Instructions to use architext/gptj-162M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use architext/gptj-162M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="architext/gptj-162M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("architext/gptj-162M") model = AutoModelForCausalLM.from_pretrained("architext/gptj-162M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use architext/gptj-162M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "architext/gptj-162M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "architext/gptj-162M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/architext/gptj-162M
- SGLang
How to use architext/gptj-162M 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 "architext/gptj-162M" \ --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": "architext/gptj-162M", "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 "architext/gptj-162M" \ --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": "architext/gptj-162M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use architext/gptj-162M with Docker Model Runner:
docker model run hf.co/architext/gptj-162M
Update README.md
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README.md
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# Citation and Related Information
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## BibTeX entry
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To cite this model:
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TBD
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@misc{mesh-transformer-jax,
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author = {Wang, Ben},
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title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
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year = 2021,
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month = May
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}
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# Acknowledgements
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This project would not have been possible without compute generously provided by Google through the TPU Research Cloud that generously provided access to Clout TPU VMs used to finetune this model.
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# Citation and Related Information
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## BibTeX entry
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To cite this model:
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```
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@article{galanos2023architext,
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title={Architext: Language-Driven Generative Architecture Design},
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author={Galanos, Theodoros and Liapis, Antonios and Yannakakis, Georgios N},
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journal={arXiv preprint arXiv:2303.07519},
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year={2023}
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}
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```
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To cite the codebase that trained this model:
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```
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@misc{mesh-transformer-jax,
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author = {Wang, Ben},
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title = {{Mesh-Transformer-JAX: Model-Parallel Implementation of Transformer Language Model with JAX}},
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year = 2021,
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month = May
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}
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```
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# Acknowledgements
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This project would not have been possible without compute generously provided by Google through the TPU Research Cloud that generously provided access to Clout TPU VMs used to finetune this model.
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