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license: apache-2.0 |
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<p style="font-size:20px;" align="center"> |
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🏠 <a href="https://wizardlm.github.io/WizardLM2" target="_blank">WizardLM-2 Release Blog</a> </p> |
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<p align="center"> |
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🤗 <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> |
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</p> |
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<p align="center"> |
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👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> |
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</p> |
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## See [here](https://huggingface.co/lucyknada/microsoft_WizardLM-2-7B) for the WizardLM-2-7B re-upload. |
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## News 🔥🔥🔥 [2024/04/15] |
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We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models, |
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which have improved performance on complex chat, multilingual, reasoning and agent. |
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New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B. |
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- WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works |
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and consistently outperforms all the existing state-of-the-art opensource models. |
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- WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. |
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- WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models. |
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For more details of WizardLM-2 please read our [release blog post](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) and upcoming paper. |
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## Model Details |
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* **Model name**: WizardLM-2 8x22B |
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* **Developed by**: WizardLM@Microsoft AI |
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* **Model type**: Mixture of Experts (MoE) |
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* **Base model**: [mistral-community/Mixtral-8x22B-v0.1](https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1) |
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* **Parameters**: 141B |
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* **Language(s)**: Multilingual |
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* **Blog**: [Introducing WizardLM-2](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) |
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* **Repository**: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM) |
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* **Paper**: WizardLM-2 (Upcoming) |
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* **License**: Apache2.0 |
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## Model Capacities |
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**MT-Bench** |
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We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models. |
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The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models. |
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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. |
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<p align="center" width="100%"> |
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<a ><img src="https://web.archive.org/web/20240415175608im_/https://wizardlm.github.io/WizardLM2/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> |
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</p> |
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**Human Preferences Evaluation** |
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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. |
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We report the win:loss rate without tie: |
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- WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314. |
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- WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat. |
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- WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta. |
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<p align="center" width="100%"> |
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<a ><img src="https://web.archive.org/web/20240415163303im_/https://wizardlm.github.io/WizardLM2/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> |
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</p> |
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## Method Overview |
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We built a **fully AI powered synthetic training system** to train WizardLM-2 models, please refer to our [blog](https://web.archive.org/web/20240415221214/https://wizardlm.github.io/WizardLM2/) for more details of this system. |
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<p align="center" width="100%"> |
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<a ><img src="https://web.archive.org/web/20240415163303im_/https://wizardlm.github.io/WizardLM2/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> |
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</p> |
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## Usage |
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❗<b>Note for model system prompts usage:</b> |
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<b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following: |
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``` |
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A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, |
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detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s> |
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USER: Who are you? ASSISTANT: I am WizardLM.</s>...... |
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``` |
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<b> Inference WizardLM-2 Demo Script</b> |
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We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github. |
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