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

Libraries

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

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

pipe = pipeline("text-generation", model="unsloth/DeepSeek-V4-Flash-Vision-Exp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsloth/DeepSeek-V4-Flash-Vision-Exp")
model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-V4-Flash-Vision-Exp", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use unsloth/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 "unsloth/DeepSeek-V4-Flash-Vision-Exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/DeepSeek-V4-Flash-Vision-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use unsloth/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 "unsloth/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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/DeepSeek-V4-Flash-Vision-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "unsloth/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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/DeepSeek-V4-Flash-Vision-Exp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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

danielhanchen

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	# Minimal inference

	This directory contains a readable reference implementation rather than a
	production serving engine. The model code includes Vision + Aligner, DFlash,
	MoE, Hyper-Connections, and `Transformer.forward_spec()` for the DSpark forward
	path. The generation loop remains straightforward autoregressive sampling.

	## Install

	```bash
	python -m pip install -r requirements.txt
	```

	## Convert Hugging Face weights

	The reference runtime uses one converted checkpoint file per tensor-parallel
	rank. From this directory:

	```bash
	export HF_CKPT_PATH=/path/to/DeepSeek-V4-Flash-Vision-Exp-HF
	export SAVE_PATH=/path/to/DeepSeek-V4-Flash-Vision-Exp-TP4
	export MP=4

	python convert.py \
	--hf-ckpt-path "${HF_CKPT_PATH}" \
	--save-path "${SAVE_PATH}" \
	--n-experts 256 \
	--model-parallel "${MP}" \
	--expert-dtype fp4
	```

	`convert.py` also copies `tokenizer.json` and `tokenizer_config.json` into the
	converted checkpoint directory. `--tokenizer-path` can be used when tokenizer
	files live outside the weight directory.

	## Run the equivalent TXT and JSON examples

	```bash
	export CKPT_PATH=/path/to/DeepSeek-V4-Flash-Vision-Exp-TP4
	export MP=4

	INPUT_FILE=examples/example_vl.txt ./run.sh
	INPUT_FILE=examples/example_vl_harmony.json ./run.sh
	```

	The two files express the same interleaved two-image prompt and therefore
	produce identical encoded prompts and input token IDs.

	For interactive chat:

	```bash
	torchrun --nproc-per-node "${MP}" generate.py \
	--ckpt-path "${CKPT_PATH}" \
	--config config.json \
	--interactive \
	--temperature 1.0
	```

	For multi-node execution, pass the usual `torchrun --nnodes`, `--node-rank`,
	`--master-addr`, and `--master-port` arguments before `generate.py`.

	## Preprocessing tests

	From the repository root:

	```bash
	python -m pytest -q \
	encoding/test_encoding_dsv4.py \
	inference/test_image_processor.py
	```