Instructions to use QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF",
	filename="ArliAI-Llama-3-8B-Instruct-DPO-v0.2.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-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/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-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/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-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/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-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/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M

Ollama
How to use QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-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/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-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/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-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/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF

This is quantized version of OwenArli/ArliAI-Llama-3-8B-Instruct-DPO-v0.2 created using llama.cpp

Model Description

Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct

Realized a tokenization mistake with the previous DPO model. So this is now a new version testing out DPO training on the following dataset:

https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k

The open LLM results are really BAD lol. Something with this dataset is disagreeing with llama 3?

Instruct format:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Downloads last month: 97

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF

Base model

OwenArli/ArliAI-Llama-3-8B-Instruct-DPO-v0.2

Quantized

(5)

this model

Collection including QuantFactory/ArliAI-Llama-3-8B-Instruct-DPO-v0.2-GGUF

ArliAI-Llama-3

Collection

Quantizations of ArliAI-Llama-3 models • 15 items • Updated Oct 3, 2024 • 2