modelscope · tastelikefeet · Aug 14, 2026 · Aug 11, 2026 · Aug 12, 2026 · Aug 12, 2026
diff --git a/README.md b/README.md
@@ -106,6 +106,7 @@ sh INSTALL_MEGATRON.sh
 | Server startup scripts               | transformers/megatron | [Script](cookbook/client/server)                 |
 
 ## Changelog
+- 🎉2026-08-12 The ModelScope training service has been deployed to [Qwen/Qwen3.8-27B](https://www.modelscope.cn/models/Qwen/Qwen3.8-27B).
 - 🎉2026-08-04 Sandboxed multi-turn RL is now supported: run model-generated code in isolated [AgentENV](https://github.com/kvcache-ai/AgentENV) Firecracker microVMs, or in an OpenEnv server, with the same `train.py`. See the [cookbook](cookbook/rl/envs) and the [deployment guide](docs/source_en/Usage%20Guide/Agentic-RL-Deployment-and-Training.md).
 - 🎉2026-05-20 Support DeepSeek-V4-Flash and DeepSeek-V4-Pro models.
 - 🎉2026-05-20 Multi-turn rollout and tool calling in RL are now supported. The Cookbook is currently being written. You can use `from twinkle_agentic.rollout import MultiTurnRollout/APIMultiTurnRollout` directly for multi-turn rollout.
@@ -142,13 +143,16 @@ supported on Twinkle✨ framework.
 > For serverless training service accessed via `base_url=https://www.modelscope.cn/twinkle`, it
 > is currently provided via the Tinker-compatible APIs. We will be rolling out services that support
 > both Tinker APIs, as well as the full-fledged Twinkle✨ native APIs. The serverless endpoint is backed
-> by one training base at a time, and currently it is [Qwen3.6-27B](https://modelscope.cn/models/Qwen/Qwen3.6-27B).
+> by one training base at a time, and currently it is [Qwen3.8-27B](https://modelscope.cn/models/Qwen/Qwen3.8-27B).
 
 | Model Type          | Model ID on [ModelScope](https://modelscope.cn)                                                                 |               Model Size                | Requires             | Support Megatron |                                                HF Model ID                                                |
 |---------------------|-----------------------------------------------------------------------------------------------------------------|:---------------------------------------:|----------------------|:----------------:|:---------------------------------------------------------------------------------------------------------:|
 | qwen3 series        | [Qwen/Qwen3-14B-Base](https://modelscope.cn/models/Qwen/Qwen3-14B-Base)                                         |           0.6B/1.7B/4B/8B/14B           | transformers>=4.51   |        ✔         |                     [Qwen/Qwen3-14B-Base](https://huggingface.co/Qwen/Qwen3-14B-Base)                     |
 |                     | [Qwen/Qwen3-32B](https://modelscope.cn/models/Qwen/Qwen3-32B)                                                   |         0.6B/1.7B/4B/8B/14B/32B         | transformers>=4.51   |        ✔         |                          [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B)                          |
 | qwen3_moe series    | [Qwen/Qwen3-30B-A3B-Base](https://modelscope.cn/models/Qwen/Qwen3-30B-A3B-Base)                                 |       30B-A3B/A3B-Base,235B-A22B        | transformers>=4.51   |        ✔         |                 [Qwen/Qwen3-30B-A3B-Base](https://huggingface.co/Qwen/Qwen3-30B-A3B-Base)                 |
+| qwen3.8 series | [Qwen/Qwen3.8-27B](https://www.modelscope.cn/models/Qwen/Qwen3.8-27B) | 27B | transformers>=5.2.0 | ✔ | [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) |
+| qwen3.6 moe series | [Qwen/Qwen3.6-35B-A3B](https://www.modelscope.cn/models/Qwen/Qwen3.6-35B-A3B) | 35B-A3B, etc. | transformers>=5.2.0 | ✔ | [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) |
+| qwen3.6 series | [Qwen/Qwen3.6-27B](https://www.modelscope.cn/models/Qwen/Qwen3.6-27B) | 4B ~ 27B | transformers>=5.2.0 | ✔ | [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) |
 | qwen3.5 moe series  | [Qwen/Qwen3.5-35B-A3B](https://www.modelscope.cn/models/Qwen/Qwen3.5-35B-A3B)                                   |         35B-A3B,122B-A10B, etc.         | transformers>=5.2.0   |        ✔         |                    [Qwen/Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B)                    |
 | qwen3.5 series      | [Qwen/Qwen3.5-9B](https://www.modelscope.cn/models/Qwen/Qwen3.5-9B)                                             |                2B ~ 27B                 | transformers>=5.2.0   |        ✔         |                         [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B)                         |
 | qwen2 series        | [Qwen/Qwen2-0.5B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-0.5B-Instruct)                               |            0.5B/1.5B/7B/72B             | transformers>=4.37   |        ✔         |                [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)                |
@@ -254,7 +258,7 @@ from twinkle.dataset import Dataset, DatasetMeta
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.server.common import input_feature_to_datum
 
