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---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-14B
model-index:
- name: Virtuoso-Small
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 79.35
name: strict accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Virtuoso-Small
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 50.4
name: normalized accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Virtuoso-Small
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 34.29
name: exact match
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Virtuoso-Small
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.52
name: acc_norm
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Virtuoso-Small
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 14.44
name: acc_norm
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Virtuoso-Small
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 46.57
name: accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Virtuoso-Small
name: Open LLM Leaderboard
---
<div align="center">
<img src="https://i.ibb.co/pXD6Bcv/SW2-U-g-QQLSH1-ZAbxhs-Iu-A.webp" alt="Virtuoso-Small" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
</div>
GGUF Available [Here](https://huggingface.co./arcee-ai/Virtuoso-Small-GGUF)
# 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](https://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, Medium and Large, 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](https://models.arcee.ai). For larger-scale or more demanding applications, consider Virtuoso-Medium or Virtuoso-Large.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_arcee-ai__Virtuoso-Small)
| 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|
|