Instructions to use OFA-Sys/MuggleMath_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OFA-Sys/MuggleMath_7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OFA-Sys/MuggleMath_7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OFA-Sys/MuggleMath_7B") model = AutoModelForCausalLM.from_pretrained("OFA-Sys/MuggleMath_7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OFA-Sys/MuggleMath_7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OFA-Sys/MuggleMath_7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OFA-Sys/MuggleMath_7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OFA-Sys/MuggleMath_7B
- SGLang
How to use OFA-Sys/MuggleMath_7B 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 "OFA-Sys/MuggleMath_7B" \ --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": "OFA-Sys/MuggleMath_7B", "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 "OFA-Sys/MuggleMath_7B" \ --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": "OFA-Sys/MuggleMath_7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OFA-Sys/MuggleMath_7B with Docker Model Runner:
docker model run hf.co/OFA-Sys/MuggleMath_7B
Update README.md
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license: apache-2.0
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---
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license: apache-2.0
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---
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see our paper in: [https://arxiv.org/abs/2310.05506](https://arxiv.org/abs/2310.05506)
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## Model Details
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MuggleMATH is fully fine-tuned on the AugGSM8K and AugMATH datasets and based on the LLaMA-2 Models.
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## **Model Usage**
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prompting template:
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'''
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"Below is an instruction that describes a task. " "Write a response that appropriately completes the request.\n\n" "### Instruction:\n{instruction}\n\n### Response:"
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'''
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We recommend using vllm to accelerate inference.
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## Experiment
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| | GSM8K | MATH |
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| --- | --- | --- |
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| MuggleMATH-7B | 69.8 | 25.8 |
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| MuggleMATH-13B | 74.3 | 30.7 |
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| MuggleMATH-70B | 82.5 | 42.1 |
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## **Citation**
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@misc{li2023query,
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title={Query and Response Augmentation Cannot Help Out-of-domain Math Reasoning Generalization},
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author={Chengpeng Li and Zheng Yuan and Hongyi Yuan and Guanting Dong and Keming Lu and Jiancan Wu and Chuanqi Tan and Xiang Wang and Chang Zhou},
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journal={arXiv preprint arXiv: 2310.05506},
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year={2023}
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}
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