update model_name

src/models/deepseek_ocr/generate.rs

@@ -42,9 +42,10 @@ impl DeepseekOCRGenerateModel {
         let device = &get_device(device);
         let dtype = get_dtype(dtype, &cfg_dtype);
         let model_name = std::path::Path::new(path)
-            .file_stem() // 获取文件名主干（不含扩展名）
+            .file_name() 
             .and_then(|s| s.to_str())
-            .unwrap_or("deepseek-ocr");
+            .unwrap_or("deepseek-ocr")
+            .to_string();
         let version = if model_name.contains("2") || cfg.vision_config.width.qwen2_0_5b.is_some() {
             2usize
         } else {

src/models/fun_asr_nano/generate.rs

@@ -78,6 +78,11 @@ impl FunAsrNanoGenerateModel {
         }
         let vb = VarBuilder::from_tensors(dict_to_hashmap, dtype, &device);
         let fun_asr_nano = FunAsrNanoModel::new(vb, &cfg, &llm_cfg)?;
+        let model_name = std::path::Path::new(path)
+            .file_name()
+            .and_then(|s| s.to_str())
+            .unwrap_or("fun-asr-nano")
+            .to_string();
         Ok(Self {
             tokenizer,
             processor,
@@ -87,7 +92,7 @@ impl FunAsrNanoGenerateModel {
             eos_token_id1: generation_config.eos_token_id[0] as u32,
             eos_token_id2: generation_config.eos_token_id[1] as u32,
             generation_config,
-            model_name: "fun-asr-nano".to_string(),
+            model_name,
         })
     }
 }

src/models/glm_asr_nano/generate.rs

@@ -48,6 +48,11 @@ impl<'a> GlmAsrNanoGenerateModel<'a> {
         let model_list = find_type_files(path, "safetensors")?;
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, &device)? };
         let glm_asr_nano = GlmAsrNanoModel::new(vb, cfg)?;
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("glm-asr-nano")
+            .to_string();
         Ok(Self {
             chat_template,
             tokenizer,
@@ -58,7 +63,7 @@ impl<'a> GlmAsrNanoGenerateModel<'a> {
             eos_token_id1: 59246,
             eos_token_id2: 59253,
             eos_token_id3: 59255,
-            model_name: "glm-asr-nano".to_string(),
+            model_name,
         })
     }
 }

src/models/glm_ocr/generate.rs

@@ -57,7 +57,11 @@ impl GlmOcrGenerateModel {
         let generation_config_path = path.to_string() + "/generation_config.json";
         let generation_config: GlmOcrGenerationConfig =
             serde_json::from_slice(&std::fs::read(generation_config_path)?)?;
-
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("glm-ocr")
+            .to_string();
         Ok(Self {
             // chat_template,
             tokenizer,
@@ -65,7 +69,7 @@ impl GlmOcrGenerateModel {
             model,
             device,
             eos_token_ids: generation_config.eos_token_id.clone(),
-            model_name: "glm-ocr".to_string(),
+            model_name,
             image_token_id: cfg.image_token_id,
             image_start_token_id: cfg.image_start_token_id,
             image_end_token_id: cfg.image_end_token_id,

src/models/hunyuan_ocr/generate.rs

@@ -52,6 +52,11 @@ impl<'a> HunyuanOCRGenerateModel<'a> {
         let generation_config_path = path.to_string() + "/generation_config.json";
         let generation_config: HunyuanOCRGenerationConfig =
             serde_json::from_slice(&std::fs::read(generation_config_path)?)?;
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("hunyuan_ocr")
+            .to_string();
         Ok(Self {
             chat_template,
             tokenizer,
@@ -61,7 +66,7 @@ impl<'a> HunyuanOCRGenerateModel<'a> {
             eos_token_id1: generation_config.eos_token_id[0] as u32,
             eos_token_id2: generation_config.eos_token_id[1] as u32,
             generation_config,
-            model_name: "hunyuan_ocr".to_string(),
+            model_name,
         })
     }
 }

src/models/minicpm4/generate.rs

@@ -40,7 +40,11 @@ impl<'a> MiniCPMGenerateModel<'a> {
         let model_list = find_type_files(path, "safetensors")?;
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, device)? };
         let minicpm = MiniCPMModel::new(vb, cfg)?;
-
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("minicpm4")
+            .to_string();
         Ok(MiniCPMGenerateModel {
             chat_template,
             tokenizer,
@@ -48,7 +52,7 @@ impl<'a> MiniCPMGenerateModel<'a> {
             device: device.clone(),
             endoftext_id,
             im_end_id,
-            model_name: "minicpm4".to_string(),
+            model_name,
         })
     }
 }

