🏠 WizardLM-2 Release Blog

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## See [here](https://huggingface.co/lucyknada/microsoft_WizardLM-2-7B) for the WizardLM-2-7B re-upload. ## 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://web.archive.org/web/20240415221214/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://web.archive.org/web/20240415221214/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.

**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.

## Method Overview 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.

## Usage ❗Note for model system prompts usage: WizardLM-2 adopts the prompt format from Vicuna 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. USER: Who are you? ASSISTANT: I am WizardLM....... ``` Inference WizardLM-2 Demo Script We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_alpindale__WizardLM-2-8x22B) | Metric |Value| |-------------------|----:| |Avg. |32.61| |IFEval (0-Shot) |52.72| |BBH (3-Shot) |48.58| |MATH Lvl 5 (4-Shot)|22.28| |GPQA (0-shot) |17.56| |MuSR (0-shot) |14.54| |MMLU-PRO (5-shot) |39.96|