Instructions to use QuantFactory/Llama-3-Smaug-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Llama-3-Smaug-8B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Llama-3-Smaug-8B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Llama-3-Smaug-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Llama-3-Smaug-8B-GGUF",
	filename="Llama-3-Smaug-8B.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-Smaug-8B-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-Smaug-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Smaug-8B-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-Smaug-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Smaug-8B-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-Smaug-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-3-Smaug-8B-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-Smaug-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3-Smaug-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-3-Smaug-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Llama-3-Smaug-8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama-3-Smaug-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Llama-3-Smaug-8B-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-Smaug-8B-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-Smaug-8B-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-Smaug-8B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Llama-3-Smaug-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama-3-Smaug-8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama-3-Smaug-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-3-Smaug-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-Smaug-8B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Llama-3-Smaug-8B-GGUF

This is quantized version of abacusai/Llama-3-Smaug-8B created using llama.cpp

Original Model Card

Llama-3-Smaug-8B

Built with Meta Llama 3

This model was built using the Smaug recipe for improving performance on real world multi-turn conversations applied to meta-llama/Meta-Llama-3-8B-Instruct.

Model Description

Developed by: Abacus.AI
License: https://llama.meta.com/llama3/license/
Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct.

Evaluation

MT-Bench

########## First turn ##########
                   score
model             turn
Llama-3-Smaug-8B 1   8.77500
Meta-Llama-3-8B-Instruct 1   8.31250
########## Second turn ##########
                   score
model             turn
Meta-Llama-3-8B-Instruct 2   7.8875 
Llama-3-Smaug-8B 2   7.8875
########## Average ##########
                 score
model
Llama-3-Smaug-8B  8.331250
Meta-Llama-3-8B-Instruct 8.10

Model	First turn	Second Turn	Average
Llama-3-Smaug-8B	8.78	7.89	8.33
Llama-3-8B-Instruct	8.31	7.89	8.10

This version of Smaug uses new techniques and new data compared to Smaug-72B, and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228.

Downloads last month: 508

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Datasets used to train QuantFactory/Llama-3-Smaug-8B-GGUF

Paper for QuantFactory/Llama-3-Smaug-8B-GGUF

Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive

Paper • 2402.13228 • Published Feb 20, 2024 • 3