src/models/paddleocr_vl/generate.rs

@@ -45,7 +45,11 @@ impl<'a> PaddleOCRVLGenerateModel<'a> {
         let model_list = find_type_files(path, "safetensors")?;
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, device)? };
         let paddleocr_vl = PaddleOCRVLModel::new(cfg.clone(), vb)?;
-
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("paddleocr_vl")
+            .to_string();
         Ok(PaddleOCRVLGenerateModel {
             chat_template,
             tokenizer,
@@ -54,7 +58,7 @@ impl<'a> PaddleOCRVLGenerateModel<'a> {
             cfg,
             device: device.clone(),
             end_token_id,
-            model_name: "paddleocr_vl".to_string(),
+            model_name,
         })
     }
 }

src/models/qwen2_5vl/config.rs

@@ -1,5 +1,7 @@
 use candle_nn::Activation;
 
+use crate::models::qwen2::Qwen2Config;
+
 #[derive(Debug, Clone, PartialEq, serde::Deserialize)]
 pub struct VisionConfig {
     pub depth: usize,
@@ -53,6 +55,27 @@ pub struct Qwen2_5VLConfig {
     pub vocab_size: usize,
 }
 
+impl Qwen2_5VLConfig {
+    pub fn to_qwen2cfg(&self) -> Qwen2Config {
+        Qwen2Config {
+            vocab_size: self.vocab_size,
+            hidden_size: self.hidden_size,
+            intermediate_size: self.intermediate_size,
+            num_hidden_layers: self.num_hidden_layers,
+            num_attention_heads: self.num_attention_heads,
+            num_key_value_heads: self.num_key_value_heads,
+            max_position_embeddings: self.max_position_embeddings,
+            sliding_window: self.sliding_window,
+            max_window_layers: self.max_window_layers,
+            tie_word_embeddings: self.tie_word_embeddings,
+            rope_theta: self.rope_theta,
+            rms_norm_eps: self.rms_norm_eps,
+            use_sliding_window: self.use_sliding_window,
+            hidden_act: self.hidden_act,
+        }
+    }
+}
+
 pub struct VisionSetting {
     pub image_factor: u32,
     pub min_pixels: u32,

src/models/qwen2_5vl/generate.rs

@@ -48,7 +48,11 @@ impl<'a> Qwen2_5VLGenerateModel<'a> {
         let model_list = find_type_files(path, "safetensors")?;
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, device)? };
         let qwen2_5_vl = Qwen2_5VLModel::new(cfg, vb)?;
-
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("qwen2.5vl")
+            .to_string();
         Ok(Qwen2_5VLGenerateModel {
             chat_template,
             tokenizer,
@@ -57,7 +61,7 @@ impl<'a> Qwen2_5VLGenerateModel<'a> {
             device: device.clone(),
             endoftext_id,
             im_end_id,
-            model_name: "qwen2.5vl".to_string(),
+            model_name,
         })
     }
 }

src/models/qwen2_5vl/model.rs

@@ -4,8 +4,7 @@ use candle_nn::{Init, Linear, Module, RmsNorm, VarBuilder, linear, linear_no_bia
 
 use crate::{
     models::{
-        common::{GateUpDownMLP, eager_attention_forward},
+        common::{GateUpDownMLP, eager_attention_forward}, qwen2::Qwen2DecoderLayer, qwen2_5vl::config::{Qwen2_5VLConfig, RopeScaling}
-        qwen2_5vl::config::{Qwen2_5VLConfig, RopeScaling},
     },
     position_embed::rope::{
         Qwen2_5VLTextRotaryEmbedding, Qwen2_5VisionRotaryEmbedding, apply_rotary_pos_emb,
@@ -512,158 +511,11 @@ impl Qwen2_5VLVisionModel {
     }
 }
 
