Instructions to use Vedika35/Vedika-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vedika35/Vedika-2.0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Vedika35/Vedika-2.0", trust_remote_code=True)
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 AutoModel
model = AutoModel.from_pretrained("Vedika35/Vedika-2.0", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Vedika35/Vedika-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vedika35/Vedika-2.0"
# 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-2.0",
		"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-2.0

SGLang

How to use Vedika35/Vedika-2.0 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-2.0" \
    --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-2.0",
		"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-2.0" \
        --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-2.0",
		"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-2.0 with Docker Model Runner:
```
docker model run hf.co/Vedika35/Vedika-2.0
```

Vedika-2.0

File size: 21,022 Bytes

---
tags:
- compressed-tensors
license: other
license_name: modified-mit
library_name: transformers
pipeline_tag: image-text-to-text
language:
- en
- hi
---
<div align="center">
  <picture>
      <img src="https://i.ibb.co/k26TQhCd/6967-removebg-preview.png" alt="6967 removebg preview" border="0">
  </picture>


## 1. Model Introduction

Vedika 2.0 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.

### Key Features
- **Long-Horizon Coding**: Vedika 2.0 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming languages (Rust, Go, Python) and domains spanning front-end, DevOps, and performance optimization.
- **Coding-Driven Design**: Vedika 2.0 is capable of transforming simple prompts and visual inputs into production-ready interfaces and lightweight full-stack workflows, generating structured layouts, interactive elements, and rich animations with deliberate aesthetic precision.
- **Elevated Agent Swarm**: Scaling horizontally to 300 sub-agents executing 4,000 coordinated steps, Vedika 2.0 can dynamically decompose tasks into parallel, domain-specialized subtasks, delivering end-to-end outputs from documents to websites to spreadsheets in a single autonomous run.
- **Proactive & Open Orchestration**: For autonomous tasks, Vedika 2.0 demonstrates strong performance in powering persistent, 24/7 background agents that proactively manage schedules, execute code, and orchestrate cross-platform operations without human oversight.

## 2. Model Summary

<div align="center">


| | |
|:---:|:---:|
| **Architecture** | Mixture-of-Experts (MoE) |
| **Total Parameters** | 1T |
| **Activated Parameters** | 32B |
| **Number of Layers** (Dense layer included) | 61 |
| **Number of Dense Layers** | 1 |
| **Attention Hidden Dimension** | 7168 |
| **MoE Hidden Dimension** (per Expert) | 2048 |
| **Number of Attention Heads** | 64 |
| **Number of Experts** | 384 |
| **Selected Experts per Token** | 8 |
| **Number of Shared Experts** | 1 |
| **Vocabulary Size** | 160K |
| **Context Length** | 256K |
| **Attention Mechanism** | MLA |
| **Activation Function** | SwiGLU |
| **Vision Encoder** | MoonViT |
| **Parameters of Vision Encoder** | 400M |
</div>

