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

Libraries

How to use QuantFactory/Llama-3-Instruct-8B-SimPO-ExPO-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-SimPO-ExPO-GGUF",
	filename="Llama-3-Instruct-8B-SimPO-ExPO.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-SimPO-ExPO-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-SimPO-ExPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Instruct-8B-SimPO-ExPO-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-SimPO-ExPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Instruct-8B-SimPO-ExPO-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-SimPO-ExPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-3-Instruct-8B-SimPO-ExPO-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-SimPO-ExPO-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3-Instruct-8B-SimPO-ExPO-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use QuantFactory/Llama-3-Instruct-8B-SimPO-ExPO-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-SimPO-ExPO-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-SimPO-ExPO-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio new

How to use QuantFactory/Llama-3-Instruct-8B-SimPO-ExPO-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-SimPO-ExPO-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-SimPO-ExPO-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-SimPO-ExPO-GGUF to start chatting

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

Lemonade

How to use QuantFactory/Llama-3-Instruct-8B-SimPO-ExPO-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Llama-3-Instruct-8B-SimPO-ExPO-GGUF-Q4_K_M

List all available models

lemonade list

Llama-3-Instruct-8B-SimPO-ExPO-GGUF

This is quantized version of chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO created using llama.cpp

Model Description

The extrapolated (ExPO) model based on princeton-nlp/Llama-3-Instruct-8B-SimPO and meta-llama/Meta-Llama-3-8B-Instruct, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper.

Specifically, we obtain this model by extrapolating (alpha = 0.3) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference.

This extrapolated model achieves the 40.6% win rate and 45.8% LC win rate on AlpacaEval 2.0, outperforming the original Llama-3-Instruct-8B-SimPO's 40.5% and 44.7%, respectively.

Evaluation Results

Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the official GitHub repo):

	Win Rate (Ori)	LC Win Rate (Ori)	Win Rate (+ ExPO)	LC Win Rate (+ ExPO)
`HuggingFaceH4/zephyr-7b-alpha`	6.7%	10.0%	10.6%	13.6%
`HuggingFaceH4/zephyr-7b-beta`	10.2%	13.2%	11.1%	14.0%
`berkeley-nest/Starling-LM-7B-alpha`	15.0%	18.3%	18.2%	19.5%
`Nexusflow/Starling-LM-7B-beta`	26.6%	25.8%	29.6%	26.4%
`snorkelai/Snorkel-Mistral-PairRM`	24.7%	24.0%	28.8%	26.4%
`RLHFlow/LLaMA3-iterative-DPO-final`	29.2%	36.0%	32.7%	37.8%
`internlm/internlm2-chat-1.8b`	3.8%	4.0%	5.2%	4.3%
`internlm/internlm2-chat-7b`	20.5%	18.3%	28.1%	22.7%
`internlm/internlm2-chat-20b`	36.1%	24.9%	46.2%	27.2%
`allenai/tulu-2-dpo-7b`	8.5%	10.2%	11.5%	11.7%
`allenai/tulu-2-dpo-13b`	11.2%	15.5%	15.6%	17.6%
`allenai/tulu-2-dpo-70b`	15.4%	21.2%	23.0%	25.7%

Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the official GitHub repo):

	Original	+ ExPO
`HuggingFaceH4/zephyr-7b-alpha`	6.85	6.87
`HuggingFaceH4/zephyr-7b-beta`	7.02	7.06
`berkeley-nest/Starling-LM-7B-alpha`	7.82	7.91
`Nexusflow/Starling-LM-7B-beta`	8.10	8.18
`snorkelai/Snorkel-Mistral-PairRM`	7.63	7.69
`RLHFlow/LLaMA3-iterative-DPO-final`	8.08	8.45
`internlm/internlm2-chat-1.8b`	5.17	5.26
`internlm/internlm2-chat-7b`	7.72	7.80
`internlm/internlm2-chat-20b`	8.13	8.26
`allenai/tulu-2-dpo-7b`	6.35	6.38
`allenai/tulu-2-dpo-13b`	7.00	7.26
`allenai/tulu-2-dpo-70b`	7.79	8.03

Downloads last month: 353

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-SimPO-ExPO-GGUF

Base model

chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO

Quantized

(5)

this model

Paper for QuantFactory/Llama-3-Instruct-8B-SimPO-ExPO-GGUF

Weak-to-Strong Extrapolation Expedites Alignment

Paper • 2404.16792 • Published Apr 25, 2024 • 11