Support qwen3.8

content/_index.md

@@ -138,7 +138,7 @@ sections:
         # Use ModelScope's official TaaS endpoint — free, no local GPU needed
         base_url = 'https://www.modelscope.cn/twinkle'
         api_key = os.environ.get('MODELSCOPE_TOKEN')
-        base_model = 'Qwen/Qwen3.6-27B'
+        base_model = 'Qwen/Qwen3.8-27B'
 
         # Prepare data locally
         dataset = Dataset(dataset_meta=DatasetMeta('ms://swift/self-cognition'))

content/_index.zh.md

@@ -138,7 +138,7 @@ sections:
         # 使用魔搭社区官方 TaaS 端点 — 免费，无需本地 GPU
         base_url = 'https://www.modelscope.cn/twinkle'
         api_key = os.environ.get('MODELSCOPE_TOKEN')
-        base_model = 'Qwen/Qwen3.6-27B'
+        base_model = 'Qwen/Qwen3.8-27B'
 
         # 本地准备数据
         dataset = Dataset(dataset_meta=DatasetMeta('ms://swift/self-cognition'))

content/blog/modelscope-taas/index.md

@@ -18,7 +18,7 @@ We're excited to announce that **Twinkle Training-as-a-Service (TaaS)** is now a
 
 Training-as-a-Service lets you fine-tune large language models through a simple API, without managing infrastructure. The model runs on ModelScope's backend servers; you just send data and receive trained adapters.
 
-Currently available model: **[Qwen/Qwen3.6-27B](https://www.modelscope.cn/models/Qwen/Qwen3.6-27B)**
+Currently available model: **[Qwen/Qwen3.8-27B](https://www.modelscope.cn/models/Qwen/Qwen3.8-27B)**
 
 ## Getting Started
 
@@ -44,8 +44,8 @@ from twinkle.dataset import Dataset, DatasetMeta
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.server.common import input_feature_to_datum
 
-base_model = 'Qwen/Qwen3.6-27B'
-base_url = 'http://www.modelscope.cn/twinkle'
+base_model = 'Qwen/Qwen3.8-27B'
+base_url = 'https://www.modelscope.cn/twinkle'
 api_key = os.environ.get('MODELSCOPE_TOKEN')
 
 # Load and preprocess dataset
@@ -101,8 +101,8 @@ from twinkle import init_tinker_client
 init_tinker_client()
 from tinker import ServiceClient
 
-base_model = 'Qwen/Qwen3.6-27B'
-base_url = 'http://www.modelscope.cn/twinkle'
+base_model = 'Qwen/Qwen3.8-27B'
+base_url = 'https://www.modelscope.cn/twinkle'
 
 service_client = ServiceClient(
     base_url=base_url,
@@ -111,7 +111,7 @@ service_client = ServiceClient(
 
 # Load your trained LoRA
 sampling_client = service_client.create_sampling_client(
-    model_path='twinkle://xxx-Qwen_Qwen3.6-27B-xxx/weights/twinkle-lora-1',
+    model_path='twinkle://xxx-Qwen_Qwen3.8-27B-xxx/weights/twinkle-lora-1',
     base_model=base_model
 )
 
@@ -160,7 +160,7 @@ The free tier has some limitations:
 |-----------|-------|
 | Max Rank | 32 |
 | modules_to_save | Not supported |
-| Multimodal | Text-only (Qwen3.6-27B) |
+| Multimodal | Text-only (Qwen3.8-27B) |
 
 ## What You Can Customize
 

content/blog/modelscope-taas/index.zh.md

@@ -18,7 +18,7 @@ categories:
 
 训练即服务（Training-as-a-Service）让你通过简单的 API 微调大语言模型，无需管理基础设施。模型运行在魔搭的后台服务器上；你只需发送数据，即可获得训练好的适配器。
 
-当前可用模型：**[Qwen/Qwen3.6-27B](https://www.modelscope.cn/models/Qwen/Qwen3.6-27B)**
+当前可用模型：**[Qwen/Qwen3.8-27B](https://www.modelscope.cn/models/Qwen/Qwen3.8-27B)**
 