-base_model = 'ms://Qwen/Qwen3.6-27B'
+base_model = 'ms://Qwen/Qwen3.8-27B'
 base_url='your-base-url'
 api_key='your-api-key'
 

diff --git a/README_ZH.md b/README_ZH.md
@@ -102,6 +102,7 @@ sh INSTALL_MEGATRON.sh
 Twinkle✨支持相同的算法接口运行在单GPU、torchrun多机、Ray、Client等各场景下。其算法过程是外露的，非常便于修改和调试。完整的框架介绍请查看[快速开始](https://modelscope.github.io/twinkle-web/zh/docs/usage-guide/quick-start/)
 
 ## 更新日志
+- 🎉2026-08-12 ModelScope的训练服务部署为[Qwen/Qwen3.8-27B](https://www.modelscope.cn/models/Qwen/Qwen3.8-27B)。
 - 🎉2026-08-04 支持沙箱环境下的多轮RL训练：模型生成的代码可在隔离的 [AgentENV](https://github.com/kvcache-ai/AgentENV) Firecracker microVM 或 OpenEnv 服务中执行，两个后端共用同一份 `train.py`。参考 [cookbook](cookbook/rl/envs) 和[部署文档](docs/source_zh/使用指引/Agentic%20RL部署与训练.md)。
 - 🎉2026-05-20 支持DeepSeek-V4-Flash and DeepSeek-V4-Pro系列模型。
 - 🎉2026-05-20 支持多轮rollout和RL中的工具调用，Cookbook正在编写中，可以直接使用`from twinkle_agentic.rollout import MultiTurnRollout/APIMultiTurnRollout`进行多轮rollout。
@@ -132,13 +133,16 @@ Twinkle✨支持相同的算法接口运行在单GPU、torchrun多机、Ray、Cl
 随着新模型的发布，我们将添加对更多模型的支持。下表列出了 Twinkle✨ 框架当前支持的模型。
 
 >[!Note]
-> 通过 `base_url=https://www.modelscope.cn/twinkle` 访问的无服务器训练服务，目前是通过兼容Tinker的API提供的。我们将陆续推出同时支持Tinker API和完整Twinkle✨原生 API的服务。无服务器端点每次由一个训练基座支持，目前使用的是[Qwen3.6-27B](https://modelscope.cn/models/Qwen/Qwen3.6-27B)。
+> 通过 `base_url=https://www.modelscope.cn/twinkle` 访问的无服务器训练服务，目前是通过兼容Tinker的API提供的。我们将陆续推出同时支持Tinker API和完整Twinkle✨原生 API的服务。无服务器端点每次由一个训练基座支持，目前使用的是[Qwen3.8-27B](https://modelscope.cn/models/Qwen/Qwen3.8-27B)。
 
