generate add penalty repeat

README.md

@@ -46,6 +46,10 @@ aha is a high-performance, cross-platform AI inference engine built with Rust an
 - **🧠 Attention Optimization** - Optional Flash Attention support for optimized long sequence processing
 
 ## Changelog
+### 2026-04-03
+- ChatCompletionParameters add repeat_penalty and repeat_last_n
+- generate add penalty repeat code
+
 ### 2026-04-02
 - refactor generate code 
 - \<think\>...\</think\> The content of the thought chain is returned using the reasoning_content field.
@@ -55,7 +59,7 @@ aha is a high-performance, cross-platform AI inference engine built with Rust an
 - refactor deepseek_ocr/fun_asr_nano generate code 
 
 ### 2026-03-31
-- add server adn cli mod
+- add server and cli mod
 - aha model name use modelscope id replace 
 - update WhichModel 
 - Usage add time info

README.zh-CN.md

@@ -46,6 +46,10 @@ aha 是一款基于 Rust 和 Candle 框架构建的高性能跨平台 AI 推理
 
 ## 更新日志
 ## Changelog
+### 2026-04-03
+- ChatCompletionParameters 新增 repeat_penalty 和 repeat_last_n 参数
+- 生成添加重复惩罚代码
+
 ### 2026-04-02
 - 重构生成代码 
 - \<think\>...\</think\> 思维链内容使用reasoning_content字段返回。

docs/changelog.md

@@ -5,6 +5,10 @@ All notable changes to aha will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+### 2026-04-03
+- ChatCompletionParameters add repeat_penalty and repeat_last_n
+- generate add penalty repeat code
+
 ### 2026-04-02
 - refactor generate code 
 - \<think\>...\</think\> The content of the thought chain is returned using the reasoning_content field.
@@ -14,7 +18,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - refactor deepseek_ocr/fun_asr_nano generate code 
 
 ### 2026-03-31
-- add server adn cli mod
+- add server and cli mod
 - aha model name use modelscope id replace 
 - update WhichModel enum 
 - Usage add time info

docs/changelog.zh-CN.md

@@ -5,6 +5,10 @@
 格式基于 [Keep a Changelog](https://keepachangelog.com/zh-CN/1.0.0/)，
 本项目遵循 [语义化版本](https://semver.org/lang/zh-CN/spec/v2.0.0.html)。
 
+### 2026-04-03
+- ChatCompletionParameters 新增 repeat_penalty 和 repeat_last_n 参数
+- 生成添加重复惩罚代码
+
 ### 2026-04-02
 - 重构生成代码 
 - \<think\>...\</think\> 思维链内容使用reasoning_content字段返回。

docs/installation.md

@@ -290,7 +290,8 @@ LiquidAI/LFM2-VL-1.6B                    LiquidAI             vlm          ✔
 OpenBMB/MiniCPM4-0.5B                    OpenBMB              llm          ✔       
 Qwen/Qwen2.5-VL-3B-Instruct              Qwen                 vlm          ✔       
 Qwen/Qwen2.5-VL-7B-Instruct              Qwen                 vlm                  
-Qwen/Qwen3-0.6B                          Qwen                 llm          ✔       
+Qwen/Qwen3-0.6B                          Qwen                 llm          ✔  
+Qwen/Qwen3-1.7B                          Qwen                 llm          ✔     
 Qwen/Qwen3.5-0.8B                        Qwen                 vlm          ✔       
 Qwen/Qwen3.5-2B                          Qwen                 vlm                  
 Qwen/Qwen3.5-4B                          Qwen                 vlm                  

docs/installation.zh-CN.md

@@ -290,7 +290,8 @@ LiquidAI/LFM2-VL-1.6B                    LiquidAI             vlm          ✔
 OpenBMB/MiniCPM4-0.5B                    OpenBMB              llm          ✔       
 Qwen/Qwen2.5-VL-3B-Instruct              Qwen                 vlm          ✔       
 Qwen/Qwen2.5-VL-7B-Instruct              Qwen                 vlm                  
-Qwen/Qwen3-0.6B                          Qwen                 llm          ✔       
+Qwen/Qwen3-0.6B                          Qwen                 llm          ✔ 
+Qwen/Qwen3-1.7B                          Qwen                 llm          ✔      
 Qwen/Qwen3.5-0.8B                        Qwen                 vlm          ✔       
 Qwen/Qwen3.5-2B                          Qwen                 vlm                  
 Qwen/Qwen3.5-4B                          Qwen                 vlm                  