## 3. Evaluation Results

<div align="center">
<table>
<thead>
<tr>
<th align="center">Benchmark</th>
<th align="center"><sup>Vedika 2.0</sup></th>
<th align="center"><sup>GPT-5.4 <br><sup>(xhigh)</sup></sup></th>
<th align="center"><sup>Claude Opus 4.6 <br><sup>(max effort)</sup></sup></th>
<th align="center"><sup>Gemini 3.1 Pro<br><sup>(thinking high)</sup></sup></th>
<th align="center"><sup>Kimi K2.5</sup></th>
</tr>
</thead>
<tbody>
<tr>
<td align="center" colspan=6><strong>Agentic</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">HLE-Full<br>(w/ tools)</td>
<td align="center" style="vertical-align: middle">54.0</td>
<td align="center" style="vertical-align: middle">52.1</td>
<td align="center" style="vertical-align: middle">53.0</td>
<td align="center" style="vertical-align: middle">51.4</td>
<td align="center" style="vertical-align: middle">50.2</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">BrowseComp</td>
<td align="center" style="vertical-align: middle">83.2</td>
<td align="center" style="vertical-align: middle" rowspan="2">82.7</td>
<td align="center" style="vertical-align: middle" rowspan="2">83.7</td>
<td align="center" style="vertical-align: middle" rowspan="2">85.9</td>
<td align="center" style="vertical-align: middle">74.9</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">BrowseComp<br>(Agent Swarm)</td>
<td align="center" style="vertical-align: middle">86.3</td>
<td align="center" style="vertical-align: middle">78.4</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">DeepSearchQA<br>(f1-score)</td>
<td align="center" style="vertical-align: middle">92.5</td>
<td align="center" style="vertical-align: middle">78.6</td>
<td align="center" style="vertical-align: middle">91.3</td>
<td align="center" style="vertical-align: middle">81.9</td>
<td align="center" style="vertical-align: middle">89.0</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">DeepSearchQA<br>(accuracy)</td>
<td align="center" style="vertical-align: middle">83.0</td>
<td align="center" style="vertical-align: middle">63.7</td>
<td align="center" style="vertical-align: middle">80.6</td>
<td align="center" style="vertical-align: middle">60.2</td>
<td align="center" style="vertical-align: middle">77.1</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">WideSearch<br> (item-f1)</td>
<td align="center" style="vertical-align: middle">80.8</td>
<td align="center" style="vertical-align: middle">-</td>
<td align="center" style="vertical-align: middle">-</td>
<td align="center" style="vertical-align: middle">-</td>
<td align="center" style="vertical-align: middle">72.7</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">Toolathlon</td>
<td align="center" style="vertical-align: middle">50.0</td>
<td align="center" style="vertical-align: middle">54.6</td>
<td align="center" style="vertical-align: middle">47.2</td>
<td align="center" style="vertical-align: middle">48.8</td>
<td align="center" style="vertical-align: middle">27.8</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">MCPMark</td>
<td align="center" style="vertical-align: middle">55.9</td>
<td align="center" style="vertical-align: middle">62.5*</td>
<td align="center" style="vertical-align: middle">56.7*</td>
<td align="center" style="vertical-align: middle">55.9*</td>
<td align="center" style="vertical-align: middle">29.5</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">Claw Eval (pass^3)</td>
<td align="center" style="vertical-align: middle">62.3</td>
<td align="center" style="vertical-align: middle">60.3</td>
<td align="center" style="vertical-align: middle">70.4</td>
<td align="center" style="vertical-align: middle">57.8</td>
<td align="center" style="vertical-align: middle">52.3</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">Claw Eval (pass@3)</td>
<td align="center" style="vertical-align: middle">80.9</td>
<td align="center" style="vertical-align: middle">78.4</td>
<td align="center" style="vertical-align: middle">82.4</td>
<td align="center" style="vertical-align: middle">82.9</td>
<td align="center" style="vertical-align: middle">75.4</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">APEX-Agents</td>
<td align="center" style="vertical-align: middle">27.9</td>
<td align="center" style="vertical-align: middle">33.3</td>
