Official implementation and model checkpoints for Orthrus, a dual-architecture framework that unifies the exact generation fidelity of autoregressive Large Language Models (LLMs) with the high-speed parallel token generation of diffusion models.

All models use a Qwen3 backbone and guarantee strictly lossless generation.

uv pip install -e .
uv pip install ninja packaging
uv pip install flash-attn --no-build-isolation # or: pip install "flash-attn-4[cu13]" if your device supports it

We recommend uv for fast dependency resolution.

⚡ Try instantly: Run Orthrus directly in Colab:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
 

model = AutoModelForCausalLM.from_pretrained(
    "chiennv/Orthrus-Qwen3-8B",
    dtype=torch.bfloat16, device_map="cuda",
    attn_implementation="flash_attention_2",  # options: sdpa | eager | flash_attention_4
    trust_remote_code=True,
).eval()
tokenizer = AutoTokenizer.from_pretrained("chiennv/Orthrus-Qwen3-8B")
 
prompt = "Write a program to count the frequency of each word in a paragraph."
messages = [{"role": "system", "content": ""}, {"role": "user", "content": prompt}]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True, enable_thinking=False).input_ids

output_ids = model.generate(
    input_ids=input_ids.to(model.device), 
    max_new_tokens=2048,
    use_diffusion_mode=True, 
    streamer=TextStreamer(tokenizer, skip_prompt=True) # enable streaming generation
)

Coming soon: Native integration with vLLM and SGLang is coming soon. Stay tuned!

• Significant Inference Acceleration: Breaks the sequential bottleneck of standard autoregressive decoding, delivering up to a $7.8\times$ speedup on generation tasks.
• Strictly Lossless Generation: Employs an exact intra-model consensus mechanism to guarantee that the output matches the original base model's exact predictive distribution.
• Zero Redundant Memory Overhead: Both the autoregressive and diffusion views attend to the exact same high-fidelity Key-Value (KV) cache natively, resulting in only an $O(1)$ memory cache overhead.
• Parameter Efficient: Parallel generation capabilities are injected by fine-tuning only 16% of the total model parameters while keeping the base LLM strictly frozen.

Orthrus outperforms speculative decoding methods like EAGLE-3, DFlash. By natively sharing the exact same KV cache across dual views, Orthrus avoids the redundant memory overhead of draft models, resulting in significantly higher token acceptance rates and faster inference times, especially as context length scales. Orthrus maintains consistently high end-to-end throughput—even at 40K context lengths compared to DFlash's rapid degradation.

Left: Average verified tokens per forward pass compared to EAGLE-3 and DFlash. Right: End-to-end throughput across scaling context lengths.

While recent diffusion language models (dLLMs) offer parallel decoding, they often suffer from significant conditional drift and severe accuracy degradation on complex reasoning tasks. Orthrus resolves this by decoupling parallel generation from sequential constraints, establishing a new state-of-the-art for parallel generation fidelity.

Throughput vs. Accuracy on MATH-500. Orthrus delivers a ~6x speedup over the Qwen3-8B baseline with strictly lossless performance, whereas adaptations like Fast-dLLM-v2 suffer significant accuracy drops.

If you find this model or architecture useful in your work, please cite our paper:

@misc{vannguyen2026orthrusmemoryefficientparalleltoken,
      title={Orthrus: Memory-Efficient Parallel Token Generation via Dual-View Diffusion}, 
      author={Chien Van Nguyen and Chaitra Hegde and Van Cuong Pham and Ryan A. Rossi and Franck Dernoncourt and Thien Huu Nguyen},
      year={2026},
      eprint={2605.12825},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2605.12825}, 
}