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

Libraries

How to use QuantFactory/Llama-3.1-8B-Ultra-Instruct-GGUF with Transformers:

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

llama-cpp-python

How to use QuantFactory/Llama-3.1-8B-Ultra-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Llama-3.1-8B-Ultra-Instruct-GGUF",
	filename="Llama-3.1-8B-Ultra-Instruct.Q2_K.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 QuantFactory/Llama-3.1-8B-Ultra-Instruct-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-3.1-8B-Ultra-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3.1-8B-Ultra-Instruct-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-3.1-8B-Ultra-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3.1-8B-Ultra-Instruct-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-3.1-8B-Ultra-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-3.1-8B-Ultra-Instruct-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-3.1-8B-Ultra-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3.1-8B-Ultra-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-3.1-8B-Ultra-Instruct-GGUF:Q4_K_M

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

Unsloth Studio new

How to use QuantFactory/Llama-3.1-8B-Ultra-Instruct-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-3.1-8B-Ultra-Instruct-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-3.1-8B-Ultra-Instruct-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-3.1-8B-Ultra-Instruct-GGUF to start chatting

Pi new

How to use QuantFactory/Llama-3.1-8B-Ultra-Instruct-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/Llama-3.1-8B-Ultra-Instruct-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/Llama-3.1-8B-Ultra-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Llama-3.1-8B-Ultra-Instruct-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/Llama-3.1-8B-Ultra-Instruct-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/Llama-3.1-8B-Ultra-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

How to use QuantFactory/Llama-3.1-8B-Ultra-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-3.1-8B-Ultra-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.1-8B-Ultra-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Llama-3.1-8B-Ultra-Instruct-GGUF

This is quantized version of Dampfinchen/Llama-3.1-8B-Ultra-Instruct 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 DARE TIES merge method using NousResearch/Meta-Llama-3.1-8B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Sao10K/Llama-3.1-8B-Stheno-v3.4
    parameters:
      weight: 0.2
      density: 0.5
  - model: akjindal53244/Llama-3.1-Storm-8B
    parameters:
      weight: 0.5
      density: 0.5
  - model: nbeerbower/llama3.1-gutenberg-8B
    parameters:
      weight: 0.3
      density: 0.5
  - model: nbeerbower/llama3.1-airoboros3.2-QDT-8B
    parameters:
      weight: 0.2
      density: 0.5
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3.1-8B
dtype: bfloat16
name: Llama-3.1-8B-Ultra-Instruct

Use Llama 3 Instruct prompt template. Use with caution, I'm not responsible for what you do with it. All credits and thanks go to the creators of the fine tunes I've merged. In my own tests and on HF Eval it performs very well for a 8B model and I can recommend it. High quality quants by Bartowski: https://huggingface.co/bartowski/Llama-3.1-8B-Ultra-Instruct-GGUF