🤗 Hugging Face | 🤖 ModelScope
## Ring-1T-preview, Deep Thinking, No Waiting Recently, we have been fully occupied with the post-training of Ling 2.0's __1T foundational language model__, striving to maximize the __natural language reasoning__ potential of this trillion-scale base model. Conducting post-training on such a huge model, particularly the "training" involved in large-scale reinforcement learning, stands as one of the most technically challenging tasks the Ling Team has encountered since its establishment. On the other hand, it has also been a process that continuously reshapes our technical understanding and reinforces the belief that "__scaling is all you need__" In the early stages of large-scale reinforcement learning training, __Ring-1T__, the thinking version of the 1T foundational language model, has already demonstrated __powerful natural language reasoning capabilities__. In __AIME 2025__ (American Invitational Mathematics Examination), the model achieved a high score of 92.6 through pure natural language reasoning, further approaching GPT-5 with thinking (no tools)'s score of 94.6. Additionally, the model has shown strong competitiveness in the __Harvard-MIT Mathematics Tournament__ (HMMT) 2025, __competition-level code generation tasks__ such as LiveCodeBench v6 and CodeForces, as well as the __abstraction and reasoning benchmark__ ARC-AGI-1 task.
To further explore the reasoning limits of the early version of Ring-1T, we integrated it into the multi-agent framework AWorld (https://github.com/inclusionAI/AWorld ) and conducted pure natural language reasoning tests on the IMO 2025 (International Mathematical Olympiad, 6 problems in total). Previously, we tested Ring-flash-2.0, using the same method: under the setting of three allowed reasoning attempts, Ring-flash-2.0 only managed to solve Problem 3 on the third try. In contrast, during this test, Ring-1T solved Problem 3 in just one attempt, and also produced partially correct answers on Problems 1, 2, 4, and 5 in a single try. This demonstrates advanced reasoning capabilities essential for top-tier math competitions—such as insight, constructive problem solving, counterexample generation, strategic thinking, and rigorous logical-chain reasoning—highlighting the stronger reasoning potential of large-scale thinking models. ### IMO Cases
To facilitate early community exploration of the reasoning capabilities of the trillion-parameter thinking model Ring-1T, we have decided to open-source its preview version, __Ring-1T-preview__, ahead of schedule. This model retains the efficient MoE architecture of Ling 2.0, completed pre-training on 20T tokens of corpora, and underwent RLVR training tailored for reasoning abilities within our self-developed efficient reinforcement learning system __ASystem__ (the __AReaL__ framework of which has been open-sourced), leveraging the previously disclosed "__icepop__" method(https://ringtech.notion.site/icepop). __Ring-1T__ remains under continuous training. While the preview version already demonstrates powerful natural language reasoning capabilities, it still exhibits issues such as language mixing, repetitive reasoning and identity misperception. __We look forward to community exploration and feedback to collectively accelerate the iterative refinement of this trillion-parameter foundation.__ ## Quickstart ### 🤗 Hugging Face Transformers Here is a code snippet to show you how to use the chat model with `transformers`: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "inclusionAI/Ring-1T-preview" model = AutoModelForCausalLM.from_pretrained( model_name, dtype="auto", device_map="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Give me a short introduction to large language models." messages = [ {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt", return_token_type_ids=False).to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=8192 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### 🤖 ModelScope If you're in mainland China, we strongly recommend you to use our model from 🤖 ModelScope. ## License This code repository is licensed under [the MIT License](https://github.com/inclusionAI/Ling-V2/blob/main/LICENSE). ## Tip To facilitate academic research and downstream applications with customizable model naming, we did not conduct specific identity recognition training.