<td align="center" style="vertical-align: middle">33.0</td>
<td align="center" style="vertical-align: middle">32.0</td>
<td align="center" style="vertical-align: middle">11.5</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">OSWorld-Verified</td>
<td align="center" style="vertical-align: middle">73.1</td>
<td align="center" style="vertical-align: middle">75.0</td>
<td align="center" style="vertical-align: middle">72.7</td>
<td align="center" style="vertical-align: middle">-</td>
<td align="center" style="vertical-align: middle">63.3</td>
</tr>
<tr>
<td align="center" colspan=6><strong>Coding</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">Terminal-Bench 2.0<br>(Terminus-2)</td>
<td align="center" style="vertical-align: middle">66.7</td>
<td align="center" style="vertical-align: middle">65.4*</td>
<td align="center" style="vertical-align: middle">65.4</td>
<td align="center" style="vertical-align: middle">68.5</td>
<td align="center" style="vertical-align: middle">50.8</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">SWE-Bench Pro</td>
<td align="center" style="vertical-align: middle">58.6</td>
<td align="center" style="vertical-align: middle">57.7</td>
<td align="center" style="vertical-align: middle">53.4</td>
<td align="center" style="vertical-align: middle">54.2</td>
<td align="center" style="vertical-align: middle">50.7</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">SWE-Bench Multilingual</td>
<td align="center" style="vertical-align: middle">76.7</td>
<td align="center" style="vertical-align: middle">-</td>
<td align="center" style="vertical-align: middle">77.8</td>
<td align="center" style="vertical-align: middle">76.9*</td>
<td align="center" style="vertical-align: middle">73.0</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">SWE-Bench Verified</td>
<td align="center" style="vertical-align: middle">80.2</td>
<td align="center" style="vertical-align: middle">-</td>
<td align="center" style="vertical-align: middle">80.8</td>
<td align="center" style="vertical-align: middle">80.6</td>
<td align="center" style="vertical-align: middle">76.8</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">SciCode</td>
<td align="center" style="vertical-align: middle">52.2</td>
<td align="center" style="vertical-align: middle">56.6</td>
<td align="center" style="vertical-align: middle">51.9</td>
<td align="center" style="vertical-align: middle">58.9</td>
<td align="center" style="vertical-align: middle">48.7</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">OJBench (python)</td>
<td align="center" style="vertical-align: middle">60.6</td>
<td align="center" style="vertical-align: middle">-</td>
<td align="center" style="vertical-align: middle">60.3</td>
<td align="center" style="vertical-align: middle">70.7</td>
<td align="center" style="vertical-align: middle">54.7</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">LiveCodeBench (v6)</td>
<td align="center" style="vertical-align: middle">89.6</td>
<td align="center" style="vertical-align: middle">-</td>
<td align="center" style="vertical-align: middle">88.8</td>
<td align="center" style="vertical-align: middle">91.7</td>
<td align="center" style="vertical-align: middle">85.0</td>
</tr>
<tr>
<td align="center" colspan=6><strong>Reasoning &amp; Knowledge</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">HLE-Full</td>
<td align="center" style="vertical-align: middle">34.7</td>
<td align="center" style="vertical-align: middle">39.8</td>
<td align="center" style="vertical-align: middle">40.0</td>
<td align="center" style="vertical-align: middle">44.4</td>
<td align="center" style="vertical-align: middle">30.1</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">AIME 2026</td>
<td align="center" style="vertical-align: middle">96.4</td>
<td align="center" style="vertical-align: middle">99.2</td>
<td align="center" style="vertical-align: middle">96.7</td>
<td align="center" style="vertical-align: middle">98.3</td>
<td align="center" style="vertical-align: middle">95.8</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">HMMT 2026 (Feb)</td>
<td align="center" style="vertical-align: middle">92.7</td>
<td align="center" style="vertical-align: middle">97.7</td>
<td align="center" style="vertical-align: middle">96.2</td>
