb, &config, model_type).s()?))
+                        }
+                        BertConfigWrapper::JinaCodeBert(config) => {
+                            tracing::info!("Starting JinaCodeBert model on {:?}", device);
+                            Ok(Box::new(
+                                JinaCodeBertModel::load(vb, &config, model_type).s()?,
+                            ))
+                        }
+                        BertConfigWrapper::Bert(config) => {
+                            tracing::info!("Starting Bert model on {:?}", device);
+                            Ok(Box::new(BertModel::load(vb, &config, model_type).s()?))
+                        }
+                    }
                 }
             }
             #[cfg(feature = "cuda")]
@@ -219,7 +221,6 @@ impl CandleBackend {
             ) => {
                 if cfg!(any(feature = "flash-attn", feature = "flash-attn-v1"))
                     && dtype == DType::F16
-                    && ((config.position_embedding_type == PositionEmbeddingType::Absolute) | (config.position_embedding_type == PositionEmbeddingType::Alibi))
                     // Allow disabling because of flash attention v1 precision problems
                     // See: https://github.com/huggingface/text-embeddings-inference/issues/37
                     && &std::env::var("USE_FLASH_ATTENTION").unwrap_or("True".to_string()).to_lowercase() == "true"
diff --git a/backends/candle/src/models/flash_jina.rs b/backends/candle/src/models/flash_jina.rs
index 0e1d3006..b2d47e51 100644
--- a/backends/candle/src/models/flash_jina.rs
+++ b/backends/candle/src/models/flash_jina.rs
@@ -2,14 +2,13 @@ use crate::alibi::alibi_head_slopes;
 use crate::flash_attn::flash_attn_varlen;
 use crate::layers::{HiddenAct, LayerNorm, Linear};
 use crate::models::bert::PositionEmbeddingType;
-use crate::models::jina::BertEmbeddings;
-use crate::models::jina::{BertEmbeddings, JinaConfig};
-use crate::models::Model;
+use crate::models::jina::JinaEmbeddings;
+use crate::models::{BertConfig, Model};
 use candle::{DType, Device, IndexOp, Result, Tensor};
 use candle_nn::VarBuilder;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
-struct AlibiBertAttention {
+struct JinaAttention {
     qkv_linear: Linear,
     dense: Linear,
     layer_norm: LayerNorm,
@@ -23,7 +22,7 @@ struct AlibiBertAttention {
     span: tracing::Span,
 }
 
-impl AlibiBertAttention {
+impl JinaAttention {
     pub fn load(vb: VarBuilder, config: &BertConfig, alibi_slopes: Option<Tensor>) -> Result<Self> {
         let attention_head_size = config.hidden_size / config.num_attention_heads;
         let all_head_size = config.num_attention_heads * attention_head_size;
@@ -117,7 +116,7 @@ impl AlibiBertAttention {
 }
 
 struct JinaBertLayer {
-    attention: AlibiBertAttention,
+    attention: JinaAttention,
     gated: Linear,
     output: Linear,
     layer_norm: LayerNorm,
@@ -130,7 +129,7 @@ struct JinaBertLayer {
 
 impl JinaBertLayer {
     pub fn load(vb: VarBuilder, config: &BertConfig, alibi: Option<Tensor>) -> Result<Self> {
-        let attention = AlibiBertAttention::load(vb.pp("attention"), config, alibi)?;
+        let attention = JinaAttention::load(vb.pp("attention"), config, alibi)?;
 
         let gated_weight = vb
             .pp("mlp")
@@ -174,14 +173,14 @@ impl JinaBertLayer {
         let residual = hidden_states.clone();
 
         let hidden_states = self.gated.forward(&hidden_states)?;
-        let gated = hidden_states.i((.., 0..self.intermediate_size))?;
+        let gated = hidden_states.narrow(1, 0, self.intermediate_size)?;
         let gated = match self.act {
             HiddenAct::Gelu => gated.gelu(),
             HiddenAct::Relu => gated.relu(),
             HiddenAct::Swiglu => gated.silu(),
         }?;
 
-        let non_gated = hidden_states.i((.., self.intermediate_size..))?;
+        let non_gated = hidden_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
         let hidden_states = (gated * non_gated)?;
 
         let hidden_states = self.output.forward(&hidden_states)?;
@@ -191,12 +190,12 @@ impl JinaBertLayer {
     }
 }
 
-struct BertEncoder {
+struct JinaBertEncoder {
     layers: Vec<JinaBertLayer>,
     span: tracing::Span,
 }
 
-impl BertEncoder {
+impl JinaBertEncoder {
     pub fn load(vb: VarBuilder, config: &BertConfig, alibi: Option<Tensor>) -> Result<Self> {
         let layers = (0..config.num_hidden_layers)
             .map(|index| {
@@ -205,7 +204,7 @@ impl BertEncoder {
             .collect::<Result<Vec<_>>>()?;
         let span = tracing::span!(tracing::Level::TRACE, "encoder");
 
-        Ok(BertEncoder { layers, span })
+        Ok(JinaBertEncoder { layers, span })
     }
 
     fn forward(&self, hidden_states: &Tensor, cu_seqlens: &Tensor, max_s: usize) -> Result<Tensor> {
@@ -223,8 +222,8 @@ impl BertEncoder {
 }
 
 pub struct FlashJinaBertModel {
-    embeddings: BertEmbeddings,
-    encoder: BertEncoder,
+    embeddings: JinaEmbeddings,
+    encoder: JinaBertEncoder,
     pool: Pool,
     pub device: Device,
 
