update quantization doc

Author: chang-wenbin

docs/quantization/online_quantization.md

@@ -22,7 +22,7 @@ python -m fastdeploy.entrypoints.openai.api_server \
        --max-num-seqs 32
 ```
 
-- By specifying `--model baidu/ERNIE-4.5-300B-A47B-Paddle`, the model can be automatically downloaded from AIStudio. FastDeploy depends on Paddle format models. For more information, please refer to [Supported Model List](https://console.cloud.baidu-int.com/devops/icode/repos/baidu/paddle_internal/FastDeploy/blob/feature%2Finference-refactor-20250528/docs/supported_models.md).
+- By specifying `--model baidu/ERNIE-4.5-300B-A47B-Paddle`, the model can be automatically downloaded from AIStudio. FastDeploy depends on Paddle format models. For more information, please refer to [Supported Model List](../supported_models.md).
 - By setting `--quantization` to `wint8` or `wint4`, online INT8/INT4 quantization can be selected.
 - Deploying ERNIE-4.5-300B-A47B-Paddle WINT8 requires at least 80G * 8 cards, while WINT4 requires 80GB * 4 cards.
 - For more deployment tutorials, please refer to [get_started](../get_started/ernie-4.5.md).
@@ -48,7 +48,7 @@ python -m fastdeploy.entrypoints.openai.api_server \
        --max-num-seqs 32
 ```
 
-- By specifying `--model baidu/ERNIE-4.5-300B-A47B-Paddle`, the model can be automatically downloaded from AIStudio. FastDeploy depends on Paddle format models. For more information, please refer to [Supported Model List](https://console.cloud.baidu-int.com/devops/icode/repos/baidu/paddle_internal/FastDeploy/blob/feature%2Finference-refactor-20250528/docs/supported_models.md).
+- By specifying `--model baidu/ERNIE-4.5-300B-A47B-Paddle`, the model can be automatically downloaded from AIStudio. FastDeploy depends on Paddle format models. For more information, please refer to [Supported Model List](../supported_models.md).
 - By setting `--quantization` to `block_wise_fp8`, online Block-wise FP8 quantization can be selected.
 - Deploying ERNIE-4.5-300B-A47B-Paddle Block-wise FP8 requires at least 80G * 8 cards.
 - For more deployment tutorials, please refer to [get_started](../get_started/ernie-4.5.md) 

docs/quantization/wint2.md

@@ -46,7 +46,7 @@ Example of quantization configuration in the model's config.json file:
 ```
 
 - For more deployment tutorials, please refer to [get_started](../get_started/ernie-4.5.md);
-- For more model descriptions, please refer to [Supported Model List](https://console.cloud.baidu-int.com/devops/icode/repos/baidu/paddle_internal/FastDeploy/blob/feature%2Finference-refactor-20250528/docs/supported_models.md).
+- For more model descriptions, please refer to [Supported Model List](../supported_models.md).
 
 ## WINT2 Performance
 

docs/zh/quantization/online_quantization.md

@@ -22,7 +22,7 @@ python -m fastdeploy.entrypoints.openai.api_server \
        --max-num-seqs 32
 ```
 
-- 通过指定 `--model baidu/ERNIE-4.5-300B-A47B-Paddle` 可自动从AIStudio下载模型。FastDeploy依赖Paddle格式的模型，更多说明参考[支持模型列表](https://console.cloud.baidu-int.com/devops/icode/repos/baidu/paddle_internal/FastDeploy/blob/feature%2Finference-refactor-20250528/docs/supported_models.md)。
+- 通过指定 `--model baidu/ERNIE-4.5-300B-A47B-Paddle` 可自动从AIStudio下载模型。FastDeploy依赖Paddle格式的模型，更多说明参考[支持模型列表](../supported_models.md)。
 - 通过设置 `--quantization` 为 `wint8` 或 `wint4` 选择在线 INT8/INT4 量化。 
 - 部署 ERNIE-4.5-300B-A47B-Paddle WINT8 最少需要 80G * 8卡, WINT4 则需要 80GB * 4卡。
 - 更多部署教程请参考[get_started](../get_started/ernie-4.5.md).
@@ -48,7 +48,7 @@ python -m fastdeploy.entrypoints.openai.api_server \
        --max-num-seqs 32
 ```
 
-- 通过指定 `--model baidu/ERNIE-4.5-300B-A47B-Paddle` 可自动从AIStudio下载模型。FastDeploy依赖Paddle格式的模型，更多说明参考[支持模型列表](https://console.cloud.baidu-int.com/devops/icode/repos/baidu/paddle_internal/FastDeploy/blob/feature%2Finference-refactor-20250528/docs/supported_models.md)。
+- 通过指定 `--model baidu/ERNIE-4.5-300B-A47B-Paddle` 可自动从AIStudio下载模型。FastDeploy依赖Paddle格式的模型，更多说明参考[支持模型列表](../supported_models.md)。
 - 通过设置 `--quantization` 为 `block_wise_fp8` 选择在线 Block-wise FP8 量化。 
 - 部署 ERNIE-4.5-300B-A47B-Paddle Block-wise FP8 最少需要 80G * 8卡。
 - 更多部署教程请参考[get_started](../get_started/ernie-4.5.md)

docs/zh/quantization/wint2.md

@@ -46,7 +46,7 @@ python -m fastdeploy.entrypoints.openai.api_server \
 ```
 
 - 更多部署教程请参考[get_started](../get_started/ernie-4.5.md)；
-- 更多模型说明请参考[支持模型列表](https://console.cloud.baidu-int.com/devops/icode/repos/baidu/paddle_internal/FastDeploy/blob/feature%2Finference-refactor-20250528/docs/supported_models.md)。
+- 更多模型说明请参考[支持模型列表](../supported_models.md)。
 
 
 ## WINT2效果