<td align="center" style="vertical-align: middle">94.7</td>
<td align="center" style="vertical-align: middle">87.1</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">IMO-AnswerBench</td>
<td align="center" style="vertical-align: middle">86.0</td>
<td align="center" style="vertical-align: middle">91.4</td>
<td align="center" style="vertical-align: middle">75.3</td>
<td align="center" style="vertical-align: middle">91.0*</td>
<td align="center" style="vertical-align: middle">81.8</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">GPQA-Diamond</td>
<td align="center" style="vertical-align: middle">90.5</td>
<td align="center" style="vertical-align: middle">92.8</td>
<td align="center" style="vertical-align: middle">91.3</td>
<td align="center" style="vertical-align: middle">94.3</td>
<td align="center" style="vertical-align: middle">87.6</td>
</tr>
<tr>
<td align="center" colspan=6><strong>Vision</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">MMMU-Pro</td>
<td align="center" style="vertical-align: middle">79.4</td>
<td align="center" style="vertical-align: middle">81.2</td>
<td align="center" style="vertical-align: middle">73.9</td>
<td align="center" style="vertical-align: middle">83.0*</td>
<td align="center" style="vertical-align: middle">78.5</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">MMMU-Pro (w/ python)</td>
<td align="center" style="vertical-align: middle">80.1</td>
<td align="center" style="vertical-align: middle">82.1</td>
<td align="center" style="vertical-align: middle">77.3</td>
<td align="center" style="vertical-align: middle">85.3*</td>
<td align="center" style="vertical-align: middle">77.7</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">CharXiv (RQ)</td>
<td align="center" style="vertical-align: middle">80.4</td>
<td align="center" style="vertical-align: middle">82.8*</td>
<td align="center" style="vertical-align: middle">69.1</td>
<td align="center" style="vertical-align: middle">80.2*</td>
<td align="center" style="vertical-align: middle">77.5</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">CharXiv (RQ) (w/ python)</td>
<td align="center" style="vertical-align: middle">86.7</td>
<td align="center" style="vertical-align: middle">90.0*</td>
<td align="center" style="vertical-align: middle">84.7</td>
<td align="center" style="vertical-align: middle">89.9*</td>
<td align="center" style="vertical-align: middle">78.7</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">MathVision</td>
<td align="center" style="vertical-align: middle">87.4</td>
<td align="center" style="vertical-align: middle">92.0*</td>
<td align="center" style="vertical-align: middle">71.2*</td>
<td align="center" style="vertical-align: middle">89.8*</td>
<td align="center" style="vertical-align: middle">84.2</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">MathVision (w/ python)</td>
<td align="center" style="vertical-align: middle">93.2</td>
<td align="center" style="vertical-align: middle">96.1*</td>
<td align="center" style="vertical-align: middle">84.6*</td>
<td align="center" style="vertical-align: middle">95.7*</td>
<td align="center" style="vertical-align: middle">85.0</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">BabyVision</td>
<td align="center" style="vertical-align: middle">39.8</td>
<td align="center" style="vertical-align: middle">49.7</td>
<td align="center" style="vertical-align: middle">14.8</td>
<td align="center" style="vertical-align: middle">51.6</td>
<td align="center" style="vertical-align: middle">36.5</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">BabyVision (w/ python)</td>
<td align="center" style="vertical-align: middle">68.5</td>
<td align="center" style="vertical-align: middle">80.2*</td>
<td align="center" style="vertical-align: middle">38.4*</td>
<td align="center" style="vertical-align: middle">68.3*</td>
<td align="center" style="vertical-align: middle">40.5</td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">V* (w/ python)</td>
<td align="center" style="vertical-align: middle">96.9</td>
<td align="center" style="vertical-align: middle">98.4*</td>
<td align="center" style="vertical-align: middle">86.4*</td>
<td align="center" style="vertical-align: middle">96.9*</td>
<td align="center" style="vertical-align: middle">86.9</td>
</tr>
</tbody>
</table>
</div>

