Athene-V2-Chat-4.65bpw-h6-exl2
| Property | Value |
|---|---|
| Base Model | Qwen/Qwen2.5-72B-Instruct |
| License | Nexusflow Research License |
| Context Length | 32K tokens |
| Quantization | 4.65 bits per weight |
What is Athene-V2-Chat-4.65bpw-h6-exl2?
Athene-V2-Chat-4.65bpw-h6-exl2 is a highly optimized quantized version of the Athene-V2-Chat model, designed to rival GPT-4's performance while maintaining efficient resource usage. This model represents a significant advancement in efficient AI deployment, offering full 32K context support with Q4 cache on systems with 48GB VRAM.
Implementation Details
The model is built upon the Qwen2.5-72B-Instruct architecture and has been fine-tuned through RLHF (Reinforcement Learning from Human Feedback). The implementation uses the transformers library and maintains compatibility with the Qwen2.5 chat template, making it easily integrable into existing systems.
- EXL2 quantization at 4.65 bits per weight
- Optimized for 48GB VRAM systems
- 32K context length support
- Compatible with transformers library
Core Capabilities
- Advanced chat functionality
- Strong performance in mathematical computations
- Exceptional coding abilities
- Matches GPT-4 across various benchmarks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient quantization while maintaining GPT-4 level performance, making it accessible for systems with 48GB VRAM while supporting extensive context lengths.
Q: What are the recommended use cases?
The model excels in chat applications, mathematical problem-solving, and coding tasks. It's particularly suitable for deployments where efficient resource usage is crucial while maintaining high performance standards.
