Microsoft/Dense

WizardLM 70B

chat
70B
Parameters
8K
Context length
7
Benchmarks
4
Quantizations
60K
HF downloads
Architecture
Dense
Released
2023-08-11
Layers
80
KV Heads
8
Head Dim
128
Family
wizardlm

WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions

<p style="font-size:28px;" align="center"> 🏠 <a href="https://wizardlm.github.io/" target="_blank">Home Page</a> </p> <p align="center"> 🤗 <a href="https://huggingface.co/WizardLM" 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>

Unofficial Video Introductions

Thanks to the enthusiastic friends, their video introductions are more lively and interesting.

  1. NEW WizardLM 70b 🔥 Giant Model...Insane Performance
  2. GET WizardLM NOW! 7B LLM KING That Can Beat ChatGPT! I'm IMPRESSED!
  3. WizardLM: Enhancing Large Language Models to Follow Complex Instructions
  4. WizardCoder AI Is The NEW ChatGPT's Coding TWIN!

News

  • 🔥🔥🔥[2023/08/26] We released WizardCoder-Python-34B-V1.0 , which achieves the 73.2 pass@1 and surpasses GPT4 (2023/03/15), ChatGPT-3.5, and Claude2 on the HumanEval Benchmarks. For more details, please refer to WizardCoder.
  • [2023/06/16] We released WizardCoder-15B-V1.0 , which surpasses Claude-Plus (+6.8), Bard (+15.3) and InstructCodeT5+ (+22.3) on the HumanEval Benchmarks. For more details, please refer to WizardCoder.
ModelCheckpointPaperHumanEvalMBPPDemoLicense
WizardCoder-Python-34B-V1.0🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>73.261.2Demo<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a>
WizardCoder-15B-V1.0🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>59.850.6--<a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a>
WizardCoder-Python-13B-V1.0🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>64.055.6--<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a>
WizardCoder-Python-7B-V1.0🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0" target="_blank">HF Link</a>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>55.551.6Demo<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a>
WizardCoder-3B-V1.0🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>34.837.4--<a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a>
WizardCoder-1B-V1.0🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>23.828.6--<a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a>
  • 🔥 [08/11/2023] We release WizardMath Models.
  • 🔥 Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
  • 🔥 Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM.
  • 🔥 Our WizardMath-70B-V1.0 model achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points higher than the SOTA open-source LLM.
ModelCheckpointPaperGSM8kMATHOnline DemoLicense
WizardMath-70B-V1.0🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a>📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>81.622.7Demo<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>
WizardMath-13B-V1.0🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a>📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>63.914.0Demo<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>
WizardMath-7B-V1.0🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a>📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>54.910.7Demo<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>
<font size=4>
<sup>Model</sup><sup>Checkpoint</sup><sup>Paper</sup><sup>MT-Bench</sup><sup>AlpacaEval</sup><sup>GSM8k</sup><sup>HumanEval</sup><sup>License</sup>
<sup>WizardLM-70B-V1.0</sup><sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-70B-V1.0" target="_blank">HF Link</a> </sup><sup>📃Coming Soon</sup><sup>7.78</sup><sup>92.91%</sup><sup>77.6%</sup><sup> 50.6 pass@1</sup><sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup>
<sup>WizardLM-13B-V1.2</sup><sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup><sup>7.06</sup><sup>89.17%</sup><sup>55.3%</sup><sup>36.6 pass@1</sup><sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup>
<sup>WizardLM-13B-V1.1</sup><sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup><sup>6.76</sup><sup>86.32%</sup><sup>25.0 pass@1</sup><sup>Non-commercial</sup>
<sup>WizardLM-30B-V1.0</sup><sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup><sup>7.01</sup><sup>37.8 pass@1</sup><sup>Non-commercial</sup>
<sup>WizardLM-13B-V1.0</sup><sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup><sup>6.35</sup><sup>75.31%</sup><sup> 24.0 pass@1 </sup><sup>Non-commercial</sup>
<sup>WizardLM-7B-V1.0 </sup><sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup><sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup><sup>19.1 pass@1 </sup><sup> Non-commercial</sup>
</font>
  • 🔥🔥🔥 [08/09/2023] We released WizardLM-70B-V1.0 model.

Github Repo: https://github.com/nlpxucan/WizardLM

Twitter: https://twitter.com/WizardLM_AI/status/1689270108747976704

Discord: https://discord.gg/bpmeZD7V

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

<b>WizardLM</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>......