docs/supported-models.md

@@ -14,7 +14,8 @@ LiquidAI/LFM2-VL-1.6B                    LiquidAI             vlm          ✔
 OpenBMB/MiniCPM4-0.5B                    OpenBMB              llm          ✔       
 Qwen/Qwen2.5-VL-3B-Instruct              Qwen                 vlm          ✔       
 Qwen/Qwen2.5-VL-7B-Instruct              Qwen                 vlm                  
-Qwen/Qwen3-0.6B                          Qwen                 llm          ✔       
+Qwen/Qwen3-0.6B                          Qwen                 llm          ✔
+Qwen/Qwen3-1.7B                          Qwen                 llm          ✔       
 Qwen/Qwen3.5-0.8B                        Qwen                 vlm          ✔       
 Qwen/Qwen3.5-2B                          Qwen                 vlm                  
 Qwen/Qwen3.5-4B                          Qwen                 vlm                  
@@ -43,7 +44,7 @@ ZhipuAI/GLM-OCR                          ZhipuAI              ocr          ✔
 
 | Model | Parameters | Model Id | License |
 |-------|-----------|--------|---------|
-| **Qwen3-0.6B** | 0.6B | Qwen/Qwen3-0.6B | [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
+| **Qwen3-0.6B** | 0.6B | Qwen/Qwen3-0.6B <br> Qwen/Qwen3-1.7B | [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
 | **MiniCPM4-0.5B** | 0.5B | OpenBMB/MiniCPM4-0.5B | [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
 | **LFM2-1.2B** | 1.2B | LiquidAI/LFM2-1.2B | [lfm1.0](https://huggingface.co/LiquidAI/LFM2-1.2B/blob/main/LICENSE) |
 | **LFM2.5-1.2B-Instruct** | 1.2B | LiquidAI/LFM2.5-1.2B-Instruct | [lfm1.0](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct/blob/main/LICENSE) |

docs/supported-models.zh-CN.md

@@ -14,7 +14,8 @@ LiquidAI/LFM2-VL-1.6B                    LiquidAI             vlm          ✔
 OpenBMB/MiniCPM4-0.5B                    OpenBMB              llm          ✔       
 Qwen/Qwen2.5-VL-3B-Instruct              Qwen                 vlm          ✔       
 Qwen/Qwen2.5-VL-7B-Instruct              Qwen                 vlm                  
-Qwen/Qwen3-0.6B                          Qwen                 llm          ✔       
+Qwen/Qwen3-0.6B                          Qwen                 llm          ✔
+Qwen/Qwen3-1.7B                          Qwen                 llm          ✔       
 Qwen/Qwen3.5-0.8B                        Qwen                 vlm          ✔       
 Qwen/Qwen3.5-2B                          Qwen                 vlm                  
 Qwen/Qwen3.5-4B                          Qwen                 vlm                  
@@ -43,7 +44,7 @@ ZhipuAI/GLM-OCR                          ZhipuAI              ocr          ✔
 
 | 模型 | 参数量 | 模型id | 开源协议 |
 |------|--------|------|---------|
-| **Qwen3-0.6B** | 0.6B | Qwen/Qwen3-0.6B | [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
+| **Qwen3-0.6B** | 0.6B | Qwen/Qwen3-0.6B <br> Qwen/Qwen3-1.7B | [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
 | **MiniCPM4-0.5B** | 0.5B | OpenBMB/MiniCPM4-0.5B | [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md) |
 | **LFM2-1.2B** | 1.2B | LiquidAI/LFM2-1.2B | [lfm1.0](https://huggingface.co/LiquidAI/LFM2-1.2B/blob/main/LICENSE) |
 | **LFM2.5-1.2B-Instruct** | 1.2B | LiquidAI/LFM2.5-1.2B-Instruct | [lfm1.0](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct/blob/main/LICENSE) |