 | Model Type          | Model ID 举例                                                                                                     |               Model Size                | Requires             | Support Megatron |                                                HF Model ID                                                |
 |---------------------|-----------------------------------------------------------------------------------------------------------------|:---------------------------------------:|----------------------|:----------------:|:---------------------------------------------------------------------------------------------------------:|
 | qwen3 全系列           | [Qwen/Qwen3-14B-Base](https://modelscope.cn/models/Qwen/Qwen3-14B-Base)                                         |           0.6B/1.7B/4B/8B/14B           | transformers>=4.51   |        ✔         |                     [Qwen/Qwen3-14B-Base](https://huggingface.co/Qwen/Qwen3-14B-Base)                     |
 |                     | [Qwen/Qwen3-32B](https://modelscope.cn/models/Qwen/Qwen3-32B)                                                   |         0.6B/1.7B/4B/8B/14B/32B         | transformers>=4.51   |        ✔         |                          [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B)                          |
 | qwen3_moe 全系列       | [Qwen/Qwen3-30B-A3B-Base](https://modelscope.cn/models/Qwen/Qwen3-30B-A3B-Base)                                 |       30B-A3B/A3B-Base，235B-A22B        | transformers>=4.51   |        ✔         |                 [Qwen/Qwen3-30B-A3B-Base](https://huggingface.co/Qwen/Qwen3-30B-A3B-Base)                 |
+| qwen3.8 全系列 | [Qwen/Qwen3.8-27B](https://www.modelscope.cn/models/Qwen/Qwen3.8-27B) | 27B | transformers>=5.2.0 | ✔ | [Qwen/Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) |
+| qwen3.6 moe 全系列 | [Qwen/Qwen3.6-35B-A3B](https://www.modelscope.cn/models/Qwen/Qwen3.6-35B-A3B) | 35B-A3B, etc. | transformers>=5.2.0 | ✔ | [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) |
+| qwen3.6 全系列 | [Qwen/Qwen3.6-27B](https://www.modelscope.cn/models/Qwen/Qwen3.6-27B) | 4B ~ 27B | transformers>=5.2.0 | ✔ | [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) |
 | qwen3.5 moe 全系列     | [Qwen/Qwen3.5-35B-A3B](https://www.modelscope.cn/models/Qwen/Qwen3.5-35B-A3B)                                   |         35B-A3B,122B-A10B, etc.         | transformers>=5.2.0   |        ✔         |                    [Qwen/Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B)                    |
 | qwen3.5 全系列         | [Qwen/Qwen3.5-9B](https://www.modelscope.cn/models/Qwen/Qwen3.5-9B)                                             |                2B ~ 27B                 | transformers>=5.2.0   |        ✔         |                         [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B)                         |
 | qwen2 全系列           | [Qwen/Qwen2-0.5B-Instruct](https://modelscope.cn/models/Qwen/Qwen2-0.5B-Instruct)                               |            0.5B/1.5B/7B/72B             | transformers>=4.37   |        ✔         |                [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct)                |
@@ -243,7 +247,7 @@ from twinkle.dataset import Dataset, DatasetMeta
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.server.common import input_feature_to_datum
 
-base_model = 'ms://Qwen/Qwen3.6-27B'
+base_model = 'ms://Qwen/Qwen3.8-27B'
 base_url='your-base-url'
 api_key='your-api-key'
 

diff --git a/cookbook/client/server/megatron/server_config.yaml b/cookbook/client/server/megatron/server_config.yaml
@@ -39,7 +39,7 @@ applications:
       server_config:
         per_token_model_limit: 3      # Maximum number of models (adapters) per token (server-globally enforced)
       supported_models:
-        - Qwen/Qwen3.6-27B
+        - Qwen/Qwen3.8-27B
 
