Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use Abhay1212/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhay1212/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Abhay1212/content")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Abhay1212/content") model = AutoModelForMultimodalLM.from_pretrained("Abhay1212/content", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Abhay1212/content with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Abhay1212/content" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Abhay1212/content", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Abhay1212/content
- SGLang
How to use Abhay1212/content 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 "Abhay1212/content" \ --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": "Abhay1212/content", "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 "Abhay1212/content" \ --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": "Abhay1212/content", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Abhay1212/content with Docker Model Runner:
docker model run hf.co/Abhay1212/content
| This directory includes a few sample datasets to get you started. | |
| * `california_housing_data*.csv` is California housing data from the 1990 US | |
| Census; more information is available at: | |
| https://developers.google.com/machine-learning/crash-course/california-housing-data-description | |
| * `mnist_*.csv` is a small sample of the | |
| [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is | |
| described at: http://yann.lecun.com/exdb/mnist/ | |
| * `anscombe.json` contains a copy of | |
| [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it | |
| was originally described in | |
| Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American | |
| Statistician. 27 (1): 17-21. JSTOR 2682899. | |
| and our copy was prepared by the | |
| [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json). | |