update readme and fmt

README.md

@@ -112,9 +112,13 @@ cargo run -F cuda -- [参数]
     -m, --model <MODEL>
 * 指定要加载的模型类型
 * 可选值：
-    * minicpm4-0.5b：MiniCPM4-0.5B 模型
-    * qwen2.5vl-3b：Qwen2.5-VL-3B 模型
-    * qwen3vl-2b：Qwen3-VL-2B 模型
+    * minicpm4-0.5b：OpenBMB/MiniCPM4-0.5B 模型
+    * qwen2.5vl-3b：Qwen/Qwen2.5-VL-3B-Instruct 模型
+    * qwen2.5vl-7b：Qwen/Qwen2.5-VL-7B-Instruct 模型
+    * qwen3vl-2b：Qwen/Qwen3-VL-2B-Instruct 模型
+    * qwen3vl-4b：Qwen/Qwen3-VL-4B-Instruct 模型
+    * qwen3vl-8b：Qwen/Qwen3-VL-8B-Instruct 模型
+    * qwen3vl-32b：Qwen/Qwen3-VL-32B-Instruct 模型
 * 示例：--model minicpm4-0.5b 或 -m qwen3vl-2b
 
 3. 权重路径

src/main.rs

@@ -77,12 +77,12 @@ async fn main() -> anyhow::Result<()> {
     let args = Args::parse();
     let model_id = match args.model {
         WhichModel::MiniCPM4_0_5B => "OpenBMB/MiniCPM4-0.5B",
-        WhichModel::Qwen2_5vl3B => "Qwen/Qwen2.5-VL-3B-Instruct", 
+        WhichModel::Qwen2_5vl3B => "Qwen/Qwen2.5-VL-3B-Instruct",
         WhichModel::Qwen2_5vl7B => "Qwen/Qwen2.5-VL-7B-Instruct",
-        WhichModel::Qwen3vl2B => "Qwen/Qwen3-VL-2B-Instruct", 
-        WhichModel::Qwen3vl4B => "Qwen/Qwen3-VL-4B-Instruct", 
-        WhichModel::Qwen3vl8B => "Qwen/Qwen3-VL-8B-Instruct", 
-        WhichModel::Qwen3vl32B => "Qwen/Qwen3-VL-32B-Instruct"
+        WhichModel::Qwen3vl2B => "Qwen/Qwen3-VL-2B-Instruct",
+        WhichModel::Qwen3vl4B => "Qwen/Qwen3-VL-4B-Instruct",
+        WhichModel::Qwen3vl8B => "Qwen/Qwen3-VL-8B-Instruct",
+        WhichModel::Qwen3vl32B => "Qwen/Qwen3-VL-32B-Instruct",
     };
     let model_path = match args.weight_path {
         Some(path) => path,

src/models/mod.rs

@@ -99,15 +99,15 @@ pub fn load_model(model_type: WhichModel, path: &str) -> Result<ModelInstance<'_
         WhichModel::Qwen3vl2B => {
             let model = Qwen3VLGenerateModel::init(path, None, None)?;
             ModelInstance::Qwen3VL(model)
-        } 
+        }
         WhichModel::Qwen3vl4B => {
             let model = Qwen3VLGenerateModel::init(path, None, None)?;
             ModelInstance::Qwen3VL(model)
-        } 
+        }
         WhichModel::Qwen3vl8B => {
             let model = Qwen3VLGenerateModel::init(path, None, None)?;
             ModelInstance::Qwen3VL(model)
-        } 
+        }
         WhichModel::Qwen3vl32B => {
             let model = Qwen3VLGenerateModel::init(path, None, None)?;
             ModelInstance::Qwen3VL(model)

src/models/qwen3vl/model.rs

@@ -559,7 +559,6 @@ pub struct Qwen3VLTextAttention {
     num_key_value_heads: usize,
     num_kv_groups: usize,
     head_dim: usize,
-    hidden_size: usize,
     scaling: f64,
     kv_cache: Option<(Tensor, Tensor)>,
 }
@@ -585,7 +584,8 @@ impl Qwen3VLTextAttention {
                 linear_no_bias(hidden_size, num_key_value_heads * head_dim, vb.pp("k_proj"))?;
             let v_proj =
                 linear_no_bias(hidden_size, num_key_value_heads * head_dim, vb.pp("v_proj"))?;
-            let o_proj = linear_no_bias(num_attention_heads * head_dim, hidden_size, vb.pp("o_proj"))?;
+            let o_proj =
+                linear_no_bias(num_attention_heads * head_dim, hidden_size, vb.pp("o_proj"))?;
             (q_proj, k_proj, v_proj, o_proj)
         };
         let q_norm = rms_norm(head_dim, config.rms_norm_eps, vb.pp("q_norm"))?;
@@ -601,7 +601,6 @@ impl Qwen3VLTextAttention {
             num_key_value_heads,
             num_kv_groups,
             head_dim,
-            hidden_size,
             scaling,
             kv_cache: None,
         })
@@ -652,7 +651,8 @@ impl Qwen3VLTextAttention {
             attention_mask,
             self.scaling,
         )?;
-        let attn_output = attn_output.reshape((b_sz, q_len, self.num_attention_heads*self.head_dim))?;
+        let attn_output =
+            attn_output.reshape((b_sz, q_len, self.num_attention_heads * self.head_dim))?;
         let attn_output = attn_output.apply(&self.o_proj)?;
         Ok(attn_output)
     }

src/utils/mod.rs

@@ -21,7 +21,7 @@ pub fn get_device(device: Option<&Device>) -> Device {
         None => {
             #[cfg(feature = "cuda")]
             {
-                Device::new_cuda(6).unwrap_or(Device::Cpu)
+                Device::new_cuda(0).unwrap_or(Device::Cpu)
             }
             #[cfg(not(feature = "cuda"))]
             {

src/utils/tensor_utils.rs

@@ -15,7 +15,10 @@ pub fn prepare_causal_attention_mask(
     let arange = Tensor::arange(0u32, tgt_len as u32, device)?;
     let arange = arange.unsqueeze(1)?.broadcast_as((tgt_len, tgt_len))?;
     let upper_triangle = arange.t()?.gt(&arange)?;
-    let mask = upper_triangle.where_cond(&Tensor::new(f32::NEG_INFINITY, device)?.broadcast_as(arange.shape())?, &Tensor::new(0f32, device)?.broadcast_as(arange.shape())?)?;
+    let mask = upper_triangle.where_cond(
+        &Tensor::new(f32::NEG_INFINITY, device)?.broadcast_as(arange.shape())?,
+        &Tensor::new(0f32, device)?.broadcast_as(arange.shape())?,
+    )?;
 
     let mask = if seqlen_offset > 0 {
         let mask0 = Tensor::zeros((tgt_len, seqlen_offset), DType::F32, device)?;