@@ -266,14 +265,14 @@ impl FlashJinaBertModel {
         };
 
         let (embeddings, encoder) = match (
-            BertEmbeddings::load(vb.pp("embeddings"), config),
-            BertEncoder::load(vb.pp("encoder"), config, alibi.clone()),
+            JinaEmbeddings::load(vb.pp("embeddings"), config),
+            JinaBertEncoder::load(vb.pp("encoder"), config, alibi.clone()),
         ) {
             (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
             (Err(err), _) | (_, Err(err)) => {
                 if let (Ok(embeddings), Ok(encoder)) = (
-                    BertEmbeddings::load(vb.pp("bert.embeddings"), config),
-                    BertEncoder::load(vb.pp("bert.encoder"), config, alibi.clone()),
+                    JinaEmbeddings::load(vb.pp("bert.embeddings"), config),
+                    JinaBertEncoder::load(vb.pp("bert.encoder"), config, alibi.clone()),
                 ) {
                     (embeddings, encoder)
                 } else {
diff --git a/backends/candle/src/models/flash_jina_code.rs b/backends/candle/src/models/flash_jina_code.rs
index 0779c5d7..5df80be2 100644
--- a/backends/candle/src/models/flash_jina_code.rs
+++ b/backends/candle/src/models/flash_jina_code.rs
@@ -2,16 +2,14 @@ use crate::alibi::alibi_head_slopes;
 use crate::flash_attn::flash_attn_varlen;
 use crate::layers::{HiddenAct, LayerNorm, Linear};
 use crate::models::bert::PositionEmbeddingType;
-use crate::models::jina::{BertEmbeddings, JinaCodeConfig};
-use crate::models::Model;
+use crate::models::jina::JinaEmbeddings;
+use crate::models::{BertConfig, Model};
 use candle::{DType, Device, IndexOp, Result, Tensor};
 use candle_nn::VarBuilder;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
-struct AlibiBertAttention {
-    query_linear: Linear,
-    key_linear: Linear,
-    value_linear: Linear,
+struct JinaCodeAttention {
+    qkv_linear: Linear,
 
     dense: Linear,
     layer_norm_q: LayerNorm,
@@ -27,12 +25,8 @@ struct AlibiBertAttention {
     span: tracing::Span,
 }
 
-impl AlibiBertAttention {
-    pub fn load(
-        vb: VarBuilder,
-        config: &JinaCodeConfig,
-        alibi_slopes: Option<Tensor>,
-    ) -> Result<Self> {
+impl JinaCodeAttention {
+    pub fn load(vb: VarBuilder, config: &BertConfig, alibi_slopes: Option<Tensor>) -> Result<Self> {
         let attention_head_size = config.hidden_size / config.num_attention_heads;
         let all_head_size = config.num_attention_heads * attention_head_size;
         let hidden_size = config.hidden_size;
@@ -50,21 +44,22 @@ impl AlibiBertAttention {
             .get((all_head_size, hidden_size), "weight")?;
         let value_bias = vb.pp("self.value").get(all_head_size, "bias")?;
 
+        let qkv_weight = Tensor::cat(&[&query_weight, &key_weight, &value_weight], 0)?;
+        let qkv_bias = Tensor::cat(&[&query_bias, &key_bias, &value_bias], 0)?;
+
+        let qkv_linear = Linear::new(qkv_weight, Some(qkv_bias), None);
+
         let layer_norm_q = LayerNorm::load(
-            vp.pp("self").pp("layer_norm_q"),
+            vb.pp("self").pp("layer_norm_q"),
             config.hidden_size,
             config.layer_norm_eps as f32,
         )?;
         let layer_norm_k = LayerNorm::load(
-            vp.pp("self").pp("layer_norm_k"),
+            vb.pp("self").pp("layer_norm_k"),
             config.hidden_size,
             config.layer_norm_eps as f32,
         )?;
 
-        let query_linear = Linear::new(query_weight, Some(query_bias), None);
-        let key_linear = Linear::new(key_weight, Some(key_bias), None);
-        let value_linear = Linear::new(value_weight, Some(value_bias), None);
-
         let dense_weight = vb
             .pp("output")
             .pp("dense")
@@ -82,9 +77,7 @@ impl AlibiBertAttention {
         let softmax_scale = (1. / (attention_head_size as f64).sqrt()) as f32;
 
         Ok(Self {
-            query_linear,
-            key_linear,
-            value_linear,
+            qkv_linear,
             dense,
             layer_norm_q,
             layer_norm_k,
@@ -107,32 +100,39 @@ impl AlibiBertAttention {
 
         let residual = hidden_states.clone();
 
-        let query_layer = self.query_linear.forward(hidden_states)?;
-        let query_layer = self.layer_norm_q.forward(&query_layer, None)?;
-
-        let key_layer = self.key_linear.forward(hidden_states)?;
-        let key_layer = self.layer_norm_k.forward(&key_layer, None)?;
-
-        let value_layer = self.value_linear.forward(hidden_states)?;
+        let qkv = self.qkv_linear.forward(hidden_states)?;
 
         let mut new_qkv_shape = qkv.dims().to_vec();
         new_qkv_shape.pop();
-        new_qkv_shape.push(self.num_attention_heads);
+        new_qkv_shape.push(self.num_attention_heads * 3);
         new_qkv_shape.push(self.attention_head_size);
 
