Instructions to use LumiOpen/Viking-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LumiOpen/Viking-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LumiOpen/Viking-13B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LumiOpen/Viking-13B") model = AutoModelForCausalLM.from_pretrained("LumiOpen/Viking-13B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LumiOpen/Viking-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LumiOpen/Viking-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LumiOpen/Viking-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LumiOpen/Viking-13B
- SGLang
How to use LumiOpen/Viking-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 "LumiOpen/Viking-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": "LumiOpen/Viking-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 "LumiOpen/Viking-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": "LumiOpen/Viking-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LumiOpen/Viking-13B with Docker Model Runner:
docker model run hf.co/LumiOpen/Viking-13B
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README.md
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# Viking 13B
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_**NOTE:** This is a **research checkpoint** of a model for which **training has not been completed.** It is being provided in its current state for research and testing purposes. **Care should be taken when using the outputs of the model.** Once pretraining has completed we intend to release additional instruction-tuned and chat-tuned varieties._
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Viking 13B is a 13B parameter decoder-only transformer pretrained on Finnish,
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English, Swedish, Danish, Norwegian, Icelandic and code. It is being trained
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on 2 trillion tokens (1.3 trillion as of this release). Viking 13B is a fully open source model and is made available under the Apache 2.0 License.
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[Viking 33B](https://huggingface.co/LumiOpen/Viking-33B)
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## Model Overview
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_**NOTE:**
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Viking is a generative pretrained transformer using a LLaMA-like GPT architecture, and makes use of rotary positional embeddings and flash attention.
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* [1500B](https://huggingface.co/LumiOpen/Viking-13B/tree/1500B)
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* [1600B](https://huggingface.co/LumiOpen/Viking-13B/tree/1600B)
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* [1700B](https://huggingface.co/LumiOpen/Viking-13B/tree/1700B)
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The transformers library allows you to load a checkpoint from a branch as follows:
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# Viking 13B
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Viking 13B is a 13B parameter decoder-only transformer pretrained on Finnish,
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English, Swedish, Danish, Norwegian, Icelandic and code. It is being trained
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on 2 trillion tokens (1.3 trillion as of this release). Viking 13B is a fully open source model and is made available under the Apache 2.0 License.
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[Viking 33B](https://huggingface.co/LumiOpen/Viking-33B)
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## Model Overview
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_**NOTE:** This is a base model which needs further fine tuning for most use cases._
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Viking is a generative pretrained transformer using a LLaMA-like GPT architecture, and makes use of rotary positional embeddings and flash attention.
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* [1500B](https://huggingface.co/LumiOpen/Viking-13B/tree/1500B)
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* [1600B](https://huggingface.co/LumiOpen/Viking-13B/tree/1600B)
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* [1700B](https://huggingface.co/LumiOpen/Viking-13B/tree/1700B)
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* [1800B](https://huggingface.co/LumiOpen/Viking-13B/tree/1800B)
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* [1900B](https://huggingface.co/LumiOpen/Viking-13B/tree/1900B)
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* [2000B](https://huggingface.co/LumiOpen/Viking-13B/tree/2000B)
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The transformers library allows you to load a checkpoint from a branch as follows:
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