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WizardLM-2
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| <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-661d403f71e6c8257dbd598a" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" 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> | ||
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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, | ||
| 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. | ||
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| - 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 5x larger opensource leading models. | ||
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| For more details of WizardLM-2 please read our [release blog post](https://wizardlm.github.io/WizardLM2) and upcoming paper. | ||
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| ## 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. | ||
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| <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> | ||
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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. | ||
| 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 from 7B to 70B model scales. | ||
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| <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> | ||
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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. | ||
| 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. | ||
| - 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. | ||
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| <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> | ||
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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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| ``` | ||
| 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>...... | ||
| ``` | ||
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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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| ## License | ||
| The License of WizardLM-2 8x22B and WizardLM-2 7B is Apache2.0. The License of WizardLM-2 70B is Llama-2-Community. | ||
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