Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use Sagarjha456/line_items with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sagarjha456/line_items with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Sagarjha456/line_items")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Sagarjha456/line_items") model = AutoModelForMultimodalLM.from_pretrained("Sagarjha456/line_items", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Sagarjha456/line_items with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sagarjha456/line_items" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sagarjha456/line_items", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Sagarjha456/line_items
- SGLang
How to use Sagarjha456/line_items 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 "Sagarjha456/line_items" \ --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": "Sagarjha456/line_items", "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 "Sagarjha456/line_items" \ --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": "Sagarjha456/line_items", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Sagarjha456/line_items with Docker Model Runner:
docker model run hf.co/Sagarjha456/line_items
Download tokenizer_config.json from Sagarjha456/line_items: direct link, hf CLI and curl.
- Browser
- Download file 1.05 kB
-
https://huggingface.co/Sagarjha456/line_items/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Sagarjha456/line_items/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Sagarjha456/line_items/resolve/main/tokenizer_config.json
1.05 kB
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "extra_special_tokens": [ | |
| "<s_line_items>", | |
| "</s_gst_amount>", | |
| "<s_gst_amount>", | |
| "<s_hsn_code>", | |
| "<s_rate_per_pc>", | |
| "</s_rate_per_pc>", | |
| "<s_gst_rate>", | |
| "<s_rate_currency>", | |
| "<parse_items>", | |
| "</s_rate_currency>", | |
| "</s_hsn_code>", | |
| "</s_description>", | |
| "</s_line_items>", | |
| "<s_quantity>", | |
| "<s_quantity_unit>", | |
| "</s_weight_gms>", | |
| "<s_style_no>", | |
| "</s_style_no>", | |
| "</s_gst_rate>", | |
| "<s_weight_gms>", | |
| "<s_description>", | |
| "</s_total_amount>", | |
| "</s_quantity_unit>", | |
| "<s_total_amount>", | |
| "</s_quantity>" | |
| ], | |
| "from_slow": true, | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "processor_class": "DonutProcessor", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |