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
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README.md
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@@ -47,7 +47,7 @@ We jointly optimize the model on the conventional language modeling objective an
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We evaluate the model on the datasets of [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The results are shown below:
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| Metric | ReLU Value | Orig Value |
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| ARC (25-shot) | 49.48 | 53.07 |
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| HellaSwag (10-shot) | 74.67 | 78.59 |
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We evaluate the model on the datasets of [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The results are shown below:
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| Metric | ReLU Value | [Orig Value](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-7b-hf) |
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| ARC (25-shot) | 49.48 | 53.07 |
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| HellaSwag (10-shot) | 74.67 | 78.59 |
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