RWKV/v6-Finch-14B-HF
RWKV/v6-Finch-14B-HF is a 14 billion parameter model from the RWKV architecture family, designed for compatibility with Hugging Face transformers. This model demonstrates improved performance across various benchmarks, including MMLU, ARC, and HellaSwag, compared to its predecessors. It is suitable for general language generation tasks and can be deployed on both CPU and GPU environments.
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RWKV v6 Finch 14B: A Hugging Face Compatible Model
RWKV/v6-Finch-14B-HF is a 14 billion parameter model representing the latest iteration in the RWKV architecture, specifically engineered for seamless integration with the Hugging Face transformers library. This model builds upon previous versions, offering enhanced capabilities for a range of natural language processing tasks.
Key Capabilities and Improvements
- Hugging Face Compatibility: Designed to work directly with
AutoModelForCausalLMandAutoTokenizerfrom the Hugging Face transformers library, simplifying deployment and usage. - Performance Gains: Demonstrates notable improvements over earlier RWKV models like Eagle 7B and Finch 7B across several key benchmarks:
- MMLU: Achieves 56.05%, a significant increase from 30.86% (Eagle 7B) and 41.70% (Finch 7B).
- ARC: Scores 46.33%, up from 39.59% (Eagle 7B) and 41.47% (Finch 7B).
- HellaSwag: Reaches 57.69%, improving on 53.09% (Eagle 7B) and 55.96% (Finch 7B).
- Truthful QA: Shows an increase to 39.27%.
- Winogrande: Improves to 74.43%.
- Flexible Deployment: Supports inference on both CPU and GPU, with examples provided for single and batch inference.
Good For
- Developers seeking a performant 14B parameter model compatible with the Hugging Face ecosystem.
- Applications requiring general text generation and understanding, benefiting from improved benchmark scores.
- Experimentation with the RWKV architecture within a familiar transformer framework.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.