-        let query_layer = query_layer
-            .reshape(new_qkv_shape.as_slice())?
-            .transpose(1, 2)?;
-        let key_layer = key_layer
-            .reshape(new_qkv_shape.as_slice())?
-            .transpose(1, 2)?;
-        let value_layer = value_layer
-            .reshape(new_qkv_shape.as_slice())?
-            .transpose(1, 2)?;
+        let qkv = qkv.reshape(new_qkv_shape.as_slice())?;
+        // Splits heads
+        let qkv = qkv.chunk(3, 1)?;
+
+        // Flatten last dims again to go through the layer norm
+        let query_layer = &qkv[0].flatten_from(candle::D::Minus2)?;
+        let key_layer = &qkv[1].flatten_from(candle::D::Minus2)?;
+
+        // Layer norm on q and k
+        let query_layer = self.layer_norm_q.forward(&query_layer, None)?;
+        let key_layer = self.layer_norm_k.forward(&key_layer, None)?;
+
+        // Reshape back
+        let mut new_qk_shape = query_layer.dims().to_vec();
+        new_qk_shape.pop();
+        new_qk_shape.push(self.num_attention_heads);
+        new_qk_shape.push(self.attention_head_size);
+
+        let query_layer = query_layer.reshape(new_qk_shape.as_slice())?;
+        let key_layer = key_layer.reshape(new_qk_shape.as_slice())?;
+
+        let value_layer = &qkv[2];
 
         let attention = flash_attn_varlen(
-            query_layer,
-            key_layer,
+            &query_layer,
+            &key_layer,
             value_layer,
             self.alibi_slopes.as_ref(),
             cu_seqlens,
@@ -153,8 +153,8 @@ impl AlibiBertAttention {
     }
 }
 
-struct JinaBertLayer {
-    attention: AlibiBertAttention,
+struct JinaCodeBertLayer {
+    attention: JinaCodeAttention,
     up_gated_layer: Linear,
     down_layer: Linear,
     layer_norm_1: LayerNorm,
@@ -166,9 +166,9 @@ struct JinaBertLayer {
     span: tracing::Span,
 }
 
-impl JinaBertLayer {
-    pub fn load(vb: VarBuilder, config: &JinaCodeConfig, alibi: Option<Tensor>) -> Result<Self> {
-        let attention = AlibiBertAttention::load(vb.pp("attention"), config, alibi)?;
+impl JinaCodeBertLayer {
+    pub fn load(vb: VarBuilder, config: &BertConfig, alibi: Option<Tensor>) -> Result<Self> {
+        let attention = JinaCodeAttention::load(vb.pp("attention"), config, alibi)?;
 
         let up_gated_weight = vb
             .pp("mlp")
@@ -226,8 +226,8 @@ impl JinaBertLayer {
         // MLP block
         let residual = hidden_states.clone();
         let hidden_states = self.up_gated_layer.forward(&hidden_states)?;
-        let non_gated = hidden_states.i((.., .., 0..self.intermediate_size))?;
-        let gated = hidden_states.i((.., .., self.intermediate_size..))?;
+        let non_gated = hidden_states.narrow(1, 0, self.intermediate_size)?;
+        let gated = hidden_states.narrow(1, self.intermediate_size, self.intermediate_size)?;
         let gated = match self.act {
             HiddenAct::Gelu => gated.gelu(),
             HiddenAct::Relu => gated.relu(),
@@ -243,21 +243,21 @@ impl JinaBertLayer {
     }
 }
 
-struct BertEncoder {
-    layers: Vec<JinaBertLayer>,
+struct JinaCodeBertEncoder {
+    layers: Vec<JinaCodeBertLayer>,
     span: tracing::Span,
 }
 
-impl BertEncoder {
-    pub fn load(vb: VarBuilder, config: &JinaCodeConfig, alibi: Option<Tensor>) -> Result<Self> {
+impl JinaCodeBertEncoder {
+    pub fn load(vb: VarBuilder, config: &BertConfig, alibi: Option<Tensor>) -> Result<Self> {
         let layers = (0..config.num_hidden_layers)
             .map(|index| {
-                JinaBertLayer::load(vb.pp(format!("layer.{index}")), config, alibi.clone())
+                JinaCodeBertLayer::load(vb.pp(format!("layer.{index}")), config, alibi.clone())
             })
             .collect::<Result<Vec<_>>>()?;
         let span = tracing::span!(tracing::Level::TRACE, "encoder");
 
-        Ok(BertEncoder { layers, span })
+        Ok(JinaCodeBertEncoder { layers, span })
     }
 
     fn forward(&self, hidden_states: &Tensor, cu_seqlens: &Tensor, max_s: usize) -> Result<Tensor> {
@@ -275,8 +275,8 @@ impl BertEncoder {
 }
 
 pub struct FlashJinaCodeBertModel {
-    embeddings: BertEmbeddings,
-    encoder: BertEncoder,
+    embeddings: JinaEmbeddings,
+    encoder: JinaCodeBertEncoder,
     pool: Pool,
     pub device: Device,
 
