Flux Attention is a context-aware framework that dynamically optimizes attention computation at the layer level. By integrating a lightweight Layer Router into frozen pretrained LLMs, it adaptively routes each layer to Full Attention (FA) or Sparse Attention (SA) based on the input context. This method preserves high-fidelity information retrieval while ensuring practical wall-clock speedups.

Paper: Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference
GitHub: qqtang-code/FluxAttention
Project Page: Flux Attention Website

Installation

To use this model, you need to install the dependencies from the official repository, including Block-Sparse-Attention and the fluxattn package.

Quick Start (Inference)

The following example demonstrates how to load the model with its custom architecture and perform inference.

python

import torch
import json
from transformers import AutoTokenizer, AutoModelForCausalLM

def load_sparse_model(model_path):
    """
    Dynamically loads the correct sparse architecture based on config.
    """
    config_path = f"{model_path}/config.json"
    with open(config_path, "r") as f:
        config_data = json.load(f)

    arch = config_data.get("architectures", [])
    if not arch:
        raise ValueError("No architecture found in config.json")

    arch_name = arch[0]
    print(f"🚀 Detected architecture: {arch_name}")

    # Register custom architectures (requires fluxattn package)
    if "PawLlama" in arch_name:
        from fluxattn.training.eval.modeling_flash_llama import (
            PawLlamaForCausalLM, PawLlamaConfig
        )
        AutoModelForCausalLM.register(PawLlamaConfig, PawLlamaForCausalLM)
        model_cls = PawLlamaForCausalLM
        
    elif "PawQwen" in arch_name:
        from fluxattn.training.eval.modeling_flash_qwen import (
            PawQwen3ForCausalLM, PawQwen3Config
        )
        AutoModelForCausalLM.register(PawQwen3Config, PawQwen3ForCausalLM)
        model_cls = PawQwen3ForCausalLM
    else:
        raise ValueError(f"Unsupported architecture: {arch_name}")

    # Load model
    model = model_cls.from_pretrained(
        model_path,
        torch_dtype=torch.bfloat16,
        device_map="auto",
        trust_remote_code=True,
    )
    return model

# --- Execution ---
model_path = "QQTang1223/Flux-Attention-Qwen3-8B" # Replace with your checkpoint path if different
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

print("Loading Flux Attention Model...")
model = load_sparse_model(model_path)
model.eval()

# Generate
input_text = "Explain quantum mechanics in one sentence."
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")

print("Generating...")
outputs = model.generate(**inputs, max_new_tokens=100)
print("
Output:
" + tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation

bibtex

@misc{qiu2026fluxattentioncontextawarehybrid,
      title={Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference}, 
      author={Quantong Qiu and Zhiyi Hong and Yi Yang and Haitian Wang and Kebin Liu and Qingqing Dang and Juntao Li and Min Zhang},
      year={2026},
      eprint={2604.07394},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2604.07394}, 
}

QQTang1223/full_streaming_Qwen3-8B

Flux Attention: Context-Aware Hybrid Attention for Efficient LLMs Inference

Installation

Quick Start (Inference)

Citation

QQTang1223/
full_streaming_Qwen3-8B