Instructions to use arcee-ai/Virtuoso-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Virtuoso-Large with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="arcee-ai/Virtuoso-Large")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Virtuoso-Large")
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Virtuoso-Large")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use arcee-ai/Virtuoso-Large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/Virtuoso-Large"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Virtuoso-Large",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arcee-ai/Virtuoso-Large

SGLang

How to use arcee-ai/Virtuoso-Large with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "arcee-ai/Virtuoso-Large" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Virtuoso-Large",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "arcee-ai/Virtuoso-Large" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Virtuoso-Large",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/Virtuoso-Large with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Virtuoso-Large
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Virtuoso-Large (72B) is our most powerful and versatile general-purpose model, designed to excel at handling complex and varied tasks across domains. With state-of-the-art performance, it offers unparalleled capability for nuanced understanding, contextual adaptability, and high accuracy.

Model Details

Architecture Base: Qwen2.5-72B
Parameter Count: 72B
License: Qwen's Tongyi License

Use Cases

Advanced content creation, such as technical writing and creative storytelling
Data summarization and report generation for cross-functional domains
Detailed knowledge synthesis and deep-dive insights from diverse datasets
Multilingual support for international operations and communications

Quantizations

GGUF format available here

License

Virtuoso-Large (72B) is released under the qwen license. You are free to use, modify, and distribute this model in both commercial and non-commercial applications, subject to the terms and conditions of the license.

If you have questions or would like to share your experiences using Virtuoso-Large (72B), please feel free to connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!

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Model size

73B params

Tensor type

BF16

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