-#[derive(Debug, Clone)]
-struct Qwen2_5VLTextAttention {
-    q_proj: Linear,
-    k_proj: Linear,
-    v_proj: Linear,
-    o_proj: Linear,
-    num_heads: usize,
-    num_kv_heads: usize,
-    num_kv_groups: usize,
-    head_dim: usize,
-    hidden_size: usize,
-    kv_cache: Option<(Tensor, Tensor)>,
-}
-
-impl Qwen2_5VLTextAttention {
-    fn new(cfg: &Qwen2_5VLConfig, vb: VarBuilder) -> Result<Self> {
-        let hidden_size = cfg.hidden_size;
-        let num_heads = cfg.num_attention_heads;
-        let num_kv_heads = cfg.num_key_value_heads;
-        let num_kv_groups = num_heads / num_kv_heads;
-        let head_dim = hidden_size / num_heads;
-        let q_proj = linear(hidden_size, num_heads * head_dim, vb.pp("q_proj"))?;
-        let k_proj = linear(hidden_size, num_kv_heads * head_dim, vb.pp("k_proj"))?;
-        let v_proj = linear(hidden_size, num_kv_heads * head_dim, vb.pp("v_proj"))?;
-        let o_proj = linear_no_bias(hidden_size, hidden_size, vb.pp("o_proj"))?;
-        Ok(Self {
-            q_proj,
-            k_proj,
-            v_proj,
-            o_proj,
-            num_heads,
-            num_kv_heads,
-            num_kv_groups,
-            head_dim,
-            hidden_size,
-            kv_cache: None,
-        })
-    }
-
-    fn forward(
-        &mut self,
-        xs: &Tensor,
-        cos: &Tensor,
-        sin: &Tensor,
-        attention_mask: Option<&Tensor>,
-    ) -> Result<Tensor> {
-        let (b_sz, q_len, _) = xs.dims3()?;
-        let query_states = self.q_proj.forward(xs)?;
-        let key_states = self.k_proj.forward(xs)?;
-        let value_states = self.v_proj.forward(xs)?;
-        let query_states = query_states
-            .reshape((b_sz, q_len, self.num_heads, self.head_dim))?
-            .transpose(1, 2)?;
-        let key_states = key_states
-            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
-            .transpose(1, 2)?;
-        let value_states = value_states
-            .reshape((b_sz, q_len, self.num_kv_heads, self.head_dim))?
-            .transpose(1, 2)?;
-        let (query_states, key_states) =
-            apply_rotary_pos_emb(&query_states, &key_states, cos, sin, false)?;
-        let (key_states, value_states) = match &self.kv_cache {
-            None => (key_states, value_states),
-            Some((prev_k, prev_v)) => {
-                let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
-                let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
-                (key_states, value_states)
-            }
-        };
-
-        self.kv_cache = Some((key_states.clone(), value_states.clone()));
-        let scale = 1f64 / f64::sqrt(self.head_dim as f64);
-        let attn_output = eager_attention_forward(
-            &query_states,
-            &key_states,
-            &value_states,
-            Some(self.num_kv_groups),
-            attention_mask,
-            scale,
-        )?;
-        let attn_output = attn_output.reshape((b_sz, q_len, self.hidden_size))?;
-        let attn_output = attn_output.apply(&self.o_proj)?;
-        Ok(attn_output)
-    }
-
-    fn clear_kv_cache(&mut self) {
-        self.kv_cache = None
-    }
-}
-
-#[derive(Debug, Clone)]
-struct Qwen2_5VLTextDecoderLayer {
-    self_attn: Qwen2_5VLTextAttention,
-    mlp: GateUpDownMLP,
-    input_layernorm: RmsNorm,
-    post_attention_layernorm: RmsNorm,
-}
-
-impl Qwen2_5VLTextDecoderLayer {
-    fn new(cfg: &Qwen2_5VLConfig, vb: VarBuilder) -> Result<Self> {
-        let self_attn = Qwen2_5VLTextAttention::new(cfg, vb.pp("self_attn"))?;
-        let mlp = GateUpDownMLP::new(
-            vb.pp("mlp"),
-            cfg.hidden_size,
-            cfg.intermediate_size,
-            cfg.hidden_act,
-            false,
-            None,
-            None,
-            None,
-        )?;
-        let input_layernorm =
-            rms_norm(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("input_layernorm"))?;
-        let post_attention_layernorm = rms_norm(
-            cfg.hidden_size,
-            cfg.rms_norm_eps,
-            vb.pp("post_attention_layernorm"),
-        )?;
-        Ok(Self {
-            self_attn,
-            mlp,
-            input_layernorm,
-            post_attention_layernorm,
-        })
-    }
-
-    fn forward(
-        &mut self,
-        xs: &Tensor,
-        cos: &Tensor,
-        sin: &Tensor,
-        attention_mask: Option<&Tensor>,
-    ) -> Result<Tensor> {
-        let residual = xs;
-        let xs = self.input_layernorm.forward(xs)?;
-        let xs = self.self_attn.forward(&xs, cos, sin, attention_mask)?;
-        let xs = (xs + residual)?;
-        let residual = &xs;
-        let xs = xs.apply(&self.post_attention_layernorm)?.apply(&self.mlp)?;
-        let xs = (residual + xs)?;
-        Ok(xs)
-    }
-
-    fn clear_kv_cache(&mut self) {
-        self.self_attn.clear_kv_cache()
-    }
-}
 
