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**⚠️ IMPORTANT: This model is intended for research purposes only. It is provided as-is without warranties for production use. **
Ouro-2.6B-Thinking is a reasoning-specialized variant of the Ouro-2.6B base model, enhanced through supervised fine-tuning on high-quality reasoning data. Please use transformers==4.54.1for compatibility.
The model's computational behavior can be configured through the config.json file:
json
{ "total_ut_steps": 4, "early_exit_threshold": 1.0}total_ut_steps: Controls the number of recurrent steps (default: 4). You can adjust this value to trade off between performance and computation time.early_exit_threshold: Controls the adaptive exit mechanism (default: 1.0). Lower values encourage earlier exit, while 1.0 means always use all steps.Example: Modify recurrent steps
python
from transformers import AutoConfig, AutoModelForCausalLM config = AutoConfig.from_pretrained("ByteDance/Ouro-2.6B-Thinking")config.total_ut_steps = 3 # Use 3 recurrent steps instead of 4model = AutoModelForCausalLM.from_pretrained( "ByteDance/Ouro-2.6B-Thinking", config=config, device_map="auto")Note: vLLM does not currently support the adaptive exit feature due to its inference optimization characteristics. When using vLLM, the model will always execute the full number of
total_ut_steps.
Based on Ouro-2.6B with additional reasoning fine-tuning:
| Configuration | Value |
|---|---|
| Parameters | 2.6B |
| Layers | 24 |
| Recurrent Steps | 4 |
| Hidden Size | 2048 |
| Attention Heads | Multi-Head Attention (MHA) |
| FFN Activation | SwiGLU |
| Position Embedding | RoPE |
| Vocabulary Size | 49,152 |
| Context Length | 32K (SFT) |
| Normalization | Sandwich RMSNorm |
⚠️ IMPORTANT: Please use transformers<4.56.0 to avoid compatibility issues. We recommend transformers==4.54.1 or earlier versions.
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Bytedance/Ouro-2.6B-Thinking"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", torch_dtype="auto") # Generate with reasoningmessages = [ {"role": "user", "content": "Solve: If 2x + 3 = 11, what is x?"}]inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, temperature=1.0, top_p=0.7)print(tokenizer.decode(outputs[0], skip_special_tokens=True))We thank @Antizana for the KV cache fix merged from ouro-cache-fix, which resolved a critical compatibility issue with transformers>=4.56.0.
bibtex
@article{zhu2025scaling, title={Scaling Latent Reasoning via Looped Language Models}, author={Zhu, Rui-Jie and Wang, Zixuan and Hua, Kai and Zhang, Tianyu and Li, Ziniu and Que, Haoran and Wei, Boyi and Wen, Zixin and Yin, Fan and Xing, He and others}, journal={arXiv preprint arXiv:2510.25741}, year={2025}} ## License This model is licensed under Apache-2.0. See the LICENSE file for details. ## Project Links - **Paper**: [Scaling Latent Reasoning via Looped Language Models](https://huggingface.co/papers/2510.25741)- **Project Page**: [https://ouro-llm.github.io](https://ouro-llm.github.io)- **Code**: [https://github.com/ByteDance/Ouro](https://github.com/ByteDance/Ouro) ---{ "total_ut_steps": 4, "early_exit_threshold": 1.0}from transformers import AutoConfig, AutoModelForCausalLM config = AutoConfig.from_pretrained("ByteDance/Ouro-2.6B-Thinking")config.total_ut_steps = 3 # Use 3 recurrent steps instead of 4model = AutoModelForCausalLM.from_pretrained( "ByteDance/Ouro-2.6B-Thinking", config=config, device_map="auto")from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Bytedance/Ouro-2.6B-Thinking"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", torch_dtype="auto") # Generate with reasoningmessages = [ {"role": "user", "content": "Solve: If 2x + 3 = 11, what is x?"}]inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, temperature=1.0, top_p=0.7)print(tokenizer.decode(outputs[0], skip_special_tokens=True))@article{zhu2025scaling, title={Scaling Latent Reasoning via Looped Language Models}, author={Zhu, Rui-Jie and Wang, Zixuan and Hua, Kai and Zhang, Tianyu and Li, Ziniu and Que, Haoran and Wei, Boyi and Wen, Zixin and Yin, Fan and Xing, He and others}, journal={arXiv preprint arXiv:2510.25741}, year={2025}} ## License This model is licensed under Apache-2.0. See the LICENSE file for details. ## Project Links - **Paper**: [Scaling Latent Reasoning via Looped Language Models](https://huggingface.co/papers/2510.25741)- **Project Page**: [https://ouro-llm.github.io](https://ouro-llm.github.io)- **Code**: [https://github.com/ByteDance/Ouro](https://github.com/ByteDance/Ouro) ---