@@ -284,7 +284,7 @@ pub struct FlashJinaCodeBertModel {
 }
 
 impl FlashJinaCodeBertModel {
-    pub fn load(vb: VarBuilder, config: &JinaCodeConfig, model_type: ModelType) -> Result<Self> {
+    pub fn load(vb: VarBuilder, config: &BertConfig, model_type: ModelType) -> Result<Self> {
         let alibi = match config.position_embedding_type {
             PositionEmbeddingType::Alibi => {
                 let alibi_slopes = alibi_head_slopes(config.num_attention_heads);
@@ -318,14 +318,14 @@ impl FlashJinaCodeBertModel {
         };
 
         let (embeddings, encoder) = match (
-            BertEmbeddings::load(vb.pp("embeddings"), config),
-            BertEncoder::load(vb.pp("encoder"), config, alibi.clone()),
+            JinaEmbeddings::load(vb.pp("embeddings"), config),
+            JinaCodeBertEncoder::load(vb.pp("encoder"), config, alibi.clone()),
         ) {
             (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
             (Err(err), _) | (_, Err(err)) => {
                 if let (Ok(embeddings), Ok(encoder)) = (
-                    BertEmbeddings::load(vb.pp("bert.embeddings"), config),
-                    BertEncoder::load(vb.pp("bert.encoder"), config, alibi.clone()),
+                    JinaEmbeddings::load(vb.pp("bert.embeddings"), config),
+                    JinaCodeBertEncoder::load(vb.pp("bert.encoder"), config, alibi.clone()),
                 ) {
                     (embeddings, encoder)
                 } else {
diff --git a/backends/candle/src/models/jina.rs b/backends/candle/src/models/jina.rs
index b1d75d94..768e1a6f 100644
--- a/backends/candle/src/models/jina.rs
+++ b/backends/candle/src/models/jina.rs
@@ -1,37 +1,13 @@
 use crate::alibi::build_alibi_tensor;
 use crate::layers::{get_cublas_lt_wrapper, HiddenAct, LayerNorm, Linear};
-use crate::models::Model;
 use crate::models::PositionEmbeddingType;
+use crate::models::{BertConfig, Model};
 use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
 use candle_nn::{Embedding, VarBuilder};
-use serde::Deserialize;
-use std::collections::HashMap;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
-#[derive(Debug, Clone, PartialEq, Deserialize)]
-pub struct JinaConfig {
-    pub vocab_size: usize,
-    pub hidden_size: usize,
-    pub num_hidden_layers: usize,
-    pub num_attention_heads: usize,
-    pub intermediate_size: usize,
-    pub hidden_act: HiddenAct,
-    pub hidden_dropout_prob: f64,
-    pub max_position_embeddings: usize,
-    pub type_vocab_size: usize,
-    pub initializer_range: f64,
-    pub layer_norm_eps: f64,
-    pub pad_token_id: usize,
-    #[serde(default)]
-    pub position_embedding_type: PositionEmbeddingType,
-    #[serde(default)]
-    pub use_cache: bool,
-    pub classifier_dropout: Option<f64>,
-    pub id2label: Option<HashMap<String, String>>,
-}
-
 #[derive(Debug)]
-pub struct BertEmbeddings {
+pub struct JinaEmbeddings {
     word_embeddings: Embedding,
     token_type_embeddings: Embedding,
     position_embeddings: Option<Embedding>,
@@ -39,8 +15,8 @@ pub struct BertEmbeddings {
     span: tracing::Span,
 }
 
-impl BertEmbeddings {
-    pub fn load(vb: VarBuilder, config: &JinaConfig) -> Result<Self> {
+impl JinaEmbeddings {
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
         let position_embeddings =
             if config.position_embedding_type == PositionEmbeddingType::Absolute {
                 Some(Embedding::new(
@@ -98,7 +74,7 @@ impl BertEmbeddings {
     }
 }
 
-struct BertAttention {
+struct JinaAttention {
     qkv_linear: Linear,
 
     dense: Linear,
@@ -111,8 +87,8 @@ struct BertAttention {
     span: tracing::Span,
 }
 
-impl BertAttention {
-    pub fn load(vb: VarBuilder, config: &JinaConfig) -> Result<Self> {
+impl JinaAttention {
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
         let attention_head_size = config.hidden_size / config.num_attention_heads;
         let all_head_size = config.num_attention_heads * attention_head_size;
         let hidden_size = config.hidden_size;
@@ -261,7 +237,7 @@ impl BertAttention {
 }
 
 struct JinaBertLayer {
-    attention: BertAttention,
+    attention: JinaAttention,
     gated: Linear,
     output: Linear,
     layer_norm: LayerNorm,
@@ -273,8 +249,8 @@ struct JinaBertLayer {
 }
 
 impl JinaBertLayer {
-    pub fn load(vb: VarBuilder, config: &JinaConfig) -> Result<Self> {
-        let attention = BertAttention::load(vb.pp("attention"), config)?;
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
+        let attention = JinaAttention::load(vb.pp("attention"), config)?;
 
         let gated_weight = vb
             .pp("mlp")
@@ -334,19 +310,19 @@ impl JinaBertLayer {
     }
 }
 
-struct BertEncoder {
+struct JinaBertEncoder {
     layers: Vec<JinaBertLayer>,
     span: tracing::Span,
 }
 
-impl BertEncoder {
-    pub fn load(vb: VarBuilder, config: &JinaConfig) -> Result<Self> {
+impl JinaBertEncoder {
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
         let layers = (0..config.num_hidden_layers)
             .map(|index| JinaBertLayer::load(vb.pp(format!("layer.{index}")), config))
             .collect::<Result<Vec<_>>>()?;
         let span = tracing::span!(tracing::Level::TRACE, "encoder");
 
-        Ok(BertEncoder { layers, span })
+        Ok(JinaBertEncoder { layers, span })
     }
 
     fn forward(&self, hidden_states: &Tensor, attention_bias: Option<&Tensor>) -> Result<Tensor> {
@@ -364,8 +340,8 @@ impl BertEncoder {
 }
 
 pub struct JinaBertModel {
-    embeddings: BertEmbeddings,
-    encoder: BertEncoder,
+    embeddings: JinaEmbeddings,
+    encoder: JinaBertEncoder,
     pool: Pool,
     alibi: Option<Tensor>,
 