src/cli/mod.rs

@@ -240,6 +240,9 @@ pub(crate) fn run_run(args: RunArgs) -> anyhow::Result<()> {
         WhichModel::Qwen3_0_6B => {
             qwen3::Qwen3Exec::run(&input, output.as_deref(), &weight_path)?;
         }
+        WhichModel::Qwen3_1_7B => {
+            qwen3::Qwen3Exec::run(&input, output.as_deref(), &weight_path)?;
+        }
         WhichModel::Qwen3_5_0_8B => {
             qwen3_5::Qwen3_5Exec::run(&input, output.as_deref(), &weight_path)?;
         }

src/models/common/generate.rs

@@ -49,6 +49,8 @@ pub fn get_logit_processor(
 
 pub struct GenerationContext {
     pub logit_processor: LogitsProcessor,
+    pub repeat_penalty: f32,
+    pub repeat_last_n: usize,
     pub seqlen_offset: usize,
     pub seq_len: usize,
     pub sample_len: u32,
@@ -60,6 +62,8 @@ impl GenerationContext {
         temperature: Option<f32>,
         top_p: Option<f32>,
         top_k: Option<usize>,
+        repeat_penalty: Option<f32>,
+        repeat_last_n: Option<usize>,
         seed: u64,
         initial_seq_len: usize,
         max_tokens: u32,
@@ -67,6 +71,8 @@ impl GenerationContext {
     ) -> Self {
         Self {
             logit_processor: get_logit_processor(temperature, top_p, top_k, seed),
+            repeat_penalty: repeat_penalty.unwrap_or(1.0),
+            repeat_last_n: repeat_last_n.unwrap_or(64),
             seqlen_offset: 0,
             seq_len: initial_seq_len,
             sample_len: max_tokens,
@@ -91,27 +97,27 @@ impl GenerationContext {
 
 /// 采样辅助函数
 fn sample_and_push(
-    processor: &mut LogitsProcessor,
+    ctx: &mut GenerationContext,
     logits: &Tensor,
     generated: &mut Vec<u32>,
 ) -> Result<u32> {
     let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
-    let token = processor.sample(&logits)?;
+    // 重复惩罚
+    let logits = if ctx.repeat_penalty == 1. || ctx.repeat_last_n == 0 {
+        logits
+    } else {
+        let start_at = generated.len().saturating_sub(ctx.repeat_last_n);
+        candle_transformers::utils::apply_repeat_penalty(
+            &logits,
+            ctx.repeat_penalty,
+            &generated[start_at..],
+        )?
+    };
+    let token = ctx.logit_processor.sample(&logits)?;
     generated.push(token);
     Ok(token)
 }
 
-// TODO
-// let logits = if self.repeat_penalty == 1. {
-//             logits
-//         } else {
-//             let start_at = generate.len().saturating_sub(self.repeat_last_n);
-//             candle_transformers::utils::apply_repeat_penalty(
-//                 &logits,
-//                 self.repeat_penalty,
-//                 &generate[start_at..],
-//             )?
-//         };
 pub fn generate_generic<M: InferenceModel>(
     model: &mut M,
     tokenizer: &TokenizerModel,
@@ -125,7 +131,7 @@ pub fn generate_generic<M: InferenceModel>(
     let eos_ids = model.stop_token_ids();
     let i_start = Instant::now();
     let logits = model.forward_initial(&input_ids, ctx.seqlen_offset, data)?;
-    let next_token = sample_and_push(&mut ctx.logit_processor, &logits, &mut generated)?;
+    let next_token = sample_and_push(ctx, &logits, &mut generated)?;
     let i_duration = i_start.elapsed();
     let prompt_secs = i_duration.as_secs_f64();
     let mut input_ids = ctx.prepare_for_next_token(next_token)?;
@@ -134,12 +140,11 @@ pub fn generate_generic<M: InferenceModel>(
     let i_start = Instant::now();
     for _ in 1..ctx.sample_len {
         let logits = model.forward_step(&input_ids, ctx.seqlen_offset)?;
-        let next_token = sample_and_push(&mut ctx.logit_processor, &logits, &mut generated)?;
+        let next_token = sample_and_push(ctx, &logits, &mut generated)?;
 
