Instructions to use drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit with Transformers:

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

pipe = pipeline("image-text-to-text", model="drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit")
model = AutoModelForCausalLM.from_pretrained("drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit

SGLang

How to use drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit 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 "drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit" \
    --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": "drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit",
		"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 "drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit" \
        --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": "drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit with Docker Model Runner:
```
docker model run hf.co/drowzeys/keys-DeepSeekV4Flash-Vision-EXP-ablit
```

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This model has had safety refusals removed. That makes it useful for red-teaming, security research, evaluation, and unfiltered assistant tasks — and also removes guardrails a user must therefore supply themselves.

Prohibited uses (you must agree before access is granted):

Anything involving the sexual exploitation or endangerment of minors.
You must be of age 18 years or older to use and download this model.
You agree any information generated that can cause harm in terms of generating recipe, knowledge to make any materials/substances is your own input and responsibility. You will be accountable for any harm/damage caused by your action/input.
Content promoting self-harm or suicide.
Generation of material that is illegal in your jurisdiction, or that targets real individuals for harassment, doxxing, or fraud.
Any use prohibited by the upstream DeepSeek license.

You are responsible for adding appropriate safety filtering, human review, and access controls for your deployment. The weights are provided as-is, with no warranty. The license is inherited from the upstream DeepSeek base model — review and comply with it before use or redistribution.

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