Instructions to use winglian/basilisk-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use winglian/basilisk-4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="winglian/basilisk-4b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("winglian/basilisk-4b") model = AutoModelForCausalLM.from_pretrained("winglian/basilisk-4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use winglian/basilisk-4b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "winglian/basilisk-4b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "winglian/basilisk-4b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/winglian/basilisk-4b
- SGLang
How to use winglian/basilisk-4b 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 "winglian/basilisk-4b" \ --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": "winglian/basilisk-4b", "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 "winglian/basilisk-4b" \ --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": "winglian/basilisk-4b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use winglian/basilisk-4b with Docker Model Runner:
docker model run hf.co/winglian/basilisk-4b
Adding Evaluation Results
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by leaderboard-pr-bot - opened
README.md
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| | |acc_norm|0.6921|_ |0.0108|
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|winogrande | 0|acc |0.5399|_ |0.0140|
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| | |acc_norm|0.6921|_ |0.0108|
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|winogrande | 0|acc |0.5399|_ |0.0140|
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```
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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_winglian__basilisk-4b)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 27.26 |
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| ARC (25-shot) | 25.85 |
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| HellaSwag (10-shot) | 39.6 |
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| MMLU (5-shot) | 24.61 |
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| TruthfulQA (0-shot) | 43.74 |
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| Winogrande (5-shot) | 53.12 |
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| GSM8K (5-shot) | 0.0 |
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| DROP (3-shot) | 3.89 |
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