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

Libraries
PEFT
How to use QuantFactory/UltraLlama-3.1-8B-GGUF with PEFT:
```
Task type is invalid.
```

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

# !pip install llama-cpp-python

from llama_cpp import Llama

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

Use Docker

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

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

Unsloth Studio new

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.UltraLlama-3.1-8B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/UltraLlama-3.1-8B-GGUF

This is quantized version of mlabonne/UltraLlama-3.1-8B created using llama.cpp

Original Model Card

UltraLlama-3.1-8B

Llama 3.1 8B model trained on a high-quality magpie dataset to measure its quality:

Model	MMLU	Hellaswag	ARC-C	GSM8K	TruthfulQA	Winogrande	IFEval	MMLU-Pro	MATH Lvl 5	GPQA	MuSR	BBH
Meta-Llama-3.1-8B	65.28	82.09	59.22	51.02	45.15	77.58	11.45	32.74	4.38	30.93	7.98	46.77
Meta-Llama-3-8B-Instruct	65.60	78.79	61.95	75.28	51.66	75.77	47.43	5.87	7.95	30.11	37.92	49.04
FineLlama-3.1-8B	62.22	80.30	55.55	51.02	49.51	75.30	1.68	30.90	4.12	27.45	35.77	44.22
UltraLlama-3.1-8B	54.36	74.98	55.46	51.10	49.93	72.05	10.19	26.63	3.08	25.47	40.93	42.44

Magpie underperforms FineTome-100k. The quality looks okay, but not as good as high-quality open-source datasets.

Downloads last month: 27

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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Model tree for QuantFactory/UltraLlama-3.1-8B-GGUF

Base model

unsloth/Meta-Llama-3.1-8B

Adapter

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