Ouro-1.4B

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Fork Description

• Makes the Ouro model compatible with Transformers v5 (tested with 5.9.0).
• Fixes several smaller bugs.
• Serve Ouro with adaptive exit efficiently with Loop-SGLang.
• Adds the trained lookahead gate model.lookahead_exit_gate of the CDB paper, which predicts the exit one step in advance.

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Ouro-1.4B

📚 Paper • 🏠 Project Page

Model Description

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

Ouro-1.4B is a 1.4 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 1.4B 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. When using the Transformers library, the model will always execute the full number of total_ut_steps and select the exited loop state afterwards. Later tokens can therefore attend to KV entries from recurrent depths that earlier tokens would not have reached under actual early exit. Use Loop-SGLang for last-exited KV and early-exit compute savings.

Example: Modify recurrent steps

from transformers import AutoConfig, AutoModelForCausalLM

config = AutoConfig.from_pretrained("KristianS7/Ouro-1.4B", trust_remote_code=True)
config.total_ut_steps = 3  # Use 3 recurrent steps instead of 4
model = AutoModelForCausalLM.from_pretrained(
    "KristianS7/Ouro-1.4B",
    config=config,
    device_map="auto",
    trust_remote_code=True
)

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. For adaptive exit use, Loop-SGLang instead.

Model Architecture

Ouro-1.4B 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 (1.4T 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: Works with transformers==5.9.0.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "KristianS7/Ouro-1.4B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True
)

# 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.