liniting: this just runs a quick clippy fix

backends/candle/src/layers/mod.rs

@@ -8,10 +8,10 @@ mod rms_norm;
 mod rotary;
 
 pub use cublaslt::get_cublas_lt_wrapper;
+#[allow(unused_imports)]
+pub use index_select::index_select;
 pub use layer_norm::{LayerNorm, LayerNormNoBias};
 pub use linear::{HiddenAct, Linear};
 #[allow(unused_imports)]
 pub use rms_norm::RMSNorm;
-#[allow(unused_imports)]
-pub use index_select::index_select;
 pub use rotary::{apply_rotary, get_cos_sin, get_inv_freqs, RopeScaling};

backends/candle/src/models/qwen2.rs

@@ -1,6 +1,5 @@
 use crate::layers::HiddenAct;
 use serde::Deserialize;
-use tracing;
 
 fn default_is_causal() -> bool {
     tracing::warn!("is_causal not set in Qwen2Config, defaulting to true. e.g. Alibaba-NLP/gte-Qwen2-1.5B-instruct/ was trained with causal=False attention, but jinaai/jina-code-embeddings-0.5b with causal=True. Please set this field explicitly in the huggingface repo to avoid this warning.");

backends/candle/tests/common.rs

@@ -85,7 +85,7 @@ impl Deref for SnapshotEmbeddings {
 
 impl From<Vec<Vec<f32>>> for SnapshotEmbeddings {
     fn from(value: Vec<Vec<f32>>) -> Self {
-        Self(value.into_iter().map(|v| SnapEmbedding(v)).collect())
+        Self(value.into_iter().map(SnapEmbedding).collect())
     }
 }
 
@@ -181,7 +181,7 @@ pub fn download_artifacts(
                 }
                 _ => {
                     for path in &paths {
-                        download_dense_module(&api_repo, &path)?;
+                        download_dense_module(&api_repo, path)?;
                     }
                     Some(paths)
                 }

backends/candle/tests/test_bert.rs

@@ -166,9 +166,7 @@ fn test_emotions() -> Result<()> {
     let matcher = relative_matcher();
 
     let predictions: Vec<Vec<f32>> = backend
-        .predict(input_batch)?
-        .into_iter()
-        .map(|(_, v)| v)
+        .predict(input_batch)?.into_values()
         .collect();
     let predictions_batch = SnapshotScores::from(predictions);
     insta::assert_yaml_snapshot!("emotions_batch", predictions_batch, &matcher);
@@ -180,9 +178,7 @@ fn test_emotions() -> Result<()> {
     );
 
     let predictions: Vec<Vec<f32>> = backend
-        .predict(input_single)?
-        .into_iter()
-        .map(|(_, v)| v)
+        .predict(input_single)?.into_values()
         .collect();
     let predictions_single = SnapshotScores::from(predictions);
 
@@ -222,9 +218,7 @@ fn test_bert_classification() -> Result<()> {
     );
 
     let predictions: Vec<Vec<f32>> = backend
-        .predict(input_single)?
-        .into_iter()
-        .map(|(_, v)| v)
+        .predict(input_single)?.into_values()
         .collect();
     let predictions_single = SnapshotScores::from(predictions);
 

backends/candle/tests/test_dense.rs

@@ -65,7 +65,7 @@ fn test_stella_en_400m_v5_dense_768() -> Result<()> {
     let (model_root, dense_paths) = download_artifacts(
         "dunzhang/stella_en_400M_v5",
         None,
-        Some("2_Dense_768".into()),
+        Some("2_Dense_768"),
     )?;
     let tokenizer = load_tokenizer(&model_root)?;
 

backends/candle/tests/test_gte.rs

@@ -165,9 +165,7 @@ fn test_gte_classification() -> Result<()> {
     );
 
     let predictions: Vec<Vec<f32>> = backend
-        .predict(input_single)?
-        .into_iter()
-        .map(|(_, v)| v)
+        .predict(input_single)?.into_values()
         .collect();
     let predictions_single = SnapshotScores::from(predictions);
 

backends/candle/tests/test_jina.rs

@@ -71,9 +71,7 @@ fn test_jina_rerank() -> Result<()> {
     );
 
     let predictions: Vec<Vec<f32>> = backend
-        .predict(input_single)?
-        .into_iter()
-        .map(|(_, v)| v)
+        .predict(input_single)?.into_values()
         .collect();
 
     let predictions = SnapshotScores::from(predictions);

backends/candle/tests/test_modernbert.rs

@@ -195,9 +195,7 @@ fn test_modernbert_classification() -> Result<()> {
     );
 
     let predictions: Vec<Vec<f32>> = backend
-        .predict(input_single)?
-        .into_iter()
-        .map(|(_, v)| v)
+        .predict(input_single)?.into_values()
         .collect();
     let predictions_single = SnapshotScores::from(predictions);
 
@@ -234,9 +232,7 @@ fn test_modernbert_classification_mean_pooling() -> Result<()> {
     );
 
     let predictions: Vec<Vec<f32>> = backend
-        .predict(input_single)?
-        .into_iter()
-        .map(|(_, v)| v)
+        .predict(input_single)?.into_values()
         .collect();
     let predictions_single = SnapshotScores::from(predictions);