Instructions to use dkalpakchi/SweCTRL-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dkalpakchi/SweCTRL-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dkalpakchi/SweCTRL-Mini")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dkalpakchi/SweCTRL-Mini") model = AutoModelForCausalLM.from_pretrained("dkalpakchi/SweCTRL-Mini") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dkalpakchi/SweCTRL-Mini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dkalpakchi/SweCTRL-Mini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dkalpakchi/SweCTRL-Mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dkalpakchi/SweCTRL-Mini
- SGLang
How to use dkalpakchi/SweCTRL-Mini 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 "dkalpakchi/SweCTRL-Mini" \ --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": "dkalpakchi/SweCTRL-Mini", "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 "dkalpakchi/SweCTRL-Mini" \ --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": "dkalpakchi/SweCTRL-Mini", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dkalpakchi/SweCTRL-Mini with Docker Model Runner:
docker model run hf.co/dkalpakchi/SweCTRL-Mini
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SweCTRL-Mini is a large Swedish language model that can be used for inference and fine-tuning on a single consumer-grade GPU. The model is based on the CTRL architecture by Keskar, McCann, Varshney, Xiong, and Socher
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(2019), which means that users of the SweCTRL-Mini model can control the genre of the generated text by inserting special tokens in the generation prompts.
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Crucially, note that this model is:
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**Note on using Inference API (text box to the right):** There are a number of presets that start the text with appropriate control codes to control the genre, e.g., `:wiki:` for
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texts form Wikipedia. You can add your own prompt on top of these control codes. For instance, if you want a Wikipedia article about Stockholm, you could write
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SweCTRL-Mini is a large Swedish language model that can be used for inference and fine-tuning on a single consumer-grade GPU. The model is based on the CTRL architecture by Keskar, McCann, Varshney, Xiong, and Socher
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(2019), which means that users of the SweCTRL-Mini model can control the genre of the generated text by inserting special tokens in the generation prompts.
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Crucially, note that this model is:
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- **NOT** trained on following GPT-like instructions,
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- **NOT** trained for conversations, like ChatGPT,
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- **NOT** trained on any multi-modal data during training. Only one modality -- text, more than 99% of it in Swedish.
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**Note on using Inference API (text box to the right):** There are a number of presets that start the text with appropriate control codes to control the genre, e.g., `:wiki:` for
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texts form Wikipedia. You can add your own prompt on top of these control codes. For instance, if you want a Wikipedia article about Stockholm, you could write
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