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 / config.json

christopher-kapic

Release final vision encoder: step 4800 adapter + DeepEncoderV2 tower

e555d2d verified 5 days ago

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658 Bytes

	{
	"name": "DeepSeek-V4-Flash-0731-vision",
	"version": "004800",
	"base_model": "deepseek-ai/DeepSeek-V4-Flash-0731",
	"adapter_file": "adapter/merged-004800-5af0c5.pt",
	"adapter_latest": "adapter/latest.pt",
	"tower_file": "tower/deepencoder_v2_tower.safetensors",
	"image_token_id": 129279,
	"image_token": "<｜image｜>",
	"encoder_dim": 896,
	"hidden_size": 4096,
	"image_size": 1024,
	"tokens_per_view": 256,
	"tiles": 2,
	"adapter_params": 20459520,
	"adapter_md5": "d9b3b3bda8f790ecf7cd5a98e6fb93a5",
	"tower_md5": "2d5dba626d816cc367d28b32e744830e",
	"playbook": "https://github.com/FlyCockpit/DeepSeek-V4-Vision-2x-DGX-Sparks"
	}