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

Libraries

How to use arcee-ai/Arcee-Nova-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="arcee-ai/Arcee-Nova-GGUF",
	filename="Arcee-Nova-Alpha-GGUF.IQ1_M-00001-of-00008.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use arcee-ai/Arcee-Nova-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf arcee-ai/Arcee-Nova-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Arcee-Nova-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 arcee-ai/Arcee-Nova-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Arcee-Nova-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 arcee-ai/Arcee-Nova-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf arcee-ai/Arcee-Nova-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 arcee-ai/Arcee-Nova-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf arcee-ai/Arcee-Nova-GGUF:Q4_K_M

Use Docker

docker model run hf.co/arcee-ai/Arcee-Nova-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use arcee-ai/Arcee-Nova-GGUF with Ollama:
```
ollama run hf.co/arcee-ai/Arcee-Nova-GGUF:Q4_K_M
```

Unsloth Studio

How to use arcee-ai/Arcee-Nova-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 arcee-ai/Arcee-Nova-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 arcee-ai/Arcee-Nova-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for arcee-ai/Arcee-Nova-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use arcee-ai/Arcee-Nova-GGUF with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Arcee-Nova-GGUF:Q4_K_M
```

Lemonade

How to use arcee-ai/Arcee-Nova-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull arcee-ai/Arcee-Nova-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Arcee-Nova-GGUF-Q4_K_M

List all available models

lemonade list

Arcee-Nova is our highest performing open source model. Evaluated on the same stack as the OpenLLM Leaderboard 2.0, making it the top-performing open source model tested on that stack. Its performance approaches that of GPT-4 from May 2023, marking a significant milestone.

Nova is a merge of Qwen2-72B-Instruct with a custom model tuned on a generalist dataset mixture.

Chat with Arcee-Nova here

Capabilities and Use Cases

Arcee-Nova excels across a wide range of language tasks, demonstrating particular strength in:

Reasoning: Solving complex problems and drawing logical conclusions.
Creative Writing: Generating engaging and original content across various genres.
Coding: Assisting with programming tasks, from code generation to debugging.
General Language Understanding: Comprehending and generating human-like text in diverse contexts.

Business Applications

Arcee-Nova can be applied to various business tasks:

Customer Service: Implement sophisticated chatbots and virtual assistants.
Content Creation: Generate high-quality written content for marketing and documentation.
Software Development: Accelerate coding processes and improve code quality.
Data Analysis: Enhance data interpretation and generate insightful reports.
Research and Development: Assist in literature reviews and hypothesis generation.
Legal and Compliance: Automate contract analysis and regulatory compliance checks.
Education and Training: Create adaptive learning systems and intelligent tutoring programs.

Evaluations

Acknowledgments

We extend our gratitude to the open source AI community, whose collective efforts have paved the way for Arcee-Nova. Their commitment to transparency and collaboration continues to drive innovation. We also would like to extend our thanks to the Qwen team - without Qwen2-72B this would not be possible.

Future Directions

As we release Arcee-Nova to the public, we look forward to seeing how researchers, developers, and businesses will leverage its capabilities. We remain committed to advancing open source AI technology and invite the community to explore, contribute, and build upon Arcee-Nova.

Note: This README was written with assistance from Arcee-Nova.

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GGUF

Model size

73B params

Architecture

qwen2

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