     deployments:
       - name: TinkerCompatServer
@@ -59,11 +59,11 @@ applications:
   # 3. Sampler Service - Runs inference / sampling using vLLM engine
   #    Used for generating text from the model (e.g., evaluating LoRA results).
   #    Config: TP=2 x DP=2 on 4 GPUs, ~27GB weights/GPU, ~37GB for KV cache + LoRA
-  - name: sampler-Qwen3.6-27B
-    route_prefix: /api/v1/sampler/Qwen/Qwen3.6-27B
+  - name: sampler-Qwen3.8-27B
+    route_prefix: /api/v1/sampler/Qwen/Qwen3.8-27B
     import_path: sampler
     args:
-      model_id: "ms://Qwen/Qwen3.6-27B"  # ModelScope model identifier
+      model_id: "ms://Qwen/Qwen3.8-27B"  # ModelScope model identifier
       nproc_per_node: 4               # Number of GPU processes per node
       sampler_type: vllm              # Inference engine: 'vllm' (fast) or 'torch' (TorchSampler)
       engine_args:                    # vLLM engine-specific settings
@@ -102,12 +102,12 @@ applications:
 
   # 2. Model Service - Hosts the base model for training.
   #    Config: PP=2 x DP=2 on 4 GPUs, ~27GB weights/GPU, comfortable for LoRA training
-  - name: models-Qwen3.6-27B
-    route_prefix: /api/v1/model/Qwen/Qwen3.6-27B
+  - name: models-Qwen3.8-27B
+    route_prefix: /api/v1/model/Qwen/Qwen3.8-27B
     import_path: model
     args:
       backend: megatron                          # Use Megatron-LM backend
-      model_id: "ms://Qwen/Qwen3.6-27B"          # ModelScope model identifier
+      model_id: "ms://Qwen/Qwen3.8-27B"          # ModelScope model identifier
       max_length: 32768                           # model max length
       max_loras: 3                                # model max loras
       nproc_per_node: 4                           # Number of GPU processes per node

diff --git a/docs/source_en/Components/Model/SupportedModels.md b/docs/source_en/Components/Model/SupportedModels.md
@@ -6,6 +6,8 @@ Twinkle supports any model compatible with HuggingFace Transformers or Megatron-
 
 | Model Family | Model IDs | Parameters | Features |
 |:-------------|:----------|:-----------|:---------|
+| Qwen 3.8 | `Qwen/Qwen3.8-27B` | 27B | Thinking mode |
+| Qwen 3.6 | `Qwen/Qwen3.6-27B`, `Qwen/Qwen3.6-35B-A3B` | 27B, 35B-A3B | MoE, Thinking mode |
 | Qwen 3.5 | `Qwen/Qwen3.5-0.6B` ~ `Qwen/Qwen3.5-235B-A22B` | 0.6B–235B | MoE, Thinking mode |
 | Qwen 2.5 | `Qwen/Qwen2.5-0.5B` ~ `Qwen/Qwen2.5-72B` | 0.5B–72B | Dense |
 | DeepSeek V4 | `deepseek-ai/DeepSeek-V4` | 685B MoE | Custom DSML encoding |

diff --git a/docs/source_en/Usage Guide/Introduction-with-Qwen3.5.md b/docs/source_en/Usage Guide/Introduction-with-Qwen3.5.md
@@ -465,9 +465,9 @@ init_tinker_client()
 
 from tinker import ServiceClient
 
-# Base model
-base_model = 'Qwen/Qwen3.5-4B'
-base_url = 'http://www.modelscope.cn/twinkle'
+# Base model (currently deployed in the ModelScope official environment)
+base_model = 'Qwen/Qwen3.8-27B'
+base_url = 'https://www.modelscope.cn/twinkle'
 
 
 def train():
@@ -530,7 +530,7 @@ Alongside the open-source release of Twinkle, ModelScope provides a hosted model
 
 ```python
 base_url = 'https://www.modelscope.cn/twinkle'
-base_model = 'Qwen/Qwen3.5-4B'  # Model currently deployed in the official environment
+base_model = 'Qwen/Qwen3.8-27B'  # Model currently deployed in the official environment
 ```
 
