Instructions to use QuantFactory/Eurus-7b-kto-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Eurus-7b-kto-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Eurus-7b-kto-GGUF",
	filename="Eurus-7b-kto.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/Eurus-7b-kto-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/Eurus-7b-kto-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Eurus-7b-kto-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/Eurus-7b-kto-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Eurus-7b-kto-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/Eurus-7b-kto-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Eurus-7b-kto-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/Eurus-7b-kto-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Eurus-7b-kto-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Eurus-7b-kto-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Eurus-7b-kto-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Eurus-7b-kto-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/Eurus-7b-kto-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Eurus-7b-kto-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Eurus-7b-kto-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Eurus-7b-kto-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Eurus-7b-kto-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/Eurus-7b-kto-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/Eurus-7b-kto-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/Eurus-7b-kto-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Eurus-7b-kto-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Eurus-7b-kto-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Eurus-7b-kto-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Eurus-7b-kto-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Eurus-7b-kto-GGUF-Q4_K_M

List all available models

lemonade list

openbmb/Eurus-7b-kto-GGUF

This is quantized version of openbmb/Eurus-7b-kto

Model Description

Eurus-7B-KTO is KTO fine-tuned from Eurus-7B-SFT on all multi-turn trajectory pairs in UltraInteract and all pairs in UltraFeedback.

It achieves the best overall performance among open-source models of similar sizes and even outperforms specialized models in corresponding domains in many cases. Notably, Eurus-7B-KTO outperforms baselines that are 5× larger.

Usage

We apply tailored prompts for coding and math, consistent with UltraInteract data formats:

Coding

[INST] Write Python code to solve the task:
{Instruction} [/INST]

Math-CoT

[INST] Solve the following math problem step-by-step.
Simplify your answer as much as possible. Present your final answer as \\boxed{Your Answer}.
{Instruction} [/INST]

Math-PoT

[INST] Tool available:
[1] Python interpreter
When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.
Solve the following math problem step-by-step.
Simplify your answer as much as possible.
{Instruction} [/INST]

Evaluation

Eurus, both the 7B and 70B variants, achieve the best overall performance among open-source models of similar sizes. Eurus even outperforms specialized models in corresponding domains in many cases. Notably, Eurus-7B outperforms baselines that are 5× larger, and Eurus-70B achieves better performance than GPT-3.5 Turbo.
Preference learning with UltraInteract can further improve performance, especially in math and the multi-turn ability.

Downloads last month: 344

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Eurus-7b-kto-GGUF

Base model

openbmb/Eurus-7b-kto

Quantized

(3)

this model

Datasets used to train QuantFactory/Eurus-7b-kto-GGUF

Paper for QuantFactory/Eurus-7b-kto-GGUF

KTO: Model Alignment as Prospect Theoretic Optimization

Paper • 2402.01306 • Published Feb 2, 2024 • 22