Instructions to use QuantFactory/Eurus-7b-sft-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/Eurus-7b-sft-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/Eurus-7b-sft-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Eurus-7b-sft-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/Eurus-7b-sft-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Eurus-7b-sft-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-sft-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Eurus-7b-sft-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-sft-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Eurus-7b-sft-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use QuantFactory/Eurus-7b-sft-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-sft-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-sft-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

Ollama
How to use QuantFactory/Eurus-7b-sft-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Eurus-7b-sft-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Eurus-7b-sft-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Eurus-7b-sft-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat

Eurus-7b-sft-GGUF / README.md

munish0838

Update README.md

c0a00de verified over 2 years ago

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metadata

license: apache-2.0
datasets:
  - openbmb/UltraInteract_sft
  - stingning/ultrachat
  - openchat/openchat_sharegpt4_dataset
  - Open-Orca/OpenOrca
tags:
  - reasoning
pipeline_tag: text-generation

Eurus-7b-sft-GGUF

This is quantized version of openbmb/Eurus-7b-sft created using llama.cpp

Model Description

Eurus-7B-SFT is fine-tuned from Mistral-7B on all correct actions in UltraInteract, mixing a small proportion of UltraChat, ShareGPT, and OpenOrca examples.

It achieves better performance than other open-source models of similar sizes and even outperforms specialized models in corresponding domains in many cases.

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.