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.

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

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

Atomic Chat

munish0838 commited on Jun 4, 2024

Commit

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1 Parent(s): 3428061

Create README.md

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+---
+language:
+- en
+license: llama3
+pipeline_tag: text-generation
+base_model: chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO
+---
+# Llama-3-Instruct-8B-SimPO-ExPO-GGUF
+This is quantized version of [chujiezheng/Llama-3-Instruct-8B-SimPO-ExPO](https://huggingface.co/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`](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO) and [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), as in the "[Weak-to-Strong Extrapolation Expedites Alignment](https://arxiv.org/abs/2404.16792)" 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](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_alpaca)):
+|                                      | 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](https://github.com/chujiezheng/LLM-Extrapolation/tree/main/results_mtbench)):
+|                                      | 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** |