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
- Transformers
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
language:
- en
license: llama3
library_name: transformers
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
datasets:
- arcee-ai/EvolKit-20k
model-index:
- name: Llama-3.1-SuperNova-Lite
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 80.17
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 31.57
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 15.48
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 7.49
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.67
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 31.97
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
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 |