 ---

diff --git a/docs/source_en/Usage Guide/Server and Client/Overview.md b/docs/source_en/Usage Guide/Server and Client/Overview.md
@@ -106,5 +106,5 @@ The same examples work with the ModelScope managed service by changing only the
 ```bash
 export TWINKLE_SERVER_URL=https://www.modelscope.cn/twinkle
 export TWINKLE_SERVER_TOKEN="$MODELSCOPE_TOKEN"
-export TWINKLE_MODEL_ID=Qwen/Qwen3.6-27B
+export TWINKLE_MODEL_ID=Qwen/Qwen3.8-27B
 ```
diff --git a/docs/source_en/Usage Guide/Train-as-a-Service.md b/docs/source_en/Usage Guide/Train-as-a-Service.md
@@ -2,7 +2,7 @@
 
 Alongside the open-source release of the Twinkle framework, we also provide a hosted model training service (Training as a Service) powered by ModelScope's backend infrastructure. Developers can use this service to experience Twinkle's training API for free.
 
-The model currently running on the cluster is [Qwen/Qwen3.6-27B](https://www.modelscope.cn/models/Qwen/Qwen3.6-27B). Below are the detailed usage instructions:
+The model currently running on the cluster is [Qwen/Qwen3.8-27B](https://www.modelscope.cn/models/Qwen/Qwen3.8-27B). Below are the detailed usage instructions:
 
 ## Step 1. Register a ModelScope Account and Obtain Your API Key
 
@@ -30,7 +30,7 @@ from twinkle.dataset import Dataset, DatasetMeta
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.server.common import input_feature_to_datum
 
-base_model = 'ms://Qwen/Qwen3.6-27B'
+base_model = 'ms://Qwen/Qwen3.8-27B'
 base_url='https://www.modelscope.cn/twinkle'
 api_key=os.environ.get('MODELSCOPE_TOKEN')
 
@@ -64,7 +64,7 @@ for epoch in range(2):
     print(f'Saved checkpoint for epoch {epoch} to {result.path}')
 ```
 
-With the code above, you can train a self-cognition LoRA based on `Qwen/Qwen3.6-27B`. This LoRA will change the model's name and creator to the names specified during training. To perform inference using this LoRA:
+With the code above, you can train a self-cognition LoRA based on `Qwen/Qwen3.8-27B`. This LoRA will change the model's name and creator to the names specified during training. To perform inference using this LoRA:
 
 ```python
 import os
@@ -79,7 +79,7 @@ init_tinker_client()
 
 from tinker import ServiceClient
 
-base_model = 'Qwen/Qwen3.6-27B'
+base_model = 'Qwen/Qwen3.8-27B'
 base_url = 'https://www.modelscope.cn/twinkle'
 
 # Step 2: Define the base model and connect to the server
@@ -92,7 +92,7 @@ service_client = ServiceClient(
 # The model_path is a twinkle:// URI pointing to a previously saved LoRA checkpoint.
 # The server will load the base model and apply the LoRA adapter weights.
 sampling_client = service_client.create_sampling_client(
-    model_path='twinkle://xxx-Qwen_Qwen3.6-35B-A3B-xxx/weights/twinkle-lora-1',
+    model_path='twinkle://xxx-Qwen_Qwen3.8-27B-xxx/weights/twinkle-lora-1',
     base_model=base_model
 )
 

diff --git a/docs/source_zh/使用指引/Qwen3.5最佳实践.md b/docs/source_zh/使用指引/Qwen3.5最佳实践.md
@@ -465,9 +465,9 @@ init_tinker_client()
 
 from tinker import ServiceClient
 
-# 基座模型
-base_model = 'Qwen/Qwen3.5-4B'
-base_url = 'http://www.modelscope.cn/twinkle'
+# 基座模型（魔搭官方环境当前部署的模型）
+base_model = 'Qwen/Qwen3.8-27B'
+base_url = 'https://www.modelscope.cn/twinkle'
 
 
 def train():
@@ -530,7 +530,7 @@ Twinkle 框架开源的同时，魔搭社区依托自身算力基础设施，提
 
 ```python
 base_url = 'https://www.modelscope.cn/twinkle'
-base_model = 'Qwen/Qwen3.5-4B'  # 官方环境当前部署的模型
+base_model = 'Qwen/Qwen3.8-27B'  # 官方环境当前部署的模型
 ```
 
