Instructions to use FlyCockpit/DeepSeek-V4-Flash-0731-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FlyCockpit/DeepSeek-V4-Flash-0731-vision with Transformers:

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

pipe = pipeline("image-text-to-text", model="FlyCockpit/DeepSeek-V4-Flash-0731-vision")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FlyCockpit/DeepSeek-V4-Flash-0731-vision", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FlyCockpit/DeepSeek-V4-Flash-0731-vision with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FlyCockpit/DeepSeek-V4-Flash-0731-vision"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FlyCockpit/DeepSeek-V4-Flash-0731-vision",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FlyCockpit/DeepSeek-V4-Flash-0731-vision

SGLang

How to use FlyCockpit/DeepSeek-V4-Flash-0731-vision 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 "FlyCockpit/DeepSeek-V4-Flash-0731-vision" \
    --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": "FlyCockpit/DeepSeek-V4-Flash-0731-vision",
		"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 "FlyCockpit/DeepSeek-V4-Flash-0731-vision" \
        --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": "FlyCockpit/DeepSeek-V4-Flash-0731-vision",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FlyCockpit/DeepSeek-V4-Flash-0731-vision with Docker Model Runner:
```
docker model run hf.co/FlyCockpit/DeepSeek-V4-Flash-0731-vision
```

DeepSeek-V4-Flash-0731-vision / README.md

christopher-kapic

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metadata

license: apache-2.0
base_model: deepseek-ai/DeepSeek-V4-Flash-0731
tags:
  - multimodal
  - vision-language
  - deepseek
  - deepseek-v4
  - screenshots
  - adapter
  - dgx-spark
library_name: transformers
pipeline_tag: image-text-to-text

DeepSeek-V4-Flash — Vision Encoder

Give your self-hosted DeepSeek eyes.
Official production tower + projector for DeepSeek-V4-Flash-0731.

This repo is only the vision stack (about 900 MB total). The language model itself is still the upstream 0731 FP8 checkpoint (about 167 GB).

image  →  DeepEncoderV2 tower  →  projector  →  splice at token 129279
                                              →  DeepSeek-V4-Flash-0731

Serve it on 2× DGX Spark:
→ FlyCockpit/DeepSeek-V4-Vision-2x-DGX-Sparks

Download

hf download FlyCockpit/DeepSeek-V4-Flash-0731-vision \
  --local-dir $HOME/dsv4-vision-assets

Or pull just the two weight files:

hf download FlyCockpit/DeepSeek-V4-Flash-0731-vision \
  adapter/latest.pt \
  tower/deepencoder_v2_tower.safetensors \
  --local-dir $HOME/dsv4-vision-assets

Files

path	what	size	md5
`adapter/latest.pt`	use this — final projector	40 MB	`d9b3b3bda8f790ecf7cd5a98e6fb93a5`
`adapter/merged-004800-5af0c5.pt`	same bytes (named by step)	40 MB	`d9b3b3bda8f790ecf7cd5a98e6fb93a5`
`tower/deepencoder_v2_tower.safetensors`	frozen image tower	865 MB	`2d5dba626d816cc367d28b32e744830e`
`config.json`	release metadata	tiny	—

Final training step: 4800. Layout: tiles=2 (257 / 769 / 1281 image tokens).

What it's for

Strength	Notes
UI / screenshot understanding	Primary target
Documents & on-screen text	Strong
Everyday photos	Decent
Click agents on arbitrary real UIs	Not claimed — see below

Synthetic GUI coordinate probes pass; real-page computer-use does not transfer cleanly yet. Full write-up lives in the playbook's capabilities doc.

Compatibility (important)

This encoder maps into 0731's embedding space.

base weights	works?
`deepseek-ai/DeepSeek-V4-Flash-0731`	yes
older Flash / DSpark / NVIDIA NVFP4 of the old line	no

Precision (FP8 vs a future 0731-matched quant) is fine. Different training lineage is not.

Serve

Use the open playbook (Mia-style, worker-first TP=2):

https://github.com/FlyCockpit/DeepSeek-V4-Vision-2x-DGX-Sparks

git clone https://github.com/FlyCockpit/DeepSeek-V4-Vision-2x-DGX-Sparks.git
cd DeepSeek-V4-Vision-2x-DGX-Sparks
cp .env.vision.example .env.vision   # set fabric IPs
bash scripts/download-assets.sh
bash scripts/build-image.sh
bash scripts/start-vision.sh         # run on head

API: http://<head>:8899/v1 · model id: deepseek-v4-flash-0731-vision

License

Adapter packaging in this repo: Apache-2.0
DeepEncoderV2 tower & DeepSeek-V4-Flash-0731 backbone: upstream terms apply