Instructions to use TrishanuDas/tayavision-alignment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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-alignment

File size: 654 Bytes

e163944

{
  "add_prefix_space": false,
  "backend": "tokenizers",
  "bos_token": "<BOS_TOKEN>",
  "clean_up_tokenization_spaces": false,
  "cls_token": "<CLS>",
  "eos_token": "<|END_OF_TURN_TOKEN|>",
  "errors": "replace",
  "extra_special_tokens": [
    "<image>"
  ],
  "is_local": false,
  "legacy": true,
  "mask_token": "<MASK_TOKEN>",
  "model_max_length": 1000000000000000019884624838656,
  "pad_token": "<PAD>",
  "processor_class": "TinyAyaVisionProcessor",
  "sep_token": "<SEP>",
  "sp_model_kwargs": {},
  "spaces_between_special_tokens": false,
  "tokenizer_class": "CohereTokenizer",
  "unk_token": "<UNK>",
  "use_default_system_prompt": false
}