inclusionAI
/

Ring-1T

@@ -88,23 +88,6 @@ To ensure stable and efficient reinforcement learning training for trillion-para
 For the RL training framework, we built a hybrid reward system based on large-scale Serverless Sandbox technology. This system can start up in milliseconds, supports execution environments for over 10 programming languages, and handles request throughput of up to 10K/s. We have open-sourced AReal and hope to accelerate RL training and research in the open-source community through technological openness.
-## Limitations and Future Plans
-Ring-1T represents the Bailing team’s first attempt at developing a trillion-scale deep reasoning model. The current version may occasionally exhibit issues such as identity recognition bias, language mixing, and repetitive generation. Additionally, since its attention architecture still adopts the GQA approach from Ling 2.0, there remains room for improvement in reasoning efficiency under long-context scenarios.
-We will continue to optimize these aspects in future releases and highly welcome feedback from the community. Furthermore, training for Ring-1T is still ongoing. We are committed to further unlocking the reasoning potential of this trillion-parameter foundation model and look forward to sharing more mature upgraded versions with everyone as soon as possible.
-Welcome to visit our open-source repository and demo page for download and usage.
-Hugging Face: [https://huggingface.co/inclusionAI/Ring-1T](https://huggingface.co/inclusionAI/Ring-1T)
-ModelScope: [https://modelscope.cn/models/inclusionAI/Ring-1T](https://modelscope.cn/models/inclusionAI/Ring-1T)
-Ling Chat (for Chinese users): [https://ling.tbox.cn/chat](https://ling.tbox.cn/chat)
-ZenMux (for overseas developers, offering Chat testing and API capabilities): [https://zenmux.ai/inclusionai/ring-1t](https://zenmux.ai/inclusionai/ring-1t)
-Ring-1T@Aworld IMO test trajectory: [https://github.com/inclusionAI/AWorld/tree/main/examples/imo/samples/samples%20from%20Ring-1T](https://github.com/inclusionAI/AWorld/tree/main/examples/imo/samples/samples%20from%20Ring-1T)
 ## Quickstart
@@ -194,6 +177,24 @@ More usage can be found [here](https://docs.sglang.ai/basic_usage/send_request.h
 We recommend you to use [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory) to [finetune Ring](https://github.com/inclusionAI/Ring-V2/blob/main/docs/llamafactory_finetuning.md).
 ## License
 This code repository is licensed under [the MIT License](https://github.com/inclusionAI/Ring-V2/blob/master/LICENSE).

 For the RL training framework, we built a hybrid reward system based on large-scale Serverless Sandbox technology. This system can start up in milliseconds, supports execution environments for over 10 programming languages, and handles request throughput of up to 10K/s. We have open-sourced AReal and hope to accelerate RL training and research in the open-source community through technological openness.
 ## Quickstart
 We recommend you to use [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory) to [finetune Ring](https://github.com/inclusionAI/Ring-V2/blob/main/docs/llamafactory_finetuning.md).
+## Limitations and Future Plans
+Ring-1T represents the Bailing team’s first attempt at developing a trillion-scale deep reasoning model. The current version may occasionally exhibit issues such as identity recognition bias, language mixing, and repetitive generation. Additionally, since its attention architecture still adopts the GQA approach from Ling 2.0, there remains room for improvement in reasoning efficiency under long-context scenarios.
+We will continue to optimize these aspects in future releases and highly welcome feedback from the community. Furthermore, training for Ring-1T is still ongoing. We are committed to further unlocking the reasoning potential of this trillion-parameter foundation model and look forward to sharing more mature upgraded versions with everyone as soon as possible.
+Welcome to visit our open-source repository and demo page for download and usage.
+Hugging Face: [https://huggingface.co/inclusionAI/Ring-1T](https://huggingface.co/inclusionAI/Ring-1T)
+ModelScope: [https://modelscope.cn/models/inclusionAI/Ring-1T](https://modelscope.cn/models/inclusionAI/Ring-1T)
+Ling Chat (for Chinese users): [https://ling.tbox.cn/chat](https://ling.tbox.cn/chat)
+ZenMux (for overseas developers, offering Chat testing and API capabilities): [https://zenmux.ai/inclusionai/ring-1t?utm_source=hf_inclusionAI](https://zenmux.ai/inclusionai/ring-1t?utm_source=hf_inclusionAI)
+Ring-1T@Aworld IMO test trajectory: [https://github.com/inclusionAI/AWorld/tree/main/examples/imo/samples/samples%20from%20Ring-1T](https://github.com/inclusionAI/AWorld/tree/main/examples/imo/samples/samples%20from%20Ring-1T)
 ## License
 This code repository is licensed under [the MIT License](https://github.com/inclusionAI/Ring-V2/blob/master/LICENSE).