Instructions to use QuantFactory/Tulu-3.1-8B-SuperNova-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Tulu-3.1-8B-SuperNova-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Tulu-3.1-8B-SuperNova-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Tulu-3.1-8B-SuperNova-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Tulu-3.1-8B-SuperNova-GGUF",
	filename="Tulu-3.1-8B-SuperNova.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/Tulu-3.1-8B-SuperNova-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/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Tulu-3.1-8B-SuperNova-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/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Tulu-3.1-8B-SuperNova-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/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Tulu-3.1-8B-SuperNova-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/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Tulu-3.1-8B-SuperNova-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Tulu-3.1-8B-SuperNova-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/Tulu-3.1-8B-SuperNova-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/Tulu-3.1-8B-SuperNova-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/Tulu-3.1-8B-SuperNova-GGUF to start chatting

How to use QuantFactory/Tulu-3.1-8B-SuperNova-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Tulu-3.1-8B-SuperNova-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": "QuantFactory/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Tulu-3.1-8B-SuperNova-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/Tulu-3.1-8B-SuperNova-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Tulu-3.1-8B-SuperNova-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Tulu-3.1-8B-SuperNova-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Tulu-3.1-8B-SuperNova-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Tulu-3.1-8B-SuperNova-GGUF

This is quantized version of bunnycore/Tulu-3.1-8B-SuperNova created using llama.cpp

Original Model Card

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: arcee-ai/Llama-3.1-SuperNova-Lite
    parameters:
      weight: 1.0
  - model: allenai/Llama-3.1-Tulu-3-8B
    parameters:
      weight: 1.0
  - model: meditsolutions/Llama-3.1-MedIT-SUN-8B
    parameters:
      weight: 1.0
merge_method: linear
normalize: false
int8_mask: true
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	30.94
IFEval (0-Shot)	81.94
BBH (3-Shot)	32.50
MATH Lvl 5 (4-Shot)	24.32
GPQA (0-shot)	6.94
MuSR (0-shot)	8.69
MMLU-PRO (5-shot)	31.27

Downloads last month: 250

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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

Model tree for QuantFactory/Tulu-3.1-8B-SuperNova-GGUF

allenai/Llama-3.1-Tulu-3-8B

arcee-ai/Llama-3.1-SuperNova-Lite

meditsolutions/Llama-3.1-MedIT-SUN-8B

Merge model

this model

Paper for QuantFactory/Tulu-3.1-8B-SuperNova-GGUF

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 8

Evaluation results

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

81.940
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

32.500
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

24.320
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

6.940
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

8.690
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

31.270