Image-Text-to-Text
Transformers
Safetensors
English
multilingual
tiny_aya_vision
text-generation
conversational
Instructions to use TrishanuDas/tayavision-alignment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TrishanuDas/tayavision-alignment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="TrishanuDas/tayavision-alignment") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("TrishanuDas/tayavision-alignment", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TrishanuDas/tayavision-alignment with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TrishanuDas/tayavision-alignment" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TrishanuDas/tayavision-alignment", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/TrishanuDas/tayavision-alignment
- SGLang
How to use TrishanuDas/tayavision-alignment 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 "TrishanuDas/tayavision-alignment" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TrishanuDas/tayavision-alignment", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "TrishanuDas/tayavision-alignment" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TrishanuDas/tayavision-alignment", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use TrishanuDas/tayavision-alignment with Docker Model Runner:
docker model run hf.co/TrishanuDas/tayavision-alignment
TayaVision โ Tiny Aya Vision (Instruct)
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor
repo = "TrishanuDas/tayavision-alignment"
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, trust_remote_code=True)
model = model.to("cuda").eval()
processor = AutoProcessor.from_pretrained(repo, trust_remote_code=True)
image = Image.open("your_image.jpg").convert("RGB")
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "Describe this image in detail."},
]},
]
inputs = processor.apply_chat_template(
messages, images=image, add_generation_prompt=True, return_tensors="pt",
)
inputs = {k: v.to("cuda") for k, v in inputs.items()}
with torch.no_grad():
output_ids = model.generate(**inputs, max_new_tokens=256)
response = processor.tokenizer.decode(
output_ids[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True,
)
print(response)
- Downloads last month
- 1
docker model run hf.co/TrishanuDas/tayavision-alignment