README.md

---
base_model: microsoft/WizardLM-2-7B
inference: false
language:
- en
model_type: mistral
license: apache-2.0
tags:
- gguf
- mistral
---

## Description

This repo contains GGUF files for the original model.

### Files

- [WizardLM-2-7B_Q2_K.gguf](WizardLM-2-7B_Q2_K.gguf) (2.72 GB) - smallest, significant quality loss - not recommended for most purposes
- [WizardLM-2-7B_Q3_K_S.gguf](WizardLM-2-7B_Q3_K_S.gguf) (3.16 GB) - very small, high quality loss
- [WizardLM-2-7B_Q3_K_M.gguf](WizardLM-2-7B_Q3_K_M.gguf) (3.52 GB) - very small, high quality loss
- [WizardLM-2-7B_Q3_K_L.gguf](WizardLM-2-7B_Q3_K_L.gguf) (3.82 GB) - small, substantial quality loss
- [WizardLM-2-7B_Q4_K_S.gguf](WizardLM-2-7B_Q4_K_S.gguf) (4.14 GB) - small, greater quality loss
- [WizardLM-2-7B_Q4_K_M.gguf](WizardLM-2-7B_Q4_K_M.gguf) (4.37 GB) - medium, balanced quality - recommended
- [WizardLM-2-7B_Q5_K_S.gguf](WizardLM-2-7B_Q5_K_S.gguf) (5 GB) - large, low quality loss - recommended
- [WizardLM-2-7B_Q5_K_M.gguf](WizardLM-2-7B_Q5_K_M.gguf) (5.13 GB) - large, very low quality loss - recommended
- [WizardLM-2-7B_Q6_K.gguf](WizardLM-2-7B_Q6_K.gguf) (5.94 GB) - very large, extremely low quality loss
- [WizardLM-2-7B_Q8_0.gguf](WizardLM-2-7B_Q8_0.gguf) (7.7 GB) - very large, extremely low quality loss - not recommended

## Original model description

We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, 
which have improved performance on complex chat, multilingual, reasoning and agent. 
New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.

- WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works 
and consistently outperforms all the existing state-of-the-art opensource models.
- WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. 
- WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.

For more  details of WizardLM-2 please read our [release blog post](https://wizardlm.github.io/WizardLM2) and  upcoming paper.


## Model Details

* **Model name**: WizardLM-2 7B
* **Developed by**: WizardLM@Microsoft AI
* **Base model**: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
* **Parameters**: 7B
* **Language(s)**: Multilingual
* **Blog**: [Introducing WizardLM-2](https://wizardlm.github.io/WizardLM2)
* **Repository**: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM)
* **Paper**: WizardLM-2 (Upcoming)
* **License**: Apache2.0



## Model Capacities


**MT-Bench**

We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. 
The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. 
Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.

<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
</p>


**Human Preferences Evaluation**

We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual. 
We report the win:loss rate without tie:

- WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314.
- WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat.
- WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta.

<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
</p>





## Method Overview
We built a **fully AI powered synthetic training system** to train WizardLM-2 models, please refer to our [blog](https://wizardlm.github.io/WizardLM2) for more details of this system.

<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
</p>





## Usage

❗<b>Note for model system prompts usage:</b>


<b>WizardLM-2</b>  adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:

```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, 
detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>
USER: Who are you? ASSISTANT: I am WizardLM.</s>......
```

<b> Inference WizardLM-2 Demo Script</b>

We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github.