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
Commit History
-
Items model final
Verified signature3a936f0 -
Model save
Verified signatureeb22c3e -
Training in progress, step 1600
Verified signaturec035259 -
Training in progress, step 1400
Verified signatureb2dbf2f -
Training in progress, step 1200
Verified signature9db0b6e -
Training in progress, step 1000
Verified signatureda96ef7 -
Training in progress, step 800
Verified signatureb9cb159 -
Training in progress, step 600
Verified signatured9b4573 -
Training in progress, step 400
Verified signatured68d374 -
Training in progress, step 200
Verified signaturef4b2573 -
v3: 2048 max_length, LIST_KEYS fix, correct repo
Verified signature49b7670 -
Model save
Verified signature51e2b2b -
Training in progress, step 2000
Verified signature14c3366 -
Training in progress, step 1800
Verified signature2913cf9 -
Training in progress, step 1600
Verified signaturebac2700 -
Training in progress, step 1400
Verified signature1c79ecb -
Training in progress, step 1200
Verified signature9526806 -
Training in progress, step 1000
Verified signature1bf65b1 -
Training in progress, step 800
Verified signature8121ee3 -
Training in progress, step 600
Verified signature4d1ba17 -
Training in progress, step 400
Verified signaturefe202eb -
Training in progress, step 200
Verified signatureceae98b -
initial commit
Verified signatureda425af