|
--- |
|
license: apache-2.0 |
|
--- |
|
|
|
|
|
|
|
<p style="font-size:20px;" align="center"> |
|
🏠 <a href="https://wizardlm.github.io/WizardLM2" target="_blank">WizardLM-2 Release Blog</a> </p> |
|
<p align="center"> |
|
🤗 <a href="https://huggingface.co/collections/microsoft/wizardlm-2-661d403f71e6c8257dbd598a" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/victorsungo/WizardLM/tree/main/WizardLM-2" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br> |
|
</p> |
|
<p align="center"> |
|
👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> |
|
</p> |
|
|
|
|
|
## News 🔥🔥🔥 [2024/04/15] |
|
|
|
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 8x22B |
|
* **Developed by**: WizardLM@Microsoft AI |
|
* **Model type**: Mixture of Experts (MoE) |
|
* **Base model**: [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1) |
|
* **Parameters**: 141B |
|
* **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. |
|
|
|
|
|
|
|
|
|
|
