Instructions to use EleutherAI/gpt-j-6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EleutherAI/gpt-j-6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EleutherAI/gpt-j-6b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6b") model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use EleutherAI/gpt-j-6b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EleutherAI/gpt-j-6b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EleutherAI/gpt-j-6b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/EleutherAI/gpt-j-6b
- SGLang
How to use EleutherAI/gpt-j-6b 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 "EleutherAI/gpt-j-6b" \ --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": "EleutherAI/gpt-j-6b", "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 "EleutherAI/gpt-j-6b" \ --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": "EleutherAI/gpt-j-6b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use EleutherAI/gpt-j-6b with Docker Model Runner:
docker model run hf.co/EleutherAI/gpt-j-6b
Adding Evaluation Results
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by leaderboard-pr-bot - opened
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@@ -166,4 +166,17 @@ Thanks to everyone who have helped out one way or another (listed alphabetically
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- [Leo Gao](https://twitter.com/nabla_theta) for running zero shot evaluations for the baseline models for the table.
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- [Laurence Golding](https://github.com/researcher2/) for adding some features to the web demo.
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- [Aran Komatsuzaki](https://twitter.com/arankomatsuzaki) for advice with experiment design and writing the blog posts.
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- [Janko Prester](https://github.com/jprester/) for creating the web demo frontend.
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- [Leo Gao](https://twitter.com/nabla_theta) for running zero shot evaluations for the baseline models for the table.
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- [Laurence Golding](https://github.com/researcher2/) for adding some features to the web demo.
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- [Aran Komatsuzaki](https://twitter.com/arankomatsuzaki) for advice with experiment design and writing the blog posts.
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- [Janko Prester](https://github.com/jprester/) for creating the web demo frontend.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-j-6b)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 34.87 |
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| ARC (25-shot) | 41.38 |
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| HellaSwag (10-shot) | 67.54 |
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| MMLU (5-shot) | 26.78 |
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| TruthfulQA (0-shot) | 35.96 |
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| Winogrande (5-shot) | 65.98 |
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| GSM8K (5-shot) | 1.82 |
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| DROP (3-shot) | 4.62 |
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