@@ -378,7 +354,7 @@ pub struct JinaBertModel {
 }
 
 impl JinaBertModel {
-    pub fn load(vb: VarBuilder, config: &JinaConfig, model_type: ModelType) -> Result<Self> {
+    pub fn load(vb: VarBuilder, config: &BertConfig, model_type: ModelType) -> Result<Self> {
         let alibi = match config.position_embedding_type {
             PositionEmbeddingType::Alibi => Some(build_alibi_tensor(
                 config.max_position_embeddings,
@@ -402,14 +378,14 @@ impl JinaBertModel {
         };
 
         let (embeddings, encoder) = match (
-            BertEmbeddings::load(vb.pp("embeddings"), config),
-            BertEncoder::load(vb.pp("encoder"), config),
+            JinaEmbeddings::load(vb.pp("embeddings"), config),
+            JinaBertEncoder::load(vb.pp("encoder"), config),
         ) {
             (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
             (Err(err), _) | (_, Err(err)) => {
                 if let (Ok(embeddings), Ok(encoder)) = (
-                    BertEmbeddings::load(vb.pp("bert.embeddings"), config),
-                    BertEncoder::load(vb.pp("bert.encoder"), config),
+                    JinaEmbeddings::load(vb.pp("bert.embeddings"), config),
+                    JinaBertEncoder::load(vb.pp("bert.encoder"), config),
                 ) {
                     (embeddings, encoder)
                 } else {
diff --git a/backends/candle/src/models/jina_code.rs b/backends/candle/src/models/jina_code.rs
index ec8a8c84..348f5892 100644
--- a/backends/candle/src/models/jina_code.rs
+++ b/backends/candle/src/models/jina_code.rs
@@ -1,107 +1,14 @@
 use crate::alibi::build_alibi_tensor;
 use crate::layers::{get_cublas_lt_wrapper, HiddenAct, LayerNorm, Linear};
-use crate::models::Model;
+use crate::models::jina::JinaEmbeddings;
 use crate::models::PositionEmbeddingType;
-use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
-use candle_nn::{Embedding, VarBuilder};
-use serde::Deserialize;
-use std::collections::HashMap;
+use crate::models::{BertConfig, Model};
+use candle::{DType, Device, IndexOp, Result, Tensor, D};
+use candle_nn::VarBuilder;
 use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
-#[derive(Debug, Clone, PartialEq, Deserialize)]
-pub struct JinaCodeConfig {
-    pub vocab_size: usize,
-    pub hidden_size: usize,
-    pub num_hidden_layers: usize,
-    pub num_attention_heads: usize,
-    pub intermediate_size: usize,
-    pub hidden_act: HiddenAct,
-    pub hidden_dropout_prob: f64,
-    pub max_position_embeddings: usize,
-    pub type_vocab_size: usize,
-    pub initializer_range: f64,
-    pub layer_norm_eps: f64,
-    pub pad_token_id: usize,
-    #[serde(default)]
-    pub position_embedding_type: PositionEmbeddingType,
-    #[serde(default)]
-    pub use_cache: bool,
-    pub classifier_dropout: Option<f64>,
-    pub id2label: Option<HashMap<String, String>>,
-}
-
-#[derive(Debug)]
-pub struct BertEmbeddings {
-    word_embeddings: Embedding,
-    token_type_embeddings: Embedding,
-    position_embeddings: Option<Embedding>,
-    layer_norm: LayerNorm,
-    span: tracing::Span,
-}
-
-impl BertEmbeddings {
-    pub fn load(vb: VarBuilder, config: &JinaCodeConfig) -> Result<Self> {
-        let position_embeddings =
-            if config.position_embedding_type == PositionEmbeddingType::Absolute {
-                Some(Embedding::new(
-                    vb.pp("position_embeddings").get(
-                        (config.max_position_embeddings, config.hidden_size),
-                        "weight",
-                    )?,
-                    config.hidden_size,
-                ))
-            } else {
-                None
-            };
-
-        Ok(Self {
-            word_embeddings: Embedding::new(
-                vb.pp("word_embeddings")
-                    .get((config.vocab_size, config.hidden_size), "weight")?,
-                config.hidden_size,
-            ),
-            token_type_embeddings: Embedding::new(
-                vb.pp("token_type_embeddings")
-                    .get((config.type_vocab_size, config.hidden_size), "weight")?,
-                config.hidden_size,
-            ),
-            position_embeddings,
-            layer_norm: LayerNorm::load(
-                vb.pp("LayerNorm"),
-                config.hidden_size,
-                config.layer_norm_eps as f32,
-            )?,
-            span: tracing::span!(tracing::Level::TRACE, "embeddings"),
-        })
-    }
-
-    pub fn forward(
-        &self,
-        input_ids: &Tensor,
-        token_type_ids: &Tensor,
-        position_ids: &Tensor,
-    ) -> Result<Tensor> {
-        let _enter = self.span.enter();
-
-        let input_embeddings = self.word_embeddings.forward(input_ids)?;
-        let token_type_embeddings = self.token_type_embeddings.forward(token_type_ids)?;
-
-        if let Some(position_embeddings) = &self.position_embeddings {
-            let position_embeddings = position_embeddings.forward(position_ids)?;
-            let embeddings = input_embeddings.add(&token_type_embeddings)?;
-            self.layer_norm
-                .forward(&embeddings, Some(&position_embeddings))
-        } else {
-            self.layer_norm
-                .forward(&input_embeddings, Some(&token_type_embeddings))
-        }
-    }
-}
-
-struct BertAttention {
-    query_linear: Linear,
-    key_linear: Linear,
-    value_linear: Linear,
+struct JinaCodeAttention {
+    qkv_linear: Linear,
 
     dense: Linear,
     layer_norm_q: LayerNorm,
@@ -115,8 +22,8 @@ struct BertAttention {
     span: tracing::Span,
 }
 