<details>
<summary><b>Footnotes</b></summary>

1. **General Testing Details**
   - We report results for Kimi K2.6 and Kimi K2.5 with thinking mode enabled, Claude Opus 4.6 with max effort, GPT-5.4 with xhigh reasoning effort, and Gemini 3.1 Pro with a high thinking level. 
   - Unless otherwise specified, all Kimi K2.6 experiments were conducted with temperature = 1.0, top-p = 1.0, and a context length of 262,144 tokens. 
   - Benchmarks without publicly available scores were re-evaluated under the same conditions used for Kimi K2.6 and are marked with an asterisk (`*`). Except where noted with an asterisk, all other results are cited from official reports.
2. **Reasoning Benchmarks**
   - IMO-AnswerBench scores for GPT-5.4 and Claude 4.6 were obtained from [z.ai/blog/glm-5.1](https://z.ai/blog/glm-5.1).
   - Humanity's Last Exam (HLE) and other reasoning tasks were evaluated with a maximum generation length of 98,304 tokens. By default, we report results on the HLE full set. For the text-only subset, Kimi K2.6 achieves 36.4% accuracy without tools and 55.5% with tools. 
3. **Tool-Augmented / Agentic Tasks**
   - Vedika 2.0 was equipped with search, code-interpreter, and web-browsing tools for HLE with tools, BrowseComp, DeepSearchQA, and WideSearch.
   - For HLE-Full with tools, the maximum generation length is 262,144 tokens with a per-step limit of 49,152 tokens. We employ a simple context management strategy: once the context window exceeds the threshold, only the most recent round of tool-related messages is retained.
   - For BrowseComp, we report scores obtained with context management using the same discard-all strategy as Kimi K2.5 and DeepSeek-V3.2.
   - For DeepSearchQA, no context management was applied to Vedika 2.0 tests, and tasks exceeding the supported context length were directly counted as failed. Scores for Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro on DeepSearchQA are cited from the [Claude Opus 4.7 System Card](https://cdn.sanity.io/files/4zrzovbb/website/037f06850df7fbe871e206dad004c3db5fd50340.pdf).
   - For WideSearch, we report results under the "hide tool result" context management setting. Once the context window exceeds the threshold, only the most recent round of tool-related messages is retained.
   - The test system prompts are identical to those used in the [Kimi K2.5 technical report](https://arxiv.org/pdf/2602.02276).
   - Claw Eval was conducted using version 1.1  with max-tokens-per-step = 16384.
   - For APEX-Agents, we evaluate 452 tasks from the public 480-task release, as done by [Artificial Analysis](https://artificialanalysis.ai/evaluations/apex-agents-aa)(excluding Investment Banking Worlds 244 and 246, which have external runtime dependencies)
4. **Coding Tasks**
   - Terminal-Bench 2.0 scores were obtained with the default agent framework (Terminus-2) and the provided JSON parser, operating in preserve thinking mode. 
   - For the SWE-Bench series of evaluations (including Verified, Multilingual, and Pro), we used an in-house evaluation framework adapted from SWE-agent. This framework includes a minimal set of tools—bash tool, createfile tool, insert tool, view tool, strreplace tool, and submit tool.
   - All reported scores for coding tasks are averaged over 10 independent runs.
5. **Vision Benchmarks**
   - Max-tokens = 98,304, averaged over three runs (avg@3).
   - Settings with Python tool use max-tokens-per-step = 65,536 and max-steps = 50 for multi-step reasoning.
   - MMMU-Pro follows the official protocol, preserving input order and prepending images.

</details>


## 4. Native INT4 Quantization
Kimi-K2.6 adopts the same native int4 quantization method as [Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking#4-native-int4-quantization).

## 5. Deployment

> [!Note]
> You can access Vedika 2.0's API on https://platform.moonshot.ai and we provide Vedika 2.0 compatible API for you. To verify the deployment is correct, we also provide the  [Kimi Vendor Verifier](https://kimi.com/blog/kimi-vendor-verifier.html).
Currently, Vedika 2.0 is recommended to run on the following inference engines:
* vLLM
* SGLang
* KTransformers

The version requirement for `transformers` is `>=4.57.1, <5.0.0`.

Deployment examples can be found in the [Model Deployment Guide](docs/deploy_guidance.md).


---
## 6. Model Usage

The usage demos below demonstrate how to call our official API. 

For third-party APIs deployed with vLLM or SGLang, please note that:
> [!Note]
> - Chat with video content is an experimental feature and is only supported in our official API for now.
> 
> - The recommended `temperature` will be `1.0` for Thinking mode and `0.6` for Instant mode.
> 
> - The recommended `top_p` is `0.95`.
> 
> - To use instant mode, you need to pass `{'chat_template_kwargs': {"thinking": False}}` in `extra_body`.

### Chat Completion with visual content

Vedika 2.0 supports Image and Video input.

### Preserve Thinking
Vedika 2.0 supports `preserve_thinking` mode, which retains full reasoning content across multi-turn interactions and enhances performance in coding agent scenarios.


## 7. License

Both the code repository and the model weights are released under the [Modified MIT License](LICENSE).

---


## 9. Contact Us

If you have any questions, please reach out at [support@vedika.ai](mailto:katiyarsheelu07@gmail.com)