Instructions to use deepseek-ai/DeepSeek-V4-Flash-Vision-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/DeepSeek-V4-Flash-Vision-Exp with Transformers:

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

pipe = pipeline("image-text-to-text", model="deepseek-ai/DeepSeek-V4-Flash-Vision-Exp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V4-Flash-Vision-Exp")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V4-Flash-Vision-Exp", device_map="auto")

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/DeepSeek-V4-Flash-Vision-Exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepseek-ai/DeepSeek-V4-Flash-Vision-Exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp

SGLang

How to use deepseek-ai/DeepSeek-V4-Flash-Vision-Exp 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 "deepseek-ai/DeepSeek-V4-Flash-Vision-Exp" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "deepseek-ai/DeepSeek-V4-Flash-Vision-Exp" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepseek-ai/DeepSeek-V4-Flash-Vision-Exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-V4-Flash-Vision-Exp with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp
```

DeepSeek-V4-Flash-Vision-Exp / README.md

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	---
	license: mit
	library_name: transformers
	pipeline_tag: image-text-to-text
	---

	# DeepSeek-V4-Flash-Vision-Exp

	<!-- markdownlint-disable first-line-h1 -->
	<!-- markdownlint-disable html -->
	<!-- markdownlint-disable no-duplicate-header -->

	<div align="center">
	<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V4" />
	</div>
	<hr>
	<div align="center" style="line-height: 1;">
	<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
	<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
	</a>
	<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
	<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V4-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</div>
	<div align="center" style="line-height: 1;">
	<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
	<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
	</a>
	<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
	<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</div>
	<div align="center" style="line-height: 1;">
	<a href="LICENSE" style="margin: 2px;">
	<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</div>

	## Introduction

	We are excited to introduce DeepSeek-V4-Flash-Vision-Exp, our first experimental multimodal model in the DeepSeek-V4 family. It builds on the DeepSeek-V4-Flash architecture by incorporating visual modules and undergoing continued training to unlock visual understanding capabilities.

	Compared to DeepSeek-V4-Flash-0731, DeepSeek-V4-Flash-Vision-Exp achieves substantial improvements on its multimodal agent capabilities, while maintaining comparable performance on text-only agent tasks.

	<div align="center">

	\| Benchmark \| DeepSeek-V4-Flash-Vision-Exp \| DeepSeek-V4-Flash-0731 \| Opus-4.8 \|
	\| :--- \| :---: \| :---: \| :---: \|
	\| Text Agent Capabilities \| \| \| \|
	\| Terminal Bench 2.1 \| 83.9 \| 82.7 \| 85.0 \|
	\| NL2Repo \| 57.7 \| 54.2 \| 69.7 \|
	\| Cybergym \| 75.3 \| 76.7 \| 78.3 \|
	\| DeepSWE \| 59.3 \| 54.4 \| 58.0 \|
	\| Toolathlon-Verified \| 75.9 \| 70.3 \| 76.2 \|
	\| DSBench-Hard \| 63.6 \| 59.6 \| 71.7 \|
	\| AutomationBench (Public) \| 25.7 \| 25.1 \| 27.2 \|
	\| Multimodal Agent Capabilities \| \| \| \|
	\| ApexBench (Pass@1) \| 36.5 \| 26.2† \| 39.4 \|
	\| Agents' Last Exam \| 27.3 \| 25.2† \| 25.7 \|
	\| Chartography \| 64.3 \| - \| 65.0 \|
	\| ZeroBench (Pass@5) \| 35.0 \| - \| 34.0 \|

	</div>

	Notes:

	1. For the text agent benchmarks above, DeepSeek models are evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the `max` reasoning effort level with `temperature = 1.0, top_p = 0.95`.
	2. † For ApexBench and Agents' Last Exam, DeepSeek-V4-Flash-0731 ignores the multimodal elements in the input.


	## Repository layout

	This repository contains the tokenizer, prompt encoding reference, and a
	minimal PyTorch inference implementation for DeepSeek-V4 Flash Vision. The
	reference inference covers the vision encoder and aligner, DFlash attention,
	MoE, Hyper-Connections, and the DSpark forward path.

	```text
	.
	├── encoding/ # OpenAI-style messages -> model prompt
	├── inference/ # weight conversion and minimal inference
	│ └── examples/ # equivalent TXT and JSON vision prompts
	├── config.json # Hugging Face model metadata
	├── generation_config.json
	├── model.safetensors.index.json
	├── tokenizer.json
	└── tokenizer_config.json
	```

	`encoding/` and `inference/` deliberately remain separate: prompt formatting
	does not depend on PyTorch, while inference imports the sibling encoding module
	with an explicit Python path. No symlinks are required.

	The tokenizer files are regular files so that the repository can be uploaded
	to Hugging Face without relying on local filesystem symlinks. The large model
	shards are described by `model.safetensors.index.json` and are not duplicated
	inside the source checkout used to assemble this repository.

	## Prompt encoding

	See [`encoding/README.md`](encoding/README.md). Both OpenAI-style JSON content
	blocks and the compact `<image>path</image>` TXT notation are supported. The two
	examples under `inference/examples/` encode to identical prompts and token IDs.

	## Minimal inference

	See [`inference/README.md`](inference/README.md) for dependency installation,
	checkpoint conversion, and TXT/JSON inference commands.

	## How to Run with vLLM

	For example, the command below serves the model with vLLM on a single 4×GB300 node.
	See the [vLLM recipe](https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp) for detailed instructions and other hardware configurations.

	```bash
	docker run --gpus all \
	vllm/vllm-openai:deepseekv4-flash-vision deepseek-ai/DeepSeek-V4-Flash-Vision-Exp \
	--kv-cache-dtype fp8 \
	--block-size 256 \
	--tensor-parallel-size 4 \
	--tool-call-parser deepseek_v4 \
	--enable-auto-tool-choice \
	--reasoning-parser deepseek_v4 \
	--reasoning-config '{"reasoning_parser":"deepseek_v4","reasoning_start_str":"","reasoning_end_str":""}' \
	--speculative-config '{"method":"dspark","model":"deepseek-ai/DeepSeek-V4-Flash-Vision-Exp","num_speculative_tokens":3,"draft_sample_method":"probabilistic","enable_adaptive_verification":true}'
	```

	## How to Run with SGLang
	Enable DSpark with --speculative-algorithm DSPARK and do not set a separate --speculative-draft-model-path as the target and draft weights therefore come from the same checkpoint. See the [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4#hw=b200&variant=flash-vision&quant=fp4&strategy=low-latency&nodes=single) for detailed instructions, benchmarks and other hardwares configurations.

	```
	sglang serve \
	--model-path deepseek-ai/DeepSeek-V4-Flash-Vision-Exp \
	--tp 4 \
	--speculative-algorithm DSPARK \
	--mem-fraction-static 0.85 \
	--host 0.0.0.0 \
	--port 30000
	```

	## License

	This repository is licensed under the [MIT License](LICENSE).