Instructions to use QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF",
	filename="Llama-3-Instruct-8B-SPPO-Iter3.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-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-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-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-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-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-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-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-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-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/Llama-3-Instruct-8B-SPPO-Iter3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-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-Instruct-8B-SPPO-Iter3-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-Instruct-8B-SPPO-Iter3-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-Instruct-8B-SPPO-Iter3-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-Instruct-8B-SPPO-Iter3-GGUF-Q4_K_M

List all available models

lemonade list

Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)

Llama-3-Instruct-8B-SPPO-Iter3-GGUF

This is quantized version of UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 created using llama.cpp

Model Description

This model was developed using Self-Play Preference Optimization at iteration 3, based on the meta-llama/Meta-Llama-3-8B-Instruct architecture as starting point. We utilized the prompt sets from the openbmb/UltraFeedback dataset, splited to 3 parts for 3 iterations by snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset. All responses used are synthetic.

Links to Other Models

Model Description

Model type: A 8B parameter GPT-like model fine-tuned on synthetic datasets.
Language(s) (NLP): Primarily English
License: Apache-2.0
Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct

AlpacaEval Leaderboard Evaluation Results

Model	LC. Win Rate	Win Rate	Avg. Length
Llama-3-8B-SPPO Iter1	31.73	31.74	1962
Llama-3-8B-SPPO Iter2	35.15	35.98	2021
Llama-3-8B-SPPO Iter3	38.77	39.85	2066

Open LLM Leaderboard Evaluation Results

Results are reported by using lm-evaluation-harness v0.4.1

	arc_challenge	truthfulqa_mc2	winogrande	gsm8k	hellaswag	mmlu	average
Llama-3-8B-SPPO Iter1	63.82	54.96	76.40	75.44	79.80	65.65	69.35
Llama-3-8B-SPPO Iter2	64.93	56.48	76.87	75.13	80.39	65.67	69.91
Llama-3-8B-SPPO Iter3	65.19	58.04	77.11	74.91	80.86	65.60	70.29

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
eta: 1000
per_device_train_batch_size: 8
gradient_accumulation_steps: 1
seed: 42
distributed_type: deepspeed_zero3
num_devices: 8
optimizer: RMSProp
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_train_epochs: 6.0 (stop at epoch=1.0)

Model Citation

@misc{wu2024self,
      title={Self-Play Preference Optimization for Language Model Alignment}, 
      author={Wu, Yue and Sun, Zhiqing and Yuan, Huizhuo and Ji, Kaixuan and Yang, Yiming and Gu, Quanquan},
      year={2024},
      eprint={2405.00675},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Downloads last month: 198

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/Llama-3-Instruct-8B-SPPO-Iter3-GGUF

Base model

UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3

Quantized

(19)

this model

Dataset used to train QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF

Paper for QuantFactory/Llama-3-Instruct-8B-SPPO-Iter3-GGUF

Self-Play Preference Optimization for Language Model Alignment

Paper • 2405.00675 • Published May 1, 2024 • 28