Instructions to use Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1")
model = AutoModelForMultimodalLM.from_pretrained("Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1",
		"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/Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1

SGLang

How to use Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1 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 "Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1" \
    --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": "Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1",
		"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 "Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1" \
        --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": "Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1",
		"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 Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1 with Docker Model Runner:
```
docker model run hf.co/Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1
```

Merged ColGemma3 Model

This model is a merged version of multiple ColGemma3 models using the linear merging technique.

Source Models

Merge Method: LINEAR

Linear interpolation: Weighted average of model parameters.

Model Architecture

ColGemma3 is a vision-language model for late interaction retrieval:

Base: Gemma3 vision-language model
Vision Encoder: Processes images into patch embeddings
Custom Projection: Projects embeddings to 128 dimensions
Retrieval: Uses MaxSim scoring for multi-vector retrieval

Usage

from colpali_engine.models.gemma3.colgemma3 import ColGemma3, ColGemmaProcessor3
from PIL import Image
import torch

# Load model and processor
model = ColGemma3.from_pretrained("Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1", torch_dtype=torch.bfloat16, device_map="auto")
processor = ColGemmaProcessor3.from_pretrained("Nayana-cognitivelab/NayanaEmbed-ColGemma3-Merge-Colbert-base-nayana-linear-v1")

# Process images
images = [Image.open("document.png")]
batch_images = processor.process_images(images).to(model.device)

# Process queries
queries = ["What is this document about?"]
batch_queries = processor.process_queries(queries).to(model.device)

# Generate embeddings
with torch.no_grad():
    img_embeddings = model(**batch_images)
    query_embeddings = model(**batch_queries)

# Compute similarity scores
scores = processor.score([query_embeddings[0]], [img_embeddings[0]])

Citation

If you use this model, please cite the original ColGemma3 work and the source models.

This model was automatically merged using Modal infrastructure.

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Safetensors

Model size

4B params

Tensor type

BF16