 ## 快速开始
 
@@ -44,8 +44,8 @@ from twinkle.dataset import Dataset, DatasetMeta
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.server.common import input_feature_to_datum
 
-base_model = 'Qwen/Qwen3.6-27B'
-base_url = 'http://www.modelscope.cn/twinkle'
+base_model = 'Qwen/Qwen3.8-27B'
+base_url = 'https://www.modelscope.cn/twinkle'
 api_key = os.environ.get('MODELSCOPE_TOKEN')
 
 # 加载并预处理数据集
@@ -101,8 +101,8 @@ from twinkle import init_tinker_client
 init_tinker_client()
 from tinker import ServiceClient
 
-base_model = 'Qwen/Qwen3.6-27B'
-base_url = 'http://www.modelscope.cn/twinkle'
+base_model = 'Qwen/Qwen3.8-27B'
+base_url = 'https://www.modelscope.cn/twinkle'
 
 service_client = ServiceClient(
     base_url=base_url,
@@ -111,7 +111,7 @@ service_client = ServiceClient(
 
 # 加载训练好的 LoRA
 sampling_client = service_client.create_sampling_client(
-    model_path='twinkle://xxx-Qwen_Qwen3.6-27B-xxx/weights/twinkle-lora-1',
+    model_path='twinkle://xxx-Qwen_Qwen3.8-27B-xxx/weights/twinkle-lora-1',
     base_model=base_model
 )
 
@@ -160,7 +160,7 @@ for i, seq in enumerate(result.sequences):
 |------|------|
 | 最大 Rank | 32 |
 | modules_to_save | 不支持 |
-| 多模态 | 仅支持文本（Qwen3.6-27B） |
+| 多模态 | 仅支持文本（Qwen3.8-27B） |
 
 ## 可自定义的内容
 

content/docs/guide/taas.md

@@ -23,8 +23,8 @@ from twinkle.dataset import Dataset, DatasetMeta
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.server.common import input_feature_to_datum
 
-# The base model (currently Qwen3.6-27B)
-base_model = 'Qwen/Qwen3.6-27B'
+# The base model (currently Qwen3.8-27B)
+base_model = 'Qwen/Qwen3.8-27B'
 
 # Prepare dataset
 dataset = Dataset(dataset_meta=DatasetMeta(
@@ -167,6 +167,7 @@ client = init_twinkle_client(
 
 | Model | Size | HuggingFace ID | Megatron |
 |:------|:-----|:---------------|:---------|
+| Qwen3.8 | 27B | Qwen/Qwen3.8-* | Yes |
 | Qwen3.6 | 4B-35B-A3B | Qwen/Qwen3.6-* | Yes |
 | Qwen3.5 | 2B-27B | Qwen/Qwen3.5-* | Yes |
 | Qwen3 | 0.6B-32B | Qwen/Qwen3-* | Yes |

content/docs/guide/taas.zh.md

@@ -23,8 +23,8 @@ from twinkle.dataset import Dataset, DatasetMeta
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.server.common import input_feature_to_datum
 
-# 基座模型（当前为 Qwen3.6-27B）
-base_model = 'Qwen/Qwen3.6-27B'
+# 基座模型（当前为 Qwen3.8-27B）
+base_model = 'Qwen/Qwen3.8-27B'
 
 # 准备数据集
 dataset = Dataset(dataset_meta=DatasetMeta(
@@ -167,6 +167,7 @@ client = init_twinkle_client(
 
 | 模型 | 规模 | HuggingFace ID | Megatron |
 |:-----|:-----|:---------------|:---------|
+| Qwen3.8 | 27B | Qwen/Qwen3.8-* | 支持 |
 | Qwen3.6 | 4B-35B-A3B | Qwen/Qwen3.6-* | 支持 |
 | Qwen3.5 | 2B-27B | Qwen/Qwen3.5-* | 支持 |
 | Qwen3 | 0.6B-32B | Qwen/Qwen3-* | 支持 |