Image-Text-to-Text
Transformers
Safetensors
qwen3_vl
vision
multimodal
lora
ms-swift
vivid
conversational
Instructions to use v1v1d1/nayana-qwen3vl-2b-stage1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use v1v1d1/nayana-qwen3vl-2b-stage1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="v1v1d1/nayana-qwen3vl-2b-stage1") 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("v1v1d1/nayana-qwen3vl-2b-stage1") model = AutoModelForMultimodalLM.from_pretrained("v1v1d1/nayana-qwen3vl-2b-stage1") 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 v1v1d1/nayana-qwen3vl-2b-stage1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "v1v1d1/nayana-qwen3vl-2b-stage1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "v1v1d1/nayana-qwen3vl-2b-stage1", "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/v1v1d1/nayana-qwen3vl-2b-stage1
- SGLang
How to use v1v1d1/nayana-qwen3vl-2b-stage1 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 "v1v1d1/nayana-qwen3vl-2b-stage1" \ --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": "v1v1d1/nayana-qwen3vl-2b-stage1", "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 "v1v1d1/nayana-qwen3vl-2b-stage1" \ --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": "v1v1d1/nayana-qwen3vl-2b-stage1", "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 v1v1d1/nayana-qwen3vl-2b-stage1 with Docker Model Runner:
docker model run hf.co/v1v1d1/nayana-qwen3vl-2b-stage1
nayana-qwen3vl-2b-stage1
Fine-tuned Vision-Language Model using MS-Swift and LoRA adapters.
Model Details
- Base Model: Qwen/Qwen3-VL-2B-Instruct
- Training Method: LoRA (Low-Rank Adaptation)
- Framework: MS-Swift
- Languages: en,kn,hi
Training Details
Dataset
- Dataset: Nayana Docmatix Stage 1
- Size: 150k samples
- Languages: en,kn,hi
Training Hyperparameters
| Parameter | Value |
|---|---|
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| Batch Size | 2 |
| Learning Rate | 0.0001 |
| Epochs | 1.0 |
| Gradient Accumulation | 4 |
LoRA Configuration
- Target Modules: ['all-linear']
- Freeze ViT: True
- Freeze Aligner: True
Usage
With vLLM (Recommended)
from vllm import LLM, SamplingParams
llm = LLM(
model="v1v1d1/nayana-qwen3vl-2b-stage1",
gpu_memory_utilization=0.8,
max_model_len=8192,
)
sampling_params = SamplingParams(
temperature=0.0,
max_tokens=512,
)
# Example with image
messages = [{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "path/to/image.jpg"}},
{"type": "text", "text": "Describe this image in detail."}
]
}]
outputs = llm.chat(messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
With Transformers
from transformers import AutoModelForVision2Seq, AutoProcessor
from PIL import Image
model = AutoModelForVision2Seq.from_pretrained(
"v1v1d1/nayana-qwen3vl-2b-stage1",
torch_dtype="auto",
device_map="auto"
)
processor = AutoProcessor.from_pretrained("v1v1d1/nayana-qwen3vl-2b-stage1")
image = Image.open("path/to/image.jpg")
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "Describe this image in detail."}
]}
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(outputs[0], skip_special_tokens=True))
Citation
@misc{v1v1d1-nayana-qwen3vl-2b-stage1},
author = {Nayana Project},
title = {nayana-qwen3vl-2b-stage1},
year = {2026},
publisher = {HuggingFace},
howpublished = {https://huggingface.co/v1v1d1/nayana-qwen3vl-2b-stage1}
}
Training Details
This model was trained using the VIVID Dataset Pipeline.
Generated with 🤖 Claude Code
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Base model
Qwen/Qwen3-VL-2B-Instruct