This model requires a custom fork of llama.cpp with RWKV079 implementation
Model Overview
Model Name: RWKV-Qwen3-32B-Hybrid-GGUF Repository: OpenMOSE/RWKV-Qwen3-32B-hxa079-Low Format: GGUF (for llama.cpp) with imatrix quantization Year: 2025 Release phase: alpha
Description
RWKV-Qwen3-32B-Hybrid-GGUF is an experimental large language model that combines the strengths of traditional transformer architecture with the efficiency of RWKV (Receptance Weighted Key Value) mechanisms. This model is specifically optimized for inference in memory-constrained environments while maintaining excellent context length capabilities.
Technical Specifications
Model Parameters
Parameter Count: 32 Billion parameters
Architecture: RWKV079 + GQA (Grouped-Query Attention) Hybrid Linear Attention
Base Model: Alibaba Qwen3-32B
Suitable Ctx Length: 32768 (passkey up to 80k)
Layers: 56 RWKV, 8 NoPE GQA
Key Innovation
The model achieves remarkable efficiency by:
Converting 87.5% of attention layers from the base Qwen3-32B model to RWKV architecture
Reducing KV (Key-Value) cache size to 1/8 of the original
Enabling superior long-context inference in VRAM-limited environments
Performance Benefits
Compared to the base model, RWKV-Qwen3-32B-Hybrid offers:
We extend our heartfelt gratitude to all contributors and supporters who made this experimental model possible.
Disclaimer
EXPERIMENTAL MODEL: This model is created purely for experimental and research purposes.
No Warranty: The creators make no guarantees regarding:
Model performance
Output quality
Suitability for any particular use case
Results accuracy
Users should thoroughly evaluate the model for their specific needs before deployment in any application.
License
Apache-2.0
Please refer to the repository for specific license information. As this is based on Qwen3-32B, users should also comply with the original Qwen model's licensing terms.
Contact and Support
For issues, questions, or contributions, please visit the GitHub repository or open an issue in the project's issue tracker.
