Text Generation
Transformers
PyTorch
Norwegian
Norwegian Bokmål
Norwegian Nynorsk
text2text-generation
T5
NorT5
Norwegian
encoder-decoder
custom_code
Instructions to use ltg/nort5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltg/nort5-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ltg/nort5-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("ltg/nort5-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ltg/nort5-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ltg/nort5-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ltg/nort5-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ltg/nort5-base
- SGLang
How to use ltg/nort5-base 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 "ltg/nort5-base" \ --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": "ltg/nort5-base", "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 "ltg/nort5-base" \ --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": "ltg/nort5-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ltg/nort5-base with Docker Model Runner:
docker model run hf.co/ltg/nort5-base
File size: 950 Bytes
900d7b0 e10cc6f 900d7b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | {
"architectures": [
"NorT5ForConditionalGeneration"
],
"auto_map": {
"AutoConfig": "configuration_nort5.NorT5Config",
"AutoModel": "modeling_nort5.NorT5Model",
"AutoModelForSeq2SeqLM": "modeling_nort5.NorT5ForConditionalGeneration",
"AutoModelForConditionalGeneration": "modeling_nort5.NorT5ForConditionalGeneration"
},
"attention_probs_dropout_prob": 0.0,
"bos_token_id": 5,
"cls_token_id": 1,
"eos_token_id": 6,
"hidden_dropout_prob": 0.0,
"hidden_size": 512,
"initializer_range": 0.02,
"intermediate_size": 1365,
"layer_norm_eps": 1e-07,
"max_position_embeddings": 512,
"num_attention_heads": 8,
"num_hidden_layers": 24,
"output_all_encoded_layers": true,
"pad_token_id": 3,
"position_bucket_size": 32,
"sep_token_id": 2,
"torch_dtype": "float32",
"transformers_version": "4.24.0",
"vocab_size": 50000,
"max_length": 512,
"max_new_tokens": 256,
"is_encoder_decoder": true
}
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