         if eos_ids.contains(&next_token) {
             break;
         }
-
         input_ids = ctx.prepare_for_next_token(next_token)?;
     }
     let i_duration = i_start.elapsed();
@@ -167,6 +172,8 @@ pub fn generate_stream_generic<M: InferenceModel>(
     temperature: Option<f32>,
     top_p: Option<f32>,
     top_k: Option<usize>,
+    repeat_penalty: Option<f32>,
+    repeat_last_n: Option<usize>,
     seed: u64,
     max_tokens: u32,
     in_reasoning: bool,
@@ -177,6 +184,8 @@ pub fn generate_stream_generic<M: InferenceModel>(
         temperature,
         top_p,
         top_k,
+        repeat_penalty,
+        repeat_last_n,
         seed,
         input_ids.dim(1)?,
         max_tokens,
@@ -193,7 +202,7 @@ pub fn generate_stream_generic<M: InferenceModel>(
         let mut tool_call_id = None;
         let mut tool_call_content = String::new();
         let mut in_reasoning = in_reasoning;
-        // 处理 unicode 错误累积
+        let mut generated = Vec::new();
         for _ in 0..ctx.sample_len {
             let i_start = Instant::now();
             let logits = if ctx.seqlen_offset == 0 {
@@ -202,11 +211,7 @@ pub fn generate_stream_generic<M: InferenceModel>(
             } else {
                 model.forward_step(&input_ids, ctx.seqlen_offset)
             }?;
-
+            let next_token = sample_and_push(&mut ctx, &logits, &mut generated)?;
-            let next_token = {
-                let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
-                ctx.logit_processor.sample(&logits)?
-            };
             completion_tokens += 1;
             let i_duration = i_start.elapsed();
             if ctx.seqlen_offset == 0 {

src/models/common/model_mapping.rs

@@ -18,6 +18,8 @@ pub enum WhichModel {
     Qwen2_5VL7B,
     #[value(name = "Qwen/Qwen3-0.6B")]
     Qwen3_0_6B,
+    #[value(name = "Qwen/Qwen3-1.7B")]
+    Qwen3_1_7B,
     #[value(name = "Qwen/Qwen3.5-0.8B")]
     Qwen3_5_0_8B,
     #[value(name = "Qwen/Qwen3.5-2B")]
@@ -93,6 +95,7 @@ impl WhichModel {
             // LLM models
             WhichModel::MiniCPM4_0_5B
             | WhichModel::Qwen3_0_6B
+            | WhichModel::Qwen3_1_7B
             | WhichModel::LFM2_1_2B
             | WhichModel::LFM2_5_1_2BInstruct => "llm",
             // VLM models

src/models/deepseek_ocr/generate.rs

@@ -98,6 +98,8 @@ impl GenerateModel for DeepseekOCRGenerateModel {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             mes.seed.unwrap_or(34562) as u64,
             input_ids.dim(1)?,
             max_tokens,
@@ -169,6 +171,8 @@ impl GenerateModel for DeepseekOCRGenerateModel {
             temperature,
             top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             max_tokens,
             false,

src/models/fun_asr_nano/generate.rs

@@ -109,6 +109,8 @@ impl GenerateModel for FunAsrNanoGenerateModel {
             temperature.into(),
             top_p.into(),
             top_k.into(),
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             max_tokens,
@@ -145,8 +147,6 @@ impl GenerateModel for FunAsrNanoGenerateModel {
         let top_k = self.generation_config.top_k;
         let seed = mes.seed.unwrap_or(34562) as u64;
         let max_tokens = mes.max_tokens.unwrap_or(1024);
-        // let mut logit_processor =
-        //     get_logit_processor(Some(temperature), Some(top_p), Some(top_k), seed);
         let (speech, fbank_mask, input_ids) = self.processor.process_info(&mes, &self.tokenizer)?;
         let speech = speech.to_dtype(self.dtype)?;
         let data_vec = vec![speech.into(), fbank_mask.into()];
@@ -159,6 +159,8 @@ impl GenerateModel for FunAsrNanoGenerateModel {
             temperature.into(),
             top_p.into(),
             top_k.into(),
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             max_tokens,
             false,

