Instructions to use NewstaR/Starlight-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NewstaR/Starlight-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NewstaR/Starlight-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NewstaR/Starlight-7B") model = AutoModelForCausalLM.from_pretrained("NewstaR/Starlight-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NewstaR/Starlight-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NewstaR/Starlight-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NewstaR/Starlight-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NewstaR/Starlight-7B
- SGLang
How to use NewstaR/Starlight-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 "NewstaR/Starlight-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": "NewstaR/Starlight-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 "NewstaR/Starlight-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": "NewstaR/Starlight-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NewstaR/Starlight-7B with Docker Model Runner:
docker model run hf.co/NewstaR/Starlight-7B
Update README.md
Browse files
README.md
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@@ -37,4 +37,71 @@ tags:
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## Limitations
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- May hallucinate or generate incorrect information
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- Large model size leads to high compute requirements
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## Limitations
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- May hallucinate or generate incorrect information
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- Large model size leads to high compute requirements
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@misc{open-llm-leaderboard,
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author = {Edward Beeching, Clémentine Fourrier, Nathan Habib, Sheon Han, Nathan Lambert, Nazneen Rajani, Omar Sanseviero, Lewis Tunstall, Thomas Wolf},
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title = {Open LLM Leaderboard},
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year = {2023},
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publisher = {Hugging Face},
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howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}"
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}
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@software{eval-harness,
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author = {Gao, Leo and
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Tow, Jonathan and
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Biderman, Stella and
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Black, Sid and
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DiPofi, Anthony and
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Foster, Charles and
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Golding, Laurence and
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Hsu, Jeffrey and
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McDonell, Kyle and
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Muennighoff, Niklas and
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Phang, Jason and
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Reynolds, Laria and
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Tang, Eric and
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Thite, Anish and
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Wang, Ben and
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Wang, Kevin and
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Zou, Andy},
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title = {A framework for few-shot language model evaluation},
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month = sep,
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year = 2021,
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publisher = {Zenodo},
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version = {v0.0.1},
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doi = {10.5281/zenodo.5371628},
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url = {https://doi.org/10.5281/zenodo.5371628}
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}
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@misc{clark2018think,
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title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
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author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
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year={2018},
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eprint={1803.05457},
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archivePrefix={arXiv},
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primaryClass={cs.AI}
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}
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@misc{zellers2019hellaswag,
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title={HellaSwag: Can a Machine Really Finish Your Sentence?},
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author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi},
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year={2019},
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eprint={1905.07830},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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@misc{hendrycks2021measuring,
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title={Measuring Massive Multitask Language Understanding},
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author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
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year={2021},
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eprint={2009.03300},
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archivePrefix={arXiv},
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primaryClass={cs.CY}
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}
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@misc{lin2022truthfulqa,
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title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
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author={Stephanie Lin and Jacob Hilton and Owain Evans},
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year={2022},
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eprint={2109.07958},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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