-impl BertAttention {
-    pub fn load(vb: VarBuilder, config: &JinaCodeConfig) -> Result<Self> {
+impl JinaCodeAttention {
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
         let attention_head_size = config.hidden_size / config.num_attention_heads;
         let all_head_size = config.num_attention_heads * attention_head_size;
         let hidden_size = config.hidden_size;
@@ -136,6 +43,11 @@ impl BertAttention {
             .get((all_head_size, hidden_size), "weight")?;
         let value_bias = vb.pp("self.value").get(all_head_size, "bias")?;
 
+        let qkv_weight = Tensor::cat(&[&query_weight, &key_weight, &value_weight], 0)?;
+        let qkv_bias = Tensor::cat(&[&query_bias, &key_bias, &value_bias], 0)?;
+
+        let qkv_linear = Linear::new(qkv_weight, Some(qkv_bias), None);
+
         let layer_norm_q = LayerNorm::load(
             vb.pp("self").pp("layer_norm_q"),
             config.hidden_size,
@@ -147,10 +59,6 @@ impl BertAttention {
             config.layer_norm_eps as f32,
         )?;
 
-        let query_linear = Linear::new(query_weight, Some(query_bias), None);
-        let key_linear = Linear::new(key_weight, Some(key_bias), None);
-        let value_linear = Linear::new(value_weight, Some(value_bias), None);
-
         let dense_weight = vb
             .pp("output")
             .pp("dense")
@@ -168,9 +76,7 @@ impl BertAttention {
         let softmax_scale = 1. / (attention_head_size as f64).sqrt();
 
         Ok(Self {
-            query_linear,
-            key_linear,
-            value_linear,
+            qkv_linear,
             dense,
             layer_norm_q,
             layer_norm_k,
@@ -187,28 +93,40 @@ impl BertAttention {
         let device = hidden_states.device();
         let residual = hidden_states.clone();
 
-        let query_layer = self.query_linear.forward(hidden_states)?;
-        let query_layer = self.layer_norm_q.forward(&query_layer, None)?;
-
-        let key_layer = self.key_linear.forward(hidden_states)?;
-        let key_layer = self.layer_norm_k.forward(&key_layer, None)?;
+        let qkv = self.qkv_linear.forward(hidden_states)?;
 
-        let value_layer = self.value_linear.forward(hidden_states)?;
-
-        let mut new_qkv_shape = query_layer.dims().to_vec();
+        let mut new_qkv_shape = qkv.dims().to_vec();
         new_qkv_shape.pop();
-        new_qkv_shape.push(self.num_attention_heads);
+        new_qkv_shape.push(self.num_attention_heads * 3);
         new_qkv_shape.push(self.attention_head_size);
 
+        let qkv = qkv.reshape(new_qkv_shape.as_slice())?;
+
+        // Split heads
+        let qkv = qkv.chunk(3, 2)?;
+
+        // Flatten last dims again to go through the layer norm
+        let query_layer = &qkv[0].flatten_from(D::Minus2)?;
+        let key_layer = &qkv[1].flatten_from(D::Minus2)?;
+
+        // Layer norm on q and k
+        let query_layer = self.layer_norm_q.forward(query_layer, None)?;
+        let key_layer = self.layer_norm_k.forward(key_layer, None)?;
+
+        let mut new_qk_shape = query_layer.dims().to_vec();
+        new_qk_shape.pop();
+        new_qk_shape.push(self.num_attention_heads);
+        new_qk_shape.push(self.attention_head_size);
+
         let query_layer = query_layer
-            .reshape(new_qkv_shape.as_slice())?
-            .transpose(1, 2)?;
+            .reshape(new_qk_shape.as_slice())?
+            .transpose(1, 2)?
+            .contiguous()?;
         let key_layer = key_layer
-            .reshape(new_qkv_shape.as_slice())?
-            .transpose(1, 2)?;
-        let value_layer = value_layer
-            .reshape(new_qkv_shape.as_slice())?
-            .transpose(1, 2)?;
+            .reshape(new_qk_shape.as_slice())?
+            .transpose(1, 2)?
+            .contiguous()?;
+        let value_layer = &qkv[2].transpose(1, 2)?.contiguous()?;
 
         #[allow(unused_variables)]
         let context_layer = if let (Device::Cuda(_), Some(cublaslt)) =
@@ -290,7 +208,7 @@ impl BertAttention {
 }
 
 struct JinaCodeBertLayer {
-    attention: BertAttention,
+    attention: JinaCodeAttention,
     up_gated_layer: Linear,
     down_layer: Linear,
     layer_norm_1: LayerNorm,
@@ -303,8 +221,8 @@ struct JinaCodeBertLayer {
 }
 
 impl JinaCodeBertLayer {
-    pub fn load(vb: VarBuilder, config: &JinaCodeConfig) -> Result<Self> {
-        let attention = BertAttention::load(vb.pp("attention"), config)?;
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
+        let attention = JinaCodeAttention::load(vb.pp("attention"), config)?;
 
         let up_gated_weight = vb
             .pp("mlp")
@@ -381,19 +299,19 @@ impl JinaCodeBertLayer {
     }
 }
 