src/models/glm_asr_nano/generate.rs

@@ -77,6 +77,8 @@ impl<'a> GenerateModel for GlmAsrNanoGenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             mes.top_k,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -124,6 +126,8 @@ impl<'a> GenerateModel for GlmAsrNanoGenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             sample_len,
             false,

src/models/glm_ocr/generate.rs

@@ -113,6 +113,8 @@ impl GenerateModel for GlmOcrGenerateModel {
             mes.temperature,
             mes.top_p,
             mes.top_k,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -187,6 +189,8 @@ impl GenerateModel for GlmOcrGenerateModel {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             sample_len,
             false,

src/models/hunyuan_ocr/generate.rs

@@ -87,6 +87,8 @@ impl<'a> GenerateModel for HunyuanOCRGenerateModel<'a> {
             temperature.into(),
             top_p.into(),
             top_k.into(),
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -149,6 +151,8 @@ impl<'a> GenerateModel for HunyuanOCRGenerateModel<'a> {
             temperature.into(),
             top_p.into(),
             top_k.into(),
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             sample_len,
             false,

src/models/lfm2/generate.rs

@@ -72,6 +72,8 @@ impl<'a> GenerateModel for Lfm2GenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -114,6 +116,8 @@ impl<'a> GenerateModel for Lfm2GenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             sample_len,
             false,

src/models/lfm2vl/generate.rs

@@ -72,6 +72,8 @@ impl<'a> GenerateModel for Lfm2VLGenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -126,6 +128,8 @@ impl<'a> GenerateModel for Lfm2VLGenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             sample_len,
             false,

src/models/minicpm4/generate.rs

@@ -64,6 +64,8 @@ impl<'a> GenerateModel for MiniCPMGenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -104,6 +106,8 @@ impl<'a> GenerateModel for MiniCPMGenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             sample_len,
             false,

src/models/mod.rs

@@ -168,6 +168,10 @@ pub fn load_model<'a>(
             let model = Qwen3GenerateModel::init(path, None, None)?;
             ModelInstance::Qwen3(model)
         }
+        WhichModel::Qwen3_1_7B => {
+            let model = Qwen3GenerateModel::init(path, None, None)?;
+            ModelInstance::Qwen3(model)
+        }
         WhichModel::Qwen3_5_0_8B => {
             let model = Qwen3_5GenerateModel::init(path, None, None)?;
             ModelInstance::Qwen3_5(model)

src/models/paddleocr_vl/generate.rs

@@ -79,6 +79,8 @@ impl<'a> GenerateModel for PaddleOCRVLGenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -141,6 +143,8 @@ impl<'a> GenerateModel for PaddleOCRVLGenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             sample_len,
             false,

src/models/qwen3/generate.rs

@@ -71,6 +71,8 @@ impl<'a> GenerateModel for Qwen3GenerateModel<'a> {
             temperature.into(),
             top_p.into(),
             top_k.into(),
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -117,6 +119,8 @@ impl<'a> GenerateModel for Qwen3GenerateModel<'a> {
             temperature.into(),
             top_p.into(),
             top_k.into(),
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             sample_len,
             in_reasoning,

src/models/qwen3_5/generate.rs

@@ -29,8 +29,8 @@ pub struct Qwen3_5GenerateModel<'a> {
     qwen3_5: Qwen3_5Model,
     device: Device,
     model_name: String,
-    // repeat_penalty: f32, // TODO
+    repeat_penalty: f32,
-    // repeat_last_n: usize,
+    repeat_last_n: usize,
 }
 
 impl<'a> Qwen3_5GenerateModel<'a> {
@@ -59,8 +59,8 @@ impl<'a> Qwen3_5GenerateModel<'a> {
             qwen3_5,
             device,
             model_name: model_name.to_string(),
-            // repeat_penalty: 1.01,
+            repeat_penalty: 1.01,
-            // repeat_last_n: 64,
+            repeat_last_n: 64,
         })
     }
 
