Instructions to use senga-ml/dnote-body with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use senga-ml/dnote-body with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="senga-ml/dnote-body")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("senga-ml/dnote-body") model = AutoModelForImageTextToText.from_pretrained("senga-ml/dnote-body") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use senga-ml/dnote-body with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "senga-ml/dnote-body" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "senga-ml/dnote-body", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/senga-ml/dnote-body
- SGLang
How to use senga-ml/dnote-body 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 "senga-ml/dnote-body" \ --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": "senga-ml/dnote-body", "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 "senga-ml/dnote-body" \ --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": "senga-ml/dnote-body", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use senga-ml/dnote-body with Docker Model Runner:
docker model run hf.co/senga-ml/dnote-body
Training done
Browse files- tokenizer.json +16 -2
- tokenizer_config.json +7 -0
tokenizer.json
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{
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"version": "1.0",
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"truncation":
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"added_tokens": [
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"id": 0,
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{
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"version": "1.0",
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"truncation": {
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"direction": "Right",
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"max_length": 768,
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"strategy": "LongestFirst",
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"stride": 0
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},
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"padding": {
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"strategy": {
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"Fixed": 768
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"direction": "Right",
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"pad_to_multiple_of": null,
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"pad_id": 1,
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"pad_type_id": 0,
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"pad_token": "<pad>"
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"added_tokens": [
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"id": 0,
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tokenizer_config.json
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"processor_class": "DonutProcessor",
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"sep_token": "</s>",
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"sp_model_kwargs": {},
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"tokenizer_class": "XLMRobertaTokenizer",
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"unk_token": "<unk>"
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}
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": "<mask>",
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"max_length": 768,
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"model_max_length": 1000000000000000019884624838656,
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"pad_to_multiple_of": null,
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"pad_token": "<pad>",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"processor_class": "DonutProcessor",
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"sep_token": "</s>",
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"sp_model_kwargs": {},
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"stride": 0,
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"tokenizer_class": "XLMRobertaTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<unk>"
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}
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