Instructions to use HPLT/NorOLMo-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HPLT/NorOLMo-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HPLT/NorOLMo-13B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HPLT/NorOLMo-13B") model = AutoModelForCausalLM.from_pretrained("HPLT/NorOLMo-13B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HPLT/NorOLMo-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HPLT/NorOLMo-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HPLT/NorOLMo-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HPLT/NorOLMo-13B
- SGLang
How to use HPLT/NorOLMo-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 "HPLT/NorOLMo-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": "HPLT/NorOLMo-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 "HPLT/NorOLMo-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": "HPLT/NorOLMo-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HPLT/NorOLMo-13B with Docker Model Runner:
docker model run hf.co/HPLT/NorOLMo-13B
link to HPLT 3
Browse files
README.md
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library_name: transformers
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tags:
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- norwegian
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- norsk
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- HPLT
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---
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This is a base (not instruction-tuned) large language model, continually pre-trained on Norwegian data starting from the English [OLMo2-13B](https://huggingface.co/allenai/OLMo-2-1124-13B) model.
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Our training data mixture included HPLTv3 Bokmål and Nynorsk, FinePDF Bokmål and Nynorsk, MADLAD400 Norwegian, OLMo-Mix, Northern Sami dataset.
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Training was conducted as a part of the [HPLT project](https://hplt-project.org/)
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_This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350 and from UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10052546]_
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library_name: transformers
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tags:
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- norwegian
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- norsk
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- HPLT
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---
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This is a base (not instruction-tuned) large language model, continually pre-trained on Norwegian data starting from the English [OLMo2-13B](https://huggingface.co/allenai/OLMo-2-1124-13B) model.
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Our training data mixture included [HPLTv3](https://huggingface.co/datasets/HPLT/HPLT3.0) Bokmål and Nynorsk, FinePDF Bokmål and Nynorsk, MADLAD400 Norwegian, OLMo-Mix, Northern Sami dataset.
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Training was conducted as a part of the [HPLT project](https://hplt-project.org/)
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_This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350 and from UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10052546]_
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