HRWKV7-hxa079-Qwen3-8B
Model Description
HRWKV7-Qwen3-8N-Preview is an RNN hybrid architecture model that combines RWKV v7's linear attention mechanism with Group Query Attention (GQA) layers. Built upon the Qwen3-8B foundation, this model replaces most Transformer attention blocks with RWKV blocks while strategically maintaining some GQA layers to enhance performance on specific tasks.
- Developed by: OpenMOSE
- Model type: Hybrid Linear-Attention Language Model
- Language(s): Multilingual (inherited from Qwen3-8B)
- License: Apache-2.0
- Base Model: Qwen3-8B
- Year: 2025
Architecture Specifications
- Architecture: RWKV v7 based "hxa079" Architecture + Group Query Attention Hybrid
- Total Layers: 36 layers (L36D4096)
- 32 RWKV layers (with Rope)
- 4 GQA layers (No Rope, No Position Embeddings)
- Hidden Dimension: 4096
- Training Context Window: 4096 tokens
- Inference Context Window 16384+
- Training Strategy Following RADLADS method based knowledge distillation
Technical Innovation
RWKV "hxa079" Architecture
The model implements several key improvements over original RWKV architectures:
- Token Shift Removal: In order to effectively inherit the teacher model weights, we removed the residual connection one token ago.
- GroupNorm Removal: Helps improve training stability issues
- k_first Introduction: Experimentally adopted the approach of residually connecting k layers in layer 0.
Hybrid Design Benefits
- Linear Attention Inference: RWKV blocks enable O(1) memory complexity during inference, and the hybrid approach reduces the KVCache to 1/9 of full GQA.
- Enhanced Needle Tasks: Strategic placement of GQA layers significantly improves performance on needle-in-haystack retrieval tasks, addressing a known limitation of pure linear attention models
- Implicit Position Encoding: Interestingly, the model achieves better performance when RoPE (Rotary Position Embedding) is not applied to GQA layers, suggesting that RWKV blocks provide implicit positional encoding capabilities
Intended Use
This is an experimental research model designed to explore hybrid architectures combining linear and quadratic attention mechanisms. It is intended for:
- Research into efficient attention mechanisms
- Benchmarking hybrid architecture performance
- Exploring linear attention limitations and solutions
- Academic and industrial R&D purposes
Limitations
- Experimental Status: This model is in experimental stages and may exhibit unexpected behaviors
- Context Window: Limited to 4096 tokens during training, though RWKV architecture theoretically supports longer sequences
- Performance Variability: As a hybrid model, performance may vary significantly across different task types
Training Details
- Training Context Window: 4096 tokens
- Training GPU AMD MI300X x 1(takes 80hrs) Runpod
- Training Strategy 8bit MLP Quant, frozen emb,mlp,head, Deepspeed Stage1, Stage1 100M, Stage2 360M
- Base Model Initialization: Weights initialized from Qwen3-8B
- Architecture Conversion: Transformer attention blocks systematically replaced with RWKV blocks, except for 6 strategically placed GQA layers
Evaluation
Performance evaluation is ongoing. The model shows promising results in:
- Maintaining base model capabilities while achieving linear attention efficiency
- Significantly improved needle-in-haystack task performance compared to pure RWKV architectures
- Competitive performance on standard language modeling benchmarks
Thank you for Big help :)
Training Code
Model Card Contact
OpenMOSE - 2025
Note: This is an experimental model. Performance characteristics and behaviors may differ from both pure RWKV and standard Transformer architectures. Users should thoroughly evaluate the model for their specific use cases.