 ---

diff --git a/docs/source_zh/使用指引/服务端和客户端/概述.md b/docs/source_zh/使用指引/服务端和客户端/概述.md
@@ -106,5 +106,5 @@ python cookbook/client/twinkle/self_cognition.py
 ```bash
 export TWINKLE_SERVER_URL=https://www.modelscope.cn/twinkle
 export TWINKLE_SERVER_TOKEN="$MODELSCOPE_TOKEN"
-export TWINKLE_MODEL_ID=Qwen/Qwen3.6-27B
+export TWINKLE_MODEL_ID=Qwen/Qwen3.8-27B
 ```
diff --git a/docs/source_zh/使用指引/训练服务.md b/docs/source_zh/使用指引/训练服务.md
@@ -3,7 +3,7 @@
 在 Twinkle 框架开源的同时，我们依托ModelScope的后台服务，也提供了托管的模型训练服务(Training as a Service），开发者可以通过这一服务，
 免费体验Twinkle的训练API。
 
-目前在集群中运行的模型是[Qwen/Qwen3.6-27B](https://www.modelscope.cn/models/Qwen/Qwen3.6-27B)。下面介绍具体的使用方法：
+目前在集群中运行的模型是[Qwen/Qwen3.8-27B](https://www.modelscope.cn/models/Qwen/Qwen3.8-27B)。下面介绍具体的使用方法：
 
 ## Step 1. 注册ModelScope用户并获取 API Key
 
@@ -31,7 +31,7 @@ from twinkle.dataset import Dataset, DatasetMeta
 from twinkle.preprocessor import SelfCognitionProcessor
 from twinkle.server.common import input_feature_to_datum
 
-base_model = 'ms://Qwen/Qwen3.6-27B'
+base_model = 'ms://Qwen/Qwen3.8-27B'
 base_url='https://www.modelscope.cn/twinkle'
 api_key=os.environ.get('MODELSCOPE_TOKEN')
 
@@ -65,7 +65,7 @@ for epoch in range(2):
     print(f'Saved checkpoint for epoch {epoch} to {result.path}')
 ```
 
-通过上述代码，你可以训练一个原模型为`Qwen/Qwen3.6-27B`的自我认知lora。这个lora会改变模型的名称和制造者为训练时指定的名称。使用这个lora进行推理：
+通过上述代码，你可以训练一个原模型为`Qwen/Qwen3.8-27B`的自我认知lora。这个lora会改变模型的名称和制造者为训练时指定的名称。使用这个lora进行推理：
 
 ```python
 import os
@@ -80,7 +80,7 @@ init_tinker_client()
 
 from tinker import ServiceClient
 
-base_model = 'Qwen/Qwen3.6-27B'
+base_model = 'Qwen/Qwen3.8-27B'
 base_url = 'https://www.modelscope.cn/twinkle'
 
 # Step 2: Define the base model and connect to the server
@@ -93,7 +93,7 @@ service_client = ServiceClient(
 # The model_path is a twinkle:// URI pointing to a previously saved LoRA checkpoint.
 # The server will load the base model and apply the LoRA adapter weights.
 sampling_client = service_client.create_sampling_client(
-    model_path='twinkle://xxx-Qwen_Qwen3.6-35B-A3B-xxx/weights/twinkle-lora-1',
+    model_path='twinkle://xxx-Qwen_Qwen3.8-27B-xxx/weights/twinkle-lora-1',
     base_model=base_model
 )