RWKV7-Goose-World3-2.9B-HF-GGUF
| Property | Value |
|---|---|
| Parameter Count | 2.9B |
| License | Apache-2.0 |
| Training Data | World v3 (3.119T tokens) |
| Repository | flash-linear-attention |
| Tokenizer | RWKV World tokenizer (65,536 vocab) |
What is RWKV7-Goose-World3-2.9B-HF-GGUF?
RWKV7-Goose-World3 is a sophisticated language model implementing the RWKV7 architecture with flash-linear attention format. Developed by the RWKV Project under LF AI & Data Foundation, this model offers multiple quantization options to accommodate various hardware configurations and memory constraints.
Implementation Details
The model is available in several formats, including BF16, F16, and various quantized versions (Q4_K, Q6_K, Q8_0, IQ3). It was trained using bfloat16 with a learning rate ranging from 4e-4 to 1e-5 using a "delayed" cosine decay and weight decay of 0.1.
- Multiple quantization options for different hardware requirements
- Optimized versions for both GPU and CPU inference
- Flexible deployment options from high-precision to ultra-low-memory configurations
Core Capabilities
- High-performance text generation with 2.9B parameters
- Efficient memory usage through various quantization options
- Support for both CPU and GPU inference
- Comprehensive vocabulary with 65,536 tokens
- Compatible with popular deep learning frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its flexible deployment options through multiple quantization formats, allowing users to balance between performance and resource usage. The flash-linear attention architecture provides efficient processing while maintaining model quality.
Q: What are the recommended use cases?
The model is suitable for various text generation tasks. For high-performance systems with BF16 support, use the BF16 version. For memory-constrained environments, the quantized versions (Q4_K, IQ3) provide efficient alternatives while maintaining reasonable performance.
