Instructions to use SparseLLM/ReluLLaMA-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/ReluLLaMA-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SparseLLM/ReluLLaMA-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SparseLLM/ReluLLaMA-7B") model = AutoModelForCausalLM.from_pretrained("SparseLLM/ReluLLaMA-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SparseLLM/ReluLLaMA-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SparseLLM/ReluLLaMA-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SparseLLM/ReluLLaMA-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SparseLLM/ReluLLaMA-7B
- SGLang
How to use SparseLLM/ReluLLaMA-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 "SparseLLM/ReluLLaMA-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": "SparseLLM/ReluLLaMA-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 "SparseLLM/ReluLLaMA-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": "SparseLLM/ReluLLaMA-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SparseLLM/ReluLLaMA-7B with Docker Model Runner:
docker model run hf.co/SparseLLM/ReluLLaMA-7B
Yixin Song commited on
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Parent(s): 71acee4
Update README.md
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README.md
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@@ -59,11 +59,12 @@ We evaluate the model on the datasets of [Open LLM Leaderboard](https://huggingf
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### Inference Tool
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We utilize [PowerInfer](https://github.com/SJTU-IPADS/PowerInfer) for
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Dense Inference:
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Sparse Inference:
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### License Disclaimer:
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### Inference Tool
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We utilize [PowerInfer](https://github.com/SJTU-IPADS/PowerInfer) for inference, here we present the inference speeds of pure CPU-based inference with fp16 precision.
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The CPU configuration includes an Intel i9-13900K processor (eight performance cores at 5.4GHz) and 192GB of host memory (with a memory bandwidth of 67.2 GB/s).
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Dense Inference: 5.17 tokens/s
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Sparse Inference: 8.21 tokens/s
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### License Disclaimer:
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