@@ -116,8 +116,8 @@ impl<'a> Qwen3_5GenerateModel<'a> {
             qwen3_5,
             device,
             model_name: stem.to_string(),
-            // repeat_penalty: 1.1,
+            repeat_penalty: 1.1,
-            // repeat_last_n: 64,
+            repeat_last_n: 64,
         })
     }
 }
@@ -147,6 +147,8 @@ impl<'a> GenerateModel for Qwen3_5GenerateModel<'a> {
             temperature.into(),
             top_p.into(),
             Some(20),
+            self.repeat_penalty.into(),
+            self.repeat_last_n.into(),
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -213,6 +215,8 @@ impl<'a> GenerateModel for Qwen3_5GenerateModel<'a> {
             mes.temperature,
             mes.top_p,
             None,
+            self.repeat_penalty.into(),
+            self.repeat_last_n.into(),
             seed,
             sample_len,
             in_reasoning,

src/models/qwen3vl/generate.rs

@@ -84,6 +84,8 @@ impl<'a> GenerateModel for Qwen3VLGenerateModel<'a> {
             temperature.into(),
             top_p.into(),
             top_k.into(),
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             input_ids.dim(1)?,
             sample_len,
@@ -150,6 +152,8 @@ impl<'a> GenerateModel for Qwen3VLGenerateModel<'a> {
             temperature.into(),
             top_p.into(),
             top_k.into(),
+            mes.repeat_penalty,
+            mes.repeat_last_n,
             seed,
             sample_len,
             in_reasoning,
@@ -157,108 +161,5 @@ impl<'a> GenerateModel for Qwen3VLGenerateModel<'a> {
             &self.model_name,
         )?;
         Ok(Box::new(Box::pin(stream)))
-        // let sample_len = mes.max_tokens.unwrap_or(1024);
-        // let stream = stream! {
-        //     let mut error_tokens = Vec::new();
-        //     let mut pixel_values = input.pixel_values.as_ref();
-        //     let image_grid_thw = input.image_grid_thw.as_ref();
-        //     let mut pixel_values_video = input.pixel_values_video.as_ref();
-        //     let video_grid_thw = input.video_grid_thw.as_ref();
-        //     let mut tool_call_id = None;
-        //     let mut tool_call_content = String::new();
-        //     for _ in 0..sample_len {
-        //         let logits = self.qwen3_vl.forward(
-        //             &input_ids,
-        //             pixel_values,
-        //             image_grid_thw,
-        //             pixel_values_video,
-        //             video_grid_thw,
-        //             Some(&cache_position),
-        //             seqlen_offset,
-        //         )?;
-        //         let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
-        //         let next_token = logit_processor.sample(&logits)?;
-        //         let mut decode_ids = Vec::new();
-        //         if !error_tokens.is_empty() {
-        //             decode_ids.extend_from_slice(&error_tokens);
-        //         }
-        //         decode_ids.push(next_token);
-        //         let decoded_token = self.tokenizer.token_decode(decode_ids).map_err(|e| anyhow!(format!("stream decode error{e}")))?;
-        //         if decoded_token.contains("�") {
-        //             error_tokens.push(next_token);
-        //             if error_tokens.len() > 3 {
-        //                 error_tokens.clear();
-        //             }
-        //             seqlen_offset += seq_len;
-        //             seq_len = 1;
-        //             input_ids = Tensor::from_vec(vec![next_token], (1, 1), &self.device)?;
-        //             cache_position = Tensor::from_vec(vec![seqlen_offset as u32], 1, &self.device)?;
-        //             pixel_values = None;
-        //             pixel_values_video = None;
-        //             continue;
-        //         }
-        //         error_tokens.clear();
-
-        //         // 处理特殊标记和工具调用
-        //         match decoded_token.as_str() {
-        //             "<tool_call>" => {
-        //                 // 开始工具调用
-        //                 tool_call_id = Some(uuid::Uuid::new_v4().to_string());
-        //                 seqlen_offset += seq_len;
-        //                 seq_len = 1;
