Virtuoso-Small-RK3588-1.1.2
This version of Virtuoso-Small has been converted to run on the RK3588 NPU using ['w8a8', 'w8a8_g128', 'w8a8_g256', 'w8a8_g512'] quantization. This model has been optimized with the following LoRA:
Compatible with RKLLM version: 1.1.4
Useful links:
Pretty much anything by these folks: marty1885 and happyme531
Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit
Original Model Card for base model, Virtuoso-Small, below:
GGUF Available Here
Virtuoso-Small
Virtuoso-Small is the debut public release of the Virtuoso series of models by Arcee.ai, designed to bring cutting-edge generative AI capabilities to organizations and developers in a compact, efficient form. With 14 billion parameters, Virtuoso-Small is an accessible entry point for high-quality instruction-following, complex reasoning, and business-oriented generative AI tasks. Its larger siblings, Virtuoso-Medium and Virtuoso-Large, offer even greater capabilities and are available via API at models.arcee.ai.
Key Features
- Compact and Efficient: With 14 billion parameters, Virtuoso-Small provides a high-performance solution optimized for smaller hardware configurations without sacrificing quality.
- Business-Oriented: Tailored for use cases such as customer support, content creation, and technical assistance, Virtuoso-Small meets the demands of modern enterprises.
- Scalable Ecosystem: Part of the Virtuoso series, Virtuoso-Small is fully interoperable with its larger siblings, Forte and Prime, enabling seamless scaling as your needs grow.
Deployment Options
Virtuoso-Small is available under the Apache-2.0 license and can be deployed locally or accessed through an API at models.arcee.ai. For larger-scale or more demanding applications, consider Virtuoso-Forte or Virtuoso-Prime.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 39.43 |
| IFEval (0-Shot) | 79.35 |
| BBH (3-Shot) | 50.40 |
| MATH Lvl 5 (4-Shot) | 34.29 |
| GPQA (0-shot) | 11.52 |
| MuSR (0-shot) | 14.44 |
| MMLU-PRO (5-shot) | 46.57 |
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Model tree for c01zaut/Virtuoso-Small-rk3588-1.1.4
Base model
Qwen/Qwen2.5-14BEvaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard79.350
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard50.400
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard34.290
- acc_norm on GPQA (0-shot)Open LLM Leaderboard11.520
- acc_norm on MuSR (0-shot)Open LLM Leaderboard14.440
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard46.570