README.md

---
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](https://huggingface.co/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.
<img src="figures_main_exp.png" alt="stats" style="zoom: 40%;" />