 #[derive(Debug, Clone)]
 pub struct Qwen2_5VLTextModel {
     pub embed_tokens: candle_nn::Embedding,
-    layers: Vec<Qwen2_5VLTextDecoderLayer>,
+    layers: Vec<Qwen2DecoderLayer>,
     norm: RmsNorm,
     rotary_emb: Qwen2_5VLTextRotaryEmbedding,
     dtype: DType,
@@ -677,9 +529,10 @@ impl Qwen2_5VLTextModel {
         let head_dim = cfg.hidden_size / cfg.num_attention_heads;
         let rotary_emb = Qwen2_5VLTextRotaryEmbedding::new(head_dim, cfg.rope_theta);
         let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
+        let qwen2cfg = cfg.to_qwen2cfg();
         let vb_l = vb.pp("layers");
         for layer_idx in 0..cfg.num_hidden_layers {
-            let layer = Qwen2_5VLTextDecoderLayer::new(cfg, vb_l.pp(layer_idx))?;
+            let layer = Qwen2DecoderLayer::new(&qwen2cfg, vb_l.pp(layer_idx))?;
             layers.push(layer)
         }
         let norm = rms_norm(cfg.hidden_size, cfg.rms_norm_eps, vb.pp("norm"))?;

src/models/qwen3/generate.rs

@@ -42,7 +42,11 @@ impl<'a> Qwen3GenerateModel<'a> {
         let generation_config_path = path.to_string() + "/generation_config.json";
         let generation_config: Qwen3GenerationConfig =
             serde_json::from_slice(&std::fs::read(generation_config_path)?)?;
-
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("qwen3")
+            .to_string();
         Ok(Qwen3GenerateModel {
             chat_template,
             tokenizer,
@@ -51,7 +55,7 @@ impl<'a> Qwen3GenerateModel<'a> {
             eos_token_id1: generation_config.eos_token_id[0] as u32,
             eos_token_id2: generation_config.eos_token_id[1] as u32,
             generation_config,
-            model_name: "qwen3".to_string(),
+            model_name,
         })
     }
 }

src/models/qwen3_asr/generate.rs

@@ -57,7 +57,11 @@ impl<'a> Qwen3AsrGenerateModel<'a> {
         let model_list = find_type_files(path, "safetensors")?;
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, &device)? };
         let qwen3_asr = Qwen3ASRModel::new(vb, &cfg)?;
-
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("qwen3-asr")
+            .to_string();
         Ok(Self {
             chat_template,
             tokenizer,
@@ -68,7 +72,7 @@ impl<'a> Qwen3AsrGenerateModel<'a> {
             eos_token_id1: generation_config.eos_token_id[0] as u32,
             eos_token_id2: generation_config.eos_token_id[1] as u32,
             generation_config,
-            model_name: "qwen3-asr".to_string(),
+            model_name,
         })
     }
 }

src/models/qwen3vl/generate.rs

@@ -49,6 +49,11 @@ impl<'a> Qwen3VLGenerateModel<'a> {
         let generation_config_path = path.to_string() + "/generation_config.json";
         let generation_config: Qwen3GenerationConfig =
             serde_json::from_slice(&std::fs::read(generation_config_path)?)?;
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("qwen3vl")
+            .to_string();
         Ok(Self {
             chat_template,
             tokenizer,
@@ -58,7 +63,7 @@ impl<'a> Qwen3VLGenerateModel<'a> {
             eos_token_id1: generation_config.eos_token_id[0] as u32,
             eos_token_id2: generation_config.eos_token_id[1] as u32,
             generation_config,
-            model_name: "qwen3vl".to_string(),
+            model_name,
         })
     }
 }

src/models/rmbg2_0/generate.rs

@@ -41,6 +41,11 @@ impl RMBG2_0Model {
             Tensor::from_slice(&[0.485, 0.456, 0.406], (3, 1, 1), &device)?.to_dtype(dtype)?;
         let img_std =
             Tensor::from_slice(&[0.229, 0.224, 0.225], (3, 1, 1), &device)?.to_dtype(dtype)?;
+        let model_name = std::path::Path::new(path)
+            .file_name() 
+            .and_then(|s| s.to_str())
+            .unwrap_or("rmbg2.0")
+            .to_string();
         Ok(Self {
             model,
             h: 1024,
@@ -49,7 +54,7 @@ impl RMBG2_0Model {
             img_std,
             device,
             dtype,
-            model_name: "rmbg2.0".to_string(),
+            model_name,
         })
     }
 

src/models/voxcpm/generate.rs

@@ -62,11 +62,16 @@ impl VoxCPMGenerate {
                 sample_rate: 16000,
             },
         };
-        let model_name = if audio_config.sample_rate == 16000 {
+        let model_name = std::path::Path::new(path)
-            "VoxCPM".to_string()
+            .file_name() 
-        } else {
+            .and_then(|s| s.to_str())
-            "VoxCPM1.5".to_string()
+            .unwrap_or("VoxCPM")
-        };
+            .to_string();
+        // let model_name = if audio_config.sample_rate == 16000 {
+        //     "VoxCPM".to_string()
+        // } else {
+        //     "VoxCPM1.5".to_string()
+        // };
         let audio_vae = AudioVAE::new(
             vb_vae,
             audio_config.encoder_dim,