Inference WizardLM Demo Script

We provide the inference WizardLM demo code here.

Please cite the paper if you use the data or code from WizardLM.

@article{xu2023wizardlm,
  title={Wizardlm: Empowering large language models to follow complex instructions},
  author={Xu, Can and Sun, Qingfeng and Zheng, Kai and Geng, Xiubo and Zhao, Pu and Feng, Jiazhan and Tao, Chongyang and Jiang, Daxin},
  journal={arXiv preprint arXiv:2304.12244},
  year={2023}
}

<b>To commen concern about dataset:</b>

Recently, there have been clear changes in the open-source policy and regulations of our overall organization's code, data, and models.

Despite this, we have still worked hard to obtain opening the weights of the model first, but the data involves stricter auditing and is in review with our legal team .

Our researchers have no authority to publicly release them without authorization.

Thank you for your understanding.

Quantizations & VRAM

Q4_K_M4.5 bpw
40.8 GB
VRAM required
94%
Quality
Q6_K6.5 bpw
58.3 GB
VRAM required
97%
Quality
Q8_08 bpw
71.5 GB
VRAM required
100%
Quality
FP1616 bpw
141.5 GB
VRAM required
100%
Quality

Benchmarks (7)

HumanEval50.6
IFEval49.5
MMLU-PRO42.0
BBH37.5
MUSR14.1
MATH3.9
GPQA2.1

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Apple M3 Max (48GB)
48 GB VRAM • 400 GB/s
APPLE
$2899
Apple M4 Pro (48GB)
48 GB VRAM • 273 GB/s
APPLE
$1799
Apple M4 Max (48GB)
48 GB VRAM • 546 GB/s
APPLE
$2499
NVIDIA L40S 48GB
48 GB VRAM • 864 GB/s
NVIDIA
$7500
NVIDIA L40 48GB
48 GB VRAM • 864 GB/s
NVIDIA
$5500
NVIDIA RTX 6000 Ada 48GB
48 GB VRAM • 960 GB/s
NVIDIA
$6800
NVIDIA A40 48GB
48 GB VRAM • 696 GB/s
NVIDIA
$4650
NVIDIA RTX A6000 48GB
48 GB VRAM • 768 GB/s
NVIDIA
$4650
NVIDIA Quadro RTX 8000
48 GB VRAM • 672 GB/s
NVIDIA
NVIDIA Quadro RTX 8000 Passive
48 GB VRAM • 624 GB/s
NVIDIA
NVIDIA A40 PCIe
48 GB VRAM • 696 GB/s
NVIDIA
NVIDIA RTX 6000 Ada Generation
48 GB VRAM • 960 GB/s
NVIDIA
NVIDIA L20
48 GB VRAM • 864 GB/s
NVIDIA
AMD Radeon PRO W7800 48 GB
48 GB VRAM • 864 GB/s
AMD
AMD Radeon PRO W7900
48 GB VRAM • 864 GB/s
AMD
Intel Data Center GPU Max 1100
48 GB VRAM • 1230 GB/s
INTEL
NVIDIA RTX 5880 Ada Generation
48 GB VRAM • 864 GB/s
NVIDIA
NVIDIA RTX PRO 5000 Blackwell
48 GB VRAM • 1340 GB/s
NVIDIA
AMD Radeon PRO W7900D
48 GB VRAM • 864 GB/s
AMD
Apple M1 Ultra (64GB)
64 GB VRAM • 800 GB/s
APPLE
$2499
Apple M2 Ultra (64GB)
64 GB VRAM • 800 GB/s
APPLE
$2999
Apple M4 Max (64GB)
64 GB VRAM • 546 GB/s
APPLE
$2899
Apple M2 Max (64GB)
64 GB VRAM • 400 GB/s
APPLE
$2299
Apple M3 Max (64GB)
64 GB VRAM • 300 GB/s
APPLE
$2799
Apple M4 Pro (64GB)
64 GB VRAM • 273 GB/s
APPLE
$2599
AMD Radeon Instinct MI200
64 GB VRAM • 1640 GB/s
AMD
AMD Radeon Instinct MI210
64 GB VRAM • 1640 GB/s
AMD
NVIDIA H100 SXM5 64 GB
64 GB VRAM • 2020 GB/s
NVIDIA
NVIDIA Jetson AGX Orin 64 GB
64 GB VRAM • 205 GB/s
NVIDIA
NVIDIA Jetson T4000
64 GB VRAM • 273 GB/s
NVIDIA

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