Instructions to use Vedika35/Vedika-3.5-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vedika35/Vedika-3.5-flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="Vedika35/Vedika-3.5-flash")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Vedika35/Vedika-3.5-flash")
model = AutoModelForImageTextToText.from_pretrained("Vedika35/Vedika-3.5-flash")
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

vLLM

How to use Vedika35/Vedika-3.5-flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vedika35/Vedika-3.5-flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vedika35/Vedika-3.5-flash",
		"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/Vedika35/Vedika-3.5-flash

SGLang

How to use Vedika35/Vedika-3.5-flash 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 "Vedika35/Vedika-3.5-flash" \
    --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": "Vedika35/Vedika-3.5-flash",
		"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 "Vedika35/Vedika-3.5-flash" \
        --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": "Vedika35/Vedika-3.5-flash",
		"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 Vedika35/Vedika-3.5-flash with Docker Model Runner:
```
docker model run hf.co/Vedika35/Vedika-3.5-flash
```

Vedika-3.5-flash / README.md

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	---
	library_name: transformers
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3.5-2B/blob/main/LICENSE
	pipeline_tag: image-text-to-text
	base_model:
	- Qwen/Qwen3.5-2B-Base
	---

	# Vedika 3.5 flash

	<img width="400px" src="https://i.ibb.co/ZR5f1rxF/Blue-and-Black-Minimalist-Brand-Logo-20260423-171504-0000.png">

	> [!Note]
	> This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.
	>
	> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
	>
	> In light of its parameter scale, the intended use cases are prototyping, task-specific fine-tuning, and other research or development purposes.


	Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Vedika 3.5 flash represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.

	## Vedika 3.5 flash Highlights

	Vedika 3.5 flash features the following enhancement:

	- Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Vedika and outperforms Vedika 3.5 flash models across reasoning, coding, agents, and visual understanding benchmarks.

	- Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.

	- Scalable RL Generalization: Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.

	- Global Linguistic Coverage: Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.

	- Next-Generation Training Infrastructure: Near-100% multimodal training efficiency compared to text-only training and asynchronous RL frameworks supporting massive-scale agent scaffolds and environment orchestration.



	## Model Overview

	- Type: Causal Language Model with Vision Encoder
	- Training Stage: Pre-training & Post-training
	- Language Model
	- Number of Parameters: 2B
	- Hidden Dimension: 2048
	- Token Embedding: 248320 (Padded)
	- Number of Layers: 24
	- Hidden Layout: 6 × (3 × (Gated DeltaNet → FFN) → 1 × (Gated Attention → FFN))
	- Gated DeltaNet:
	- Number of Linear Attention Heads: 16 for V and 16 for QK
	- Head Dimension: 128
	- Gated Attention:
	- Number of Attention Heads: 8 for Q and 2 for KV
	- Head Dimension: 256
	- Rotary Position Embedding Dimension: 64
	- Feed Forward Network:
	- Intermediate Dimension: 6144
	- LM Output: 248320 (Tied to token embedding)
	- MTP: trained with multi-steps
	- Context Length: 262,144 natively