Ouro-2.6B

📚 Paper (Hugging Face) - 🌐 Project Page

Model Description

⚠️ IMPORTANT: This model is intended for research purposes only. It is provided as-is without warranties for production use.

Ouro-2.6B is a 2.6 billion parameter Looped Language Model (LoopLM) that achieves exceptional parameter efficiency through iterative shared-weight computation.

Key Features

• Exceptional Parameter Efficiency: Matches 3-4B standard transformer performance with only 2.6B parameters
• Iterative Latent Reasoning: Performs reasoning through recurrent computation in latent space
• Adaptive Computation: Supports early exit mechanisms for dynamic compute allocation

Configuration

Recurrent Steps and Adaptive Exit

The model's computational behavior can be configured through the config.json file:

{
  "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

from transformers import AutoConfig, AutoModelForCausalLM

config = AutoConfig.from_pretrained("ouro-llm/Ouro-2.6B")
config.total_ut_steps = 3  # Use 3 recurrent steps instead of 4
model = AutoModelForCausalLM.from_pretrained(
    "ByteDance/Ouro-2.6B",
    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.

Model Architecture

Ouro-2.6B is based on the decoder-only Transformer architecture with parameter sharing across recurrent steps:

Training Details

• Training Tokens: 7.7T tokens
• Training Pipeline:
  • Stage 1: Pre-training (6T tokens)
  • Stage 2: CT Annealing (2.6T tokens)
  • Stage 3: Long Context Training (20B tokens)
  • Stage 4: Mid-training (300B tokens)
• Data Composition: Web data, code, mathematics, long-context documents
• Optimizer: AdamW (β₁=0.9, β₂=0.95)
• Learning Rate Scheduler: Warmup-Stable-Decay (WSD)

Quick Start

⚠️ IMPORTANT: Please use transformers<4.56.0 to avoid compatibility issues. We recommend transformers==4.54.1 or earlier versions.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ByteDance/Ouro-2.6B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto"
)

# Generate text
inputs = tokenizer("The future of AI is", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Acknowledgments

We thank @Antizana for the KV cache fix merged from ouro-cache-fix, which resolved a critical compatibility issue with transformers>=4.56.0.

Citation

@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.

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