Instructions to use QuantFactory/Virtuoso-Small-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Virtuoso-Small-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Virtuoso-Small-GGUF",
	filename="Virtuoso-Small.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Virtuoso-Small-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Virtuoso-Small-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Virtuoso-Small-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Virtuoso-Small-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Virtuoso-Small-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Virtuoso-Small-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Virtuoso-Small-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Virtuoso-Small-GGUF to start chatting

Pi new

How to use QuantFactory/Virtuoso-Small-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/Virtuoso-Small-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Virtuoso-Small-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Virtuoso-Small-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/Virtuoso-Small-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/Virtuoso-Small-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Virtuoso-Small-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Virtuoso-Small-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Virtuoso-Small-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Virtuoso-Small-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Virtuoso-Small-GGUF

This is quantized version of arcee-ai/Virtuoso-Small created using llama.cpp

Original Model Card

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

Downloads last month: 158

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Model tree for QuantFactory/Virtuoso-Small-GGUF

Base model

Qwen/Qwen2.5-14B

Quantized

(77)

this model

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

79.350
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

50.400
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

34.290
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

11.520
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

14.440
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

46.570