-struct BertEncoder {
+struct JinaCodeBertEncoder {
     layers: Vec<JinaCodeBertLayer>,
     span: tracing::Span,
 }
 
-impl BertEncoder {
-    pub fn load(vb: VarBuilder, config: &JinaCodeConfig) -> Result<Self> {
+impl JinaCodeBertEncoder {
+    pub fn load(vb: VarBuilder, config: &BertConfig) -> Result<Self> {
         let layers = (0..config.num_hidden_layers)
             .map(|index| JinaCodeBertLayer::load(vb.pp(format!("layer.{index}")), config))
             .collect::<Result<Vec<_>>>()?;
         let span = tracing::span!(tracing::Level::TRACE, "encoder");
 
-        Ok(BertEncoder { layers, span })
+        Ok(JinaCodeBertEncoder { layers, span })
     }
 
     fn forward(&self, hidden_states: &Tensor, attention_bias: Option<&Tensor>) -> Result<Tensor> {
@@ -411,8 +329,8 @@ impl BertEncoder {
 }
 
 pub struct JinaCodeBertModel {
-    embeddings: BertEmbeddings,
-    encoder: BertEncoder,
+    embeddings: JinaEmbeddings,
+    encoder: JinaCodeBertEncoder,
     pool: Pool,
     alibi: Option<Tensor>,
 
@@ -425,7 +343,7 @@ pub struct JinaCodeBertModel {
 }
 
 impl JinaCodeBertModel {
-    pub fn load(vb: VarBuilder, config: &JinaCodeConfig, model_type: ModelType) -> Result<Self> {
+    pub fn load(vb: VarBuilder, config: &BertConfig, model_type: ModelType) -> Result<Self> {
         let alibi = match config.position_embedding_type {
             PositionEmbeddingType::Alibi => Some(build_alibi_tensor(
                 config.max_position_embeddings,
@@ -449,14 +367,14 @@ impl JinaCodeBertModel {
         };
 
         let (embeddings, encoder) = match (
-            BertEmbeddings::load(vb.pp("embeddings"), config),
-            BertEncoder::load(vb.pp("encoder"), config),
+            JinaEmbeddings::load(vb.pp("embeddings"), config),
+            JinaCodeBertEncoder::load(vb.pp("encoder"), config),
         ) {
             (Ok(embeddings), Ok(encoder)) => (embeddings, encoder),
             (Err(err), _) | (_, Err(err)) => {
                 if let (Ok(embeddings), Ok(encoder)) = (
-                    BertEmbeddings::load(vb.pp("bert.embeddings"), config),
-                    BertEncoder::load(vb.pp("bert.encoder"), config),
+                    JinaEmbeddings::load(vb.pp("bert.embeddings"), config),
+                    JinaCodeBertEncoder::load(vb.pp("bert.encoder"), config),
                 ) {
                     (embeddings, encoder)
                 } else {
diff --git a/backends/candle/src/models.rs b/backends/candle/src/models/mod.rs
similarity index 93%
rename from backends/candle/src/models.rs
rename to backends/candle/src/models/mod.rs
index d64f5a35..a7d6b267 100644
--- a/backends/candle/src/models.rs
+++ b/backends/candle/src/models/mod.rs
@@ -28,8 +28,8 @@ mod flash_distilbert;
 pub use bert::{BertConfig, BertModel, PositionEmbeddingType};
 use candle::{Result, Tensor};
 pub use distilbert::{DistilBertConfig, DistilBertModel};
-pub use jina::{JinaBertModel, JinaConfig};
-pub use jina_code::{JinaCodeBertModel, JinaCodeConfig};
+pub use jina::JinaBertModel;
+pub use jina_code::JinaCodeBertModel;
 pub use nomic::{NomicBertModel, NomicConfig};
 use text_embeddings_backend_core::Batch;
 
diff --git a/backends/candle/tests/common.rs b/backends/candle/tests/common.rs
index b9d23995..d7ebc67d 100644
--- a/backends/candle/tests/common.rs
+++ b/backends/candle/tests/common.rs
@@ -25,7 +25,7 @@ impl Score {
 impl PartialEq for Score {
     fn eq(&self, other: &Self) -> bool {
         // Default tolerance for equality
-        self.is_close(other, 5e-3)
+        self.is_close(other, 6e-3)
     }
 }
 
