Instructions to use Skywork/Skywork-13B-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Skywork/Skywork-13B-Math with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Skywork/Skywork-13B-Math", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Skywork/Skywork-13B-Math", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Skywork/Skywork-13B-Math with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Skywork/Skywork-13B-Math" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywork/Skywork-13B-Math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Skywork/Skywork-13B-Math
- SGLang
How to use Skywork/Skywork-13B-Math with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Skywork/Skywork-13B-Math" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywork/Skywork-13B-Math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Skywork/Skywork-13B-Math" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Skywork/Skywork-13B-Math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Skywork/Skywork-13B-Math with Docker Model Runner:
docker model run hf.co/Skywork/Skywork-13B-Math
Update README.md
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README.md
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<div align="center"><img src="misc/skywork_logo.jpeg" width="550"/></div>
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<p align="center">
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🤗 <a href="https://huggingface.co/Skywork" target="_blank">Hugging Face</a> • 🤖 <a href="https://modelscope.cn/organization/Skywork" target="_blank">ModelScope</a> • 💬 <a href="https://github.com/SkyworkAI/Skywork/blob/main/misc/wechat.png?raw=true" target="_blank">WeChat</a>• 📜<a href="https://arxiv.org/" target="_blank">Tech Report</a>• 🧮<a href="https://arxiv.org/" target="_blank">Skymath Paper</a>
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</p>
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**Skywork-13B-Math**: Skywork-13B-Math model has undergone specialized training to enhance its mathematical abilities. In the 13B-scale model, the Skywork-13B-Math model ranked first in the GSM8K evaluation, and it also performed exceptionally well on the MATH dataset and CMATH, placing it among the top-level 13B models.
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如果您希望了解更多的信息,如训练方案,评估方法,请参考我们的[技术报告](https://arxiv.org/skywork-tech-report)
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# 快速开始(Quickstart)
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我们将模型参数、配置文件、tokenizer等在huggingface和modelscope上进行了开源。
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```
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@article{skyworkmath,
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title={},
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author={},
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journal={arXiv preprint arXiv:},
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year={2023}
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}
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```
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<div align="center"><img src="misc/skywork_logo.jpeg" width="550"/></div>
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<p align="center">
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👨💻 <a href="https://github.com/SkyworkAI/Skywork" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/Skywork" target="_blank">Hugging Face</a> • 🤖 <a href="https://modelscope.cn/organization/Skywork" target="_blank">ModelScope</a> • 💬 <a href="https://github.com/SkyworkAI/Skywork/blob/main/misc/wechat.png?raw=true" target="_blank">WeChat</a>• 📜<a href="https://arxiv.org/" target="_blank">Tech Report</a>• 🧮<a href="https://arxiv.org/" target="_blank">Skymath Paper</a>
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• 🖼️<a href="https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf" target="_blank">SkyworkMM Paper</a>
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</p>
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**Skywork-13B-Math**: Skywork-13B-Math model has undergone specialized training to enhance its mathematical abilities. In the 13B-scale model, the Skywork-13B-Math model ranked first in the GSM8K evaluation, and it also performed exceptionally well on the MATH dataset and CMATH, placing it among the top-level 13B models.
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如果您希望了解更多的信息,如训练方案,评估方法,请参考我们的[技术报告](https://arxiv.org/skywork-tech-report),[Skymath](https://arxiv.org/abs/2310.16713)论文,[SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf)论文。
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If you are interested in more training and evaluation details, please refer to our [technical report](https://arxiv.org/skywork-tech-report), [Skymath]((https://arxiv.org/skywork-tech-report)) paper and [SkyworkMM](https://github.com/will-singularity/Skywork-MM/blob/main/skywork_mm.pdf) paper.
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# Skywork-13B-Math模型评估(Results)
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Skywork-13B-Math在数学能力相对Base模型进一步加强,我们在主流的数学相关benchmark,GSM8K,MATH和CMATH上进行评估。结果显示在13B规模模型中,我们的模型在**GSM8K和CMATH评测中得分第一**,同时MATH评测也处于前列。
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Skywork-13B-Math has further enhanced mathematical capabilities compared to the Base model. We conducted evaluations on mainstream mathematical related benchmarks, GSM8K, MATH, and CMATH. The results show that in the 13B scale model, our model ranked 1st in the GSM8K and CMATH benchmarks, and is also at the forefront in the MATH benchmark.
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| Model | GSM8K | MATH | CMATH |
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|-------------------------|:-----:|:---------------:|:----------:|
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| LLaMA-1-13B-Base | 17.80 | 3.90 | - |
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| LLaMA-2-13B-Base | 28.70 | 3.90 | - |
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| Baichuan-13B-Base | 26.76 | 4.84 | 51.33 |
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| Baichuan-2-13B-Base | 52.77 | 10.08 | - |
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| WizardMath-13B | 63.90 | 14.00 | 50.83 |
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| GAIRMATH-Abel-13B | 66.41 | 17.34 | - |
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| MetaMath-13B | 72.30 | 22.40 | - |
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| Skywork-13B-Math (ours) | **72.33** | 16.98 | **77.27** |
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# 快速开始(Quickstart)
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我们将模型参数、配置文件、tokenizer等在huggingface和modelscope上进行了开源。
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```
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@article{skyworkmath,
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title={SkyMath: Technical Report},
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author={Liu Yang, Haihua Yang, Wenjun Cheng, Lei Lin, Chenxia Li, Yifu Chen, Lunan Liu, Jianfei Pan, Tianwen Wei, Biye Li, Liang Zhao, Lijie Wang, Bo Zhu, Guoliang Li, Xuejie Wu, Xilin Luo, Rui Hu},
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journal={arXiv preprint arXiv: 2310.16713},
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url={https://arxiv.org/abs/2309.10305},
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year={2023}
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}
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```
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```
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@article{Skywork_Multi-Modal_Group_Empirical_Study_Towards_2023,
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author = {Skywork Multi-Modal Group},
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month = sep,
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title = {{Empirical Study Towards Building An Effective Multi-Modal Large Language Model}},
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year = {2023}
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}
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```
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