Instructions to use SparseLLM/relu-100B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/relu-100B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SparseLLM/relu-100B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SparseLLM/relu-100B") model = AutoModelForCausalLM.from_pretrained("SparseLLM/relu-100B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SparseLLM/relu-100B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SparseLLM/relu-100B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SparseLLM/relu-100B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SparseLLM/relu-100B
- SGLang
How to use SparseLLM/relu-100B 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 "SparseLLM/relu-100B" \ --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": "SparseLLM/relu-100B", "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 "SparseLLM/relu-100B" \ --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": "SparseLLM/relu-100B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SparseLLM/relu-100B with Docker Model Runner:
docker model run hf.co/SparseLLM/relu-100B
Yixin Song commited on
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README.md
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@@ -43,13 +43,11 @@ We pretrain the model on 100 billion tokens, including:
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Please kindly cite using the following BibTeX:
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```bibtex
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@
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title={ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs},
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author={Zhengyan Zhang and Yixin Song and Guanghui Yu and Xu Han and Yankai Lin and Chaojun Xiao and Chenyang Song and Zhiyuan Liu and Zeyu Mi and Maosong Sun},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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```
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Please kindly cite using the following BibTeX:
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```bibtex
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@article{zhang2024relu2,
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title={ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs},
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author={Zhengyan Zhang and Yixin Song and Guanghui Yu and Xu Han and Yankai Lin and Chaojun Xiao and Chenyang Song and Zhiyuan Liu and Zeyu Mi and Maosong Sun},
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journal = {arXiv preprint arXiv:2402.03804},
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year={2024},
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}
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```
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