-        //                 input_ids = Tensor::from_vec(vec![next_token], (1, 1), &self.device)?;
-        //                 cache_position = Tensor::from_vec(vec![seqlen_offset as u32], 1, &self.device)?;
-        //                 pixel_values = None;
-        //                 pixel_values_video = None;
-        //                 continue;
-        //             }
-        //             "</tool_call>" => {
-        //                 // 结束工具调用
-        //                 let chunk = build_completion_chunk_response(
-        //                     decoded_token,
-        //                     &self.model_name,
-        //                     tool_call_id.clone(),
-        //                     Some(tool_call_content.clone())
-        //                 );
-        //                 tool_call_id = None;
-        //                 tool_call_content = String::new();
-        //                 yield Ok(chunk);
-        //             }
-        //             _ => {
-        //                 if tool_call_id.is_some() {
-        //                     // 在工具调用过程中，收集工具调用内容
-        //                     tool_call_content.push_str(&decoded_token);
-        //                     seqlen_offset += seq_len;
-        //                     seq_len = 1;
-        //                     input_ids = Tensor::from_vec(vec![next_token], (1, 1), &self.device)?;
-        //                     cache_position = Tensor::from_vec(vec![seqlen_offset as u32], 1, &self.device)?;
-        //                     pixel_values = None;
-        //                     pixel_values_video = None;
-        //                     continue;
-        //                 } else {
-        //                     // 正常文本输出
-        //                     let chunk = build_completion_chunk_response(
-        //                         decoded_token,
-        //                         &self.model_name,
-        //                         None,
-        //                         None
-        //                     );
-        //                     yield Ok(chunk);
-        //                 }
-        //             }
-        //         }
-        //         if next_token == self.eos_token_id1 || next_token == self.eos_token_id2 {
-        //             break;
-        //         }
-        //         seqlen_offset += seq_len;
-        //         seq_len = 1;
-        //         input_ids = Tensor::from_vec(vec![next_token], (1, 1), &self.device)?;
-        //         cache_position = Tensor::from_vec(vec![seqlen_offset as u32], 1, &self.device)?;
-        //         pixel_values = None;
-        //         pixel_values_video = None;
-        //     }
-        //     self.qwen3_vl.clear_kv_cache();
-        // };
-        // Ok(Box::new(Box::pin(stream)))
     }
 }

src/params/chat.rs

@@ -142,6 +142,16 @@ pub struct ChatCompletionParameters {
     pub top_p: Option<f32>,
     #[serde(skip_serializing_if = "Option::is_none")]
     pub top_k: Option<usize>,
+    /// Penalize repetition of token sequences, helping to prevent the model from getting stuck in loops.
+    /// Values > 1.0 penalize repetitions, values < 1.0 encourage them.
+    /// For example, a value of 1.2 means that tokens that have already appeared will be less likely to appear again.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub repeat_penalty: Option<f32>,
+    /// The number of previous tokens to consider when applying repeat penalty.
+    /// Only the last N tokens in the conversation history will be used to calculate the penalty.
+    /// A larger value considers more history for penalizing repetitions, while 0 applies no penalty.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub repeat_last_n: Option<usize>,
     /// A list of tools the model may call. Currently, only functions are supported as a tool.
     /// Use this to provide a list of functions the model may generate JSON inputs for.
     #[serde(skip_serializing_if = "Option::is_none")]

tests/test_glm_ocr.rs

@@ -28,7 +28,9 @@ fn glm_ocr_generate() -> Result<()> {
                     }
                 ]
             }
-        ]
+        ],
+        "repeat_penalty": 1.2,
+        "repeat_last_n": 64
     }
     "#;
     let save_dir =