diff --git a/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_batch.snap b/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_batch.snap
index 3441cb71..0ad889a9 100644
--- a/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_batch.snap
+++ b/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_batch.snap
@@ -1,772 +1,2309 @@
 ---
 source: backends/candle/tests/test_flash_jina_code.rs
+assertion_line: 37
 expression: embeddings_batch
 ---
-- - -0.011624558
-  - -0.0016926473
-  - -0.006434922
-  - -0.009909191
-  - 0.027506275
-  - 0.022786874
-  - -0.059169117
-  - -0.047887173
-  - 0.038912594
-  - 0.025214802
-  - -0.014350341
-  - 0.039117776
-  - 0.03485464
-  - 0.05296896
-  - 0.034907352
-  - 0.0013971328
-  - 0.0019907136
-  - 0.052471623
-  - -0.014562107
-  - 0.00030206257
-  - -0.02770458
-  - 0.02685756
-  - -0.015385578
-  - -0.012668428
-  - -0.025259107
-  - 0.00836893
-  - 0.028925523
-  - -0.035507426
-  - -0.04220648
-  - 0.047328673
-  - -0.083232224
-  - -0.02608385
-  - -0.012809777
-  - -0.0140402755
-  - -0.013649549
-  - -0.013641793
-  - 0.02880054
-  - 0.04465023
-  - -0.0274121
-  - -0.03170939
-  - -0.047180377
-  - -0.0349574
-  - -0.037762504
-  - 0.024104225
-  - -0.039361924
-  - -0.024559166
-  - -0.0138650155
-  - -0.049862508
-  - -0.0507675
-  - -0.07775355
-  - -0.055519626
-  - -0.025151063
-  - 0.040781654
-  - 0.0039354665
-  - 0.015940087
-  - -0.022214677
-  - -0.013484792
-  - -0.00070730236
-  - -0.009409981
-  - 0.0016682384
-  - 0.016079267
-  - -0.032368172
-  - 0.024572799
-  - 0.026780155
-  - -0.025954
-  - 0.021282032
-  - -0.047395118
-  - 0.044386413
-  - -0.023020437
-  - 0.0056320634
-  - -0.017416032
-  - -0.024118245
-  - 0.05878816
-  - 0.00059366866
-  - -0.018553924
-  - 0.003762008
-  - 0.0035026476
-  - -0.0077498616
-  - -0.004517831
-  - 0.008116448
-  - -0.010922055
-  - 0.037391223
-  - -0.03318112
-  - -0.07876148
-  - -0.0018068684
-  - -0.0484656
-  - -0.038104814
-  - -0.0070756334
-  - -0.015427567
-  - -0.04499487
-  - 0.008910639
-  - 0.029193114
-  - 0.032674707
-  - 0.0305758
-  - -0.017541302
-  - 0.028164856
-  - -0.0344121
-  - 0.016969312
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diff --git a/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_single.snap b/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_single.snap
index 5db1bc22..7d7d6819 100644
--- a/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_single.snap
+++ b/backends/candle/tests/snapshots/test_flash_jina_code__jina_code_single.snap
@@ -1,772 +1,773 @@
 ---
 source: backends/candle/tests/test_flash_jina_code.rs
+assertion_line: 48
 expression: embeddings_single
 ---
-- - -0.011624558
-  - -0.0016926473
-  - -0.006434922
-  - -0.009909191
-  - 0.027506275
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diff --git a/backends/candle/tests/snapshots/test_jina_code__jina_code_batch.snap b/backends/candle/tests/snapshots/test_jina_code__jina_code_batch.snap
index 87f5bbd6..c82cb3b7 100644
--- a/backends/candle/tests/snapshots/test_jina_code__jina_code_batch.snap
+++ b/backends/candle/tests/snapshots/test_jina_code__jina_code_batch.snap
@@ -1,772 +1,2309 @@
 ---
 source: backends/candle/tests/test_jina_code.rs
+assertion_line: 34
 expression: embeddings_batch
 ---
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diff --git a/backends/candle/tests/snapshots/test_jina_code__jina_code_single.snap b/backends/candle/tests/snapshots/test_jina_code__jina_code_single.snap
index ee1cdfa1..dc2119de 100644
--- a/backends/candle/tests/snapshots/test_jina_code__jina_code_single.snap
+++ b/backends/candle/tests/snapshots/test_jina_code__jina_code_single.snap
@@ -1,772 +1,773 @@
 ---
 source: backends/candle/tests/test_jina_code.rs
+assertion_line: 45
 expression: embeddings_single
 ---
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diff --git a/backends/candle/tests/test_flash_jina.rs b/backends/candle/tests/test_flash_jina.rs
index 4e42428a..4a5f8276 100644
--- a/backends/candle/tests/test_flash_jina.rs
+++ b/backends/candle/tests/test_flash_jina.rs
@@ -34,7 +34,7 @@ fn test_flash_jina_small() -> Result<()> {
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
     let embeddings_batch = SnapshotScores::from(pooled_embeddings);
-    insta::assert_yaml_snapshot!("flash_jina_batch", embeddings_batch, &matcher);
+    insta::assert_yaml_snapshot!("jina_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
         vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
@@ -45,7 +45,7 @@ fn test_flash_jina_small() -> Result<()> {
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
     let embeddings_single = SnapshotScores::from(pooled_embeddings);
 
-    insta::assert_yaml_snapshot!("flash_jina_single", embeddings_single, &matcher);
+    insta::assert_yaml_snapshot!("jina_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
     assert_eq!(embeddings_batch[2], embeddings_single[0]);
 
diff --git a/backends/candle/tests/test_flash_jina_code.rs b/backends/candle/tests/test_flash_jina_code.rs
index 74035117..508bf722 100644
--- a/backends/candle/tests/test_flash_jina_code.rs
+++ b/backends/candle/tests/test_flash_jina_code.rs
@@ -34,7 +34,7 @@ fn test_flash_jina_code_base() -> Result<()> {
 
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
     let embeddings_batch = SnapshotScores::from(pooled_embeddings);
-    insta::assert_yaml_snapshot!("flash_jina_code_batch", embeddings_batch, &matcher);
+    insta::assert_yaml_snapshot!("jina_code_batch", embeddings_batch, &matcher);
 
     let input_single = batch(
         vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
@@ -45,7 +45,7 @@ fn test_flash_jina_code_base() -> Result<()> {
     let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
     let embeddings_single = SnapshotScores::from(pooled_embeddings);
 
-    insta::assert_yaml_snapshot!("flash_jina_code_single", embeddings_single, &matcher);
+    insta::assert_yaml_snapshot!("jina_code_single", embeddings_single, &matcher);
     assert_eq!(embeddings_batch[0], embeddings_single[0]);
     assert_eq!(embeddings_batch[2], embeddings_single[0]);