fix clippy

Cargo.toml

@@ -14,6 +14,7 @@ members = [
 version = "0.1.0"
 
 [workspace.dependencies]
+reqwest = "0.12.1"
 anyhow = "1.0.81"
 async-trait = "0.1.78"
 candle = { git = "https://github.com/huggingface/candle", package = "candle-core", version = "0.4.2" }
@@ -33,7 +34,6 @@ image = { version = "0.25.0", default-features = false, features = [
 serde = "1.0.197"
 serde_json = "1.0.114"
 rand = "0.8.5"
-reqwest = "0.12.1"
 thiserror = "1.0.58"
 tokenizers = "0.15.2"
 tokio = "1.36.0"

atoma-inference/src/candle/stable_diffusion.rs

@@ -43,9 +43,6 @@ pub struct Input {
 
     sd_version: StableDiffusionVersion,
 
-    /// Generate intermediary images at each step.
-    intermediary_images: bool,
-
     use_flash_attn: bool,
 
     use_f16: bool,
@@ -78,7 +75,6 @@ impl Input {
             n_steps: Some(20),
             num_samples: 1,
             sd_version: StableDiffusionVersion::V1_5,
-            intermediary_images: false,
             use_flash_attn: false,
             use_f16: true,
             guidance_scale: None,
@@ -315,6 +311,7 @@ impl CandleModel for StableDiffusion {
     }
 }
 
+#[allow(dead_code)]
 #[derive(Clone, Copy)]
 enum StableDiffusionVersion {
     V1_5,

atoma-inference/src/lib.rs

@@ -1,10 +1,6 @@
-pub mod model_thread;
+pub mod apis;
 pub mod candle;
-pub mod config;
-pub mod core_thread;
+pub mod model_thread;
 pub mod models;
 pub mod service;
 pub mod specs;
-
-pub mod apis;
-pub mod models;

atoma-inference/src/models/candle/mamba.rs

@@ -64,8 +64,7 @@ impl ModelTrait for MambaModel {
         let tokenizer_filename = filenames[1].clone();
         let weights_filenames = filenames[2..].to_vec();
 
-        let tokenizer =
-            Tokenizer::from_file(tokenizer_filename).map_err(ModelError::TokenizerError)?;
+        let tokenizer = Tokenizer::from_file(tokenizer_filename)?;
 
         let config: Config =
             serde_json::from_slice(&std::fs::read(config_filename).map_err(ModelError::IoError)?)
@@ -110,8 +109,7 @@ impl ModelTrait for MambaModel {
         let mut tokens = self
             .tokenizer
             .tokenizer()
-            .encode(prompt, true)
-            .map_err(ModelError::TokenizerError)?
+            .encode(prompt, true)?
             .get_ids()
             .to_vec();
         let mut logits_processor =

atoma-inference/src/models/mod.rs

@@ -41,22 +41,22 @@ pub trait Response: Send + 'static {
 
 #[derive(Debug, Error)]
 pub enum ModelError {
-    #[error("Tokenizer error: `{0}`")]
-    TokenizerError(Box<dyn std::error::Error + Send + Sync>),
-    #[error("IO error: `{0}`")]
-    IoError(std::io::Error),
     #[error("Deserialize error: `{0}`")]
-    DeserializeError(serde_json::Error),
-    #[error("Candle error: `{0}`")]
-    CandleError(CandleError),
+    DeserializeError(#[from] serde_json::Error),
     #[error("{0}")]
     Msg(String),
-}
-
-impl From<CandleError> for ModelError {
-    fn from(error: CandleError) -> Self {
-        Self::CandleError(error)
-    }
+    #[error("Candle error: `{0}`")]
+    CandleError(#[from] CandleError),
+    #[error("Config error: `{0}`")]
+    Config(String),
+    #[error("Image error: `{0}`")]
+    ImageError(#[from] image::ImageError),
+    #[error("Io error: `{0}`")]
+    IoError(#[from] std::io::Error),
+    #[error("Error: `{0}`")]
+    BoxedError(#[from] Box<dyn std::error::Error + Send + Sync>),
+    #[error("ApiError error: `{0}`")]
+    ApiError(#[from] hf_hub::api::sync::ApiError),
 }
 
 #[macro_export]