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

Libraries

How to use QuantFactory/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="QuantFactory/Llama-3.1-SuperNova-Lite-GGUF",
	filename="Llama-3.1-SuperNova-Lite.Q2_K.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 QuantFactory/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 QuantFactory/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/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 QuantFactory/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/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 QuantFactory/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/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 QuantFactory/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3.1-SuperNova-Lite-GGUF:Q4_K_M

Use Docker

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

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

Unsloth Studio

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

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/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

QuantFactory/Llama-3.1-SuperNova-Lite-GGUF

This is quantized version of arcee-ai/Llama-3.1-SuperNova-Lite created using llama.cpp

Original Model Card

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.

Evaluations

We will be submitting this model to the OpenLLM Leaderboard for a more conclusive benchmark - but here are our internal benchmarks using the main branch of lm evaluation harness:

Benchmark	SuperNova-Lite	Llama-3.1-8b-Instruct
IF_Eval	81.1	77.4
MMLU Pro	38.7	37.7
TruthfulQA	64.4	55.0
BBH	51.1	50.6
GPQA	31.2	29.02

The script used for evaluation can be found inside this repository under /eval.sh, or click here

note

This readme will be edited regularly on September 10, 2024 (the day of release). After the final readme is in place we will remove this note.

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for QuantFactory/Llama-3.1-SuperNova-Lite-GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

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

Quantized

(644)

this model

QuantFactory
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Llama-3.1-SuperNova-Lite-GGUF