Instructions to use arcee-ai/Arcee-SuperNova-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Arcee-SuperNova-v1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("arcee-ai/Arcee-SuperNova-v1-GGUF", dtype="auto")

llama-cpp-python

How to use arcee-ai/Arcee-SuperNova-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="arcee-ai/Arcee-SuperNova-v1-GGUF",
	filename="Arcee-SuperNova-v1-IQ1_M.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 arcee-ai/Arcee-SuperNova-v1-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-SuperNova-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Arcee-SuperNova-v1-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-SuperNova-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Arcee-SuperNova-v1-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-SuperNova-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf arcee-ai/Arcee-SuperNova-v1-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-SuperNova-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf arcee-ai/Arcee-SuperNova-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/arcee-ai/Arcee-SuperNova-v1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use arcee-ai/Arcee-SuperNova-v1-GGUF with Ollama:
```
ollama run hf.co/arcee-ai/Arcee-SuperNova-v1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use arcee-ai/Arcee-SuperNova-v1-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-SuperNova-v1-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-SuperNova-v1-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-SuperNova-v1-GGUF to start chatting

Pi new

How to use arcee-ai/Arcee-SuperNova-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf arcee-ai/Arcee-SuperNova-v1-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": "Arcee-SuperNova-v1-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use arcee-ai/Arcee-SuperNova-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Arcee-SuperNova-v1-GGUF:Q4_K_M
```

Lemonade

How to use arcee-ai/Arcee-SuperNova-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull arcee-ai/Arcee-SuperNova-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Arcee-SuperNova-v1-GGUF-Q4_K_M

List all available models

lemonade list

GGUF Quantizations for Arcee-SuperNova-v1

Arcee-SuperNova-v1 (70B) is a merged model built from multiple advanced training approaches. At its core is a distilled version of Llama-3.1-405B-Instruct into Llama-3.1-70B-Instruct, using out DistillKit to preserve instruction-following strengths while reducing size.

Alongside this, another Llama-3.1-70B model was instruction-tuned using synthetic data from our Evol-Kit pipeline, improving precision and adherence across diverse queries. Updates were integrated mid-epoch for smoother performance gains.

A third version underwent Direct Preference Optimization (DPO) to better align with human feedback. While its contribution was smaller, it helped refine final alignment.

The resulting Arcee-SuperNova combines all three, delivering strong human preference alignment and state-of-the-art instruction-following ability.

Model Details

Architecture Base: Llama-3.1-70B-Instruct
Parameter Count: 70B
License: llama3

Use Cases

General intelligence and instruction following
Serving as a base to be retrained over time using Reinforcement Learning from Human Feedback (RLHF)
Mathematical applications and queries

License

Arcee-SuperNova-v1 (70B) is released under the - llama3 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 Arcee-SuperNova-v1 (70B), please connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!

Downloads last month: 96

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for arcee-ai/Arcee-SuperNova-v1-GGUF

Base model

meta-llama/Llama-3.1-70B

Finetuned

meta-llama/Llama-3.1-70B-Instruct

Finetuned

arcee-ai/Arcee-SuperNova-v1

Quantized

(5)

this model