Instructions to use WizardLMTeam/WizardMath-7B-V1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WizardLMTeam/WizardMath-7B-V1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WizardLMTeam/WizardMath-7B-V1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardMath-7B-V1.1") model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardMath-7B-V1.1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use WizardLMTeam/WizardMath-7B-V1.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WizardLMTeam/WizardMath-7B-V1.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WizardLMTeam/WizardMath-7B-V1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WizardLMTeam/WizardMath-7B-V1.1
- SGLang
How to use WizardLMTeam/WizardMath-7B-V1.1 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 "WizardLMTeam/WizardMath-7B-V1.1" \ --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": "WizardLMTeam/WizardMath-7B-V1.1", "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 "WizardLMTeam/WizardMath-7B-V1.1" \ --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": "WizardLMTeam/WizardMath-7B-V1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WizardLMTeam/WizardMath-7B-V1.1 with Docker Model Runner:
docker model run hf.co/WizardLMTeam/WizardMath-7B-V1.1
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README.md
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π <a href="https://wizardlm.github.io/" target="_blank">Home Page</a> </p>
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π€ <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> β’π± <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> β’ π¦ <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a>
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π Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
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π <a href="https://wizardlm.github.io/" target="_blank">Home Page</a> </p>
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π€ <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> β’π± <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> β’ π¦ <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> </p>
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π <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> β’ π <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> β’ π <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
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π Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
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