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Seerattention QwQ 32B AttnGates
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Seerattention QwQ 32B AttnGates
Developed by SeerAttention
Introducing an attention gating (AttnGates) weight adapter for the QwQ-32B model to accelerate long-context computation through dynamic block-level sparsity
Downloads 35
Release Time : 4/25/2025
Model Overview
This repository contains the attention gating weights introduced by SeerAttention for the QwQ-32B model, accelerating the prefill phase computation of large language models through learnable attention gating modules while preserving model integrity.
Model Features
Dynamic Block-Level Sparsity
Achieves dynamic block-level sparsity through attention gating modules, accelerating computation-intensive prefill phases.
Parameter-Efficient Training
Trains gating modules using a self-distillation framework, eliminating the need for expensive full-model retraining.
Custom Computation Kernel
Utilizes a custom block-sparse FlashAttention kernel for efficient inference computation.
Attention Pattern Preservation
Gating modules learn to mimic the original model's 2D max-pooling attention patterns, maintaining model integrity.
Model Capabilities
Long-context processing
Efficient attention computation
Dynamic sparse inference
Use Cases
Efficient Inference
Long Document Processing
Accelerates prefill phase computation for long documents
Significantly reduces computational overhead through dynamic sparsity.
Large Model Deployment
Reduces computational resource requirements for large language models in real-world deployment
Improves inference efficiency while maintaining model performance.
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