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
```

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


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

3. Evaluation Results

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

Footnotes

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.
Reasoning Benchmarks
- IMO-AnswerBench scores for GPT-5.4 and Claude 4.6 were obtained from 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.
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.
- 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.
- 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(excluding Investment Banking Worlds 244 and 246, which have external runtime dependencies)
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.
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.

4. Native INT4 Quantization

Kimi-K2.6 adopts the same native int4 quantization method as Kimi-K2-Thinking.

5. Deployment

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. 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.

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:

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.

9. Contact Us

If you have any questions, please reach out at support@vedika.ai

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Paper for Vedika35/Vedika-2.0

Kimi K2.5: Visual Agentic Intelligence

Paper • 2602.02276 • Published Feb 2 • 268