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")# 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
Better description and max model size
Browse files- README.md +2 -1
- tokenizer_config.json +1 -1
README.md
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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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The model was trained for 33 000 steps on around 275 billion tokens.
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The main `branch` contains the model's weights after step 33 000 (stage 3).
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## Evaluation
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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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The model was trained for 33 000 steps on around 275 billion tokens. Maximum sequence length was set to 4 096 in the beginning of training, and then was extended to 16 384 starting from the checkpoint `step_31000`.
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Intermediate checkpoints are published here as branches.
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The main `branch` contains the model's weights after step 33 000 (stage 3).
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## Evaluation
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tokenizer_config.json
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"is_local": true,
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"local_files_only": false,
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"mask_token": "<|mask|>",
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"model_max_length":
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"pad_token": "<|pad|>",
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"sep_token": "<|endoftext|>",
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"tokenizer_class": "TokenizersBackend",
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"is_local": true,
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"local_files_only": false,
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"mask_token": "<|mask|>",
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"model_max_length": 16384,
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"pad_token": "<|pad|>",
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"sep_token": "<|endoftext|>",
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"tokenizer_class": "TokenizersBackend",
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