Instructions to use arcee-ai/Llama-3.1-SuperNova-Lite-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Llama-3.1-SuperNova-Lite-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("arcee-ai/Llama-3.1-SuperNova-Lite-GGUF", dtype="auto")

llama-cpp-python

How to use arcee-ai/Llama-3.1-SuperNova-Lite-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="arcee-ai/Llama-3.1-SuperNova-Lite-GGUF",
	filename="supernova-lite-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 Settings

llama.cpp

How to use arcee-ai/Llama-3.1-SuperNova-Lite-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/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Llama-3.1-SuperNova-Lite-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/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf arcee-ai/Llama-3.1-SuperNova-Lite-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/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf arcee-ai/Llama-3.1-SuperNova-Lite-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/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf arcee-ai/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M

Use Docker

docker model run hf.co/arcee-ai/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use arcee-ai/Llama-3.1-SuperNova-Lite-GGUF with Ollama:
```
ollama run hf.co/arcee-ai/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
```

Unsloth Studio

How to use arcee-ai/Llama-3.1-SuperNova-Lite-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/Llama-3.1-SuperNova-Lite-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/Llama-3.1-SuperNova-Lite-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/Llama-3.1-SuperNova-Lite-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use arcee-ai/Llama-3.1-SuperNova-Lite-GGUF with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
```

Lemonade

How to use arcee-ai/Llama-3.1-SuperNova-Lite-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull arcee-ai/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.1-SuperNova-Lite-GGUF-Q4_K_M

List all available models

lemonade list

Overview

Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture. It is a distilled version of the larger Llama-3.1-405B-Instruct model, leveraging offline logits extracted from the 405B parameter variant. This 8B variation of Llama-3.1-SuperNova maintains high performance while offering exceptional instruction-following capabilities and domain-specific adaptability.

The model was trained using a state-of-the-art distillation pipeline and an instruction dataset generated with EvolKit, ensuring accuracy and efficiency across a wide range of tasks. For more information on its training, visit blog.arcee.ai.

Llama-3.1-SuperNova-Lite excels in both benchmark performance and real-world applications, providing the power of large-scale models in a more compact, efficient form ideal for organizations seeking high performance with reduced resource requirements.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	29.73
IFEval (0-Shot)	80.17
BBH (3-Shot)	31.57
MATH Lvl 5 (4-Shot)	15.48
GPQA (0-shot)	7.49
MuSR (0-shot)	11.67
MMLU-PRO (5-shot)	31.97

Downloads last month: 635

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for arcee-ai/Llama-3.1-SuperNova-Lite-GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(645)

this model

Dataset used to train arcee-ai/Llama-3.1-SuperNova-Lite-GGUF

Collection including arcee-ai/Llama-3.1-SuperNova-Lite-GGUF

Flagship model quants

Collection

Quantized versions of Arcee's latest flagship models • 11 items • Updated Dec 27, 2024 • 8

Evaluation results

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

80.170
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

31.570
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

15.480
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

7.490
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

11.670
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

31.970