Instructions to use openbmb/UltraLM-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/UltraLM-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openbmb/UltraLM-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openbmb/UltraLM-13b") model = AutoModelForCausalLM.from_pretrained("openbmb/UltraLM-13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use openbmb/UltraLM-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/UltraLM-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/UltraLM-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/openbmb/UltraLM-13b
- SGLang
How to use openbmb/UltraLM-13b 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 "openbmb/UltraLM-13b" \ --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": "openbmb/UltraLM-13b", "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 "openbmb/UltraLM-13b" \ --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": "openbmb/UltraLM-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use openbmb/UltraLM-13b with Docker Model Runner:
docker model run hf.co/openbmb/UltraLM-13b
Adding Evaluation Results
#4
by leaderboard-pr-bot - opened
README.md
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User: user input<eos_token>
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Assistant:
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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_openbmb__UltraLM-13b)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 25.25 |
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| ARC (25-shot) | 29.44 |
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| HellaSwag (10-shot) | 25.99 |
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| MMLU (5-shot) | 23.12 |
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| TruthfulQA (0-shot) | 48.61 |
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| Winogrande (5-shot) | 49.57 |
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| GSM8K (5-shot) | 0.0 |
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| DROP (3-shot) | 0.0 |
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