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 /config.json

danielhanchen

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	{
	"vocab_size": 129280,
	"dim": 4096,
	"moe_inter_dim": 2048,
	"n_layers": 43,
	"n_hash_layers": 3,
	"n_mtp_layers": 3,
	"dspark_block_size": 5,
	"dspark_noise_token_id": 128799,
	"dspark_target_layer_ids": [40, 41, 42],
	"dspark_markov_rank": 256,
	"n_heads": 64,
	"n_routed_experts": 256,
	"n_shared_experts": 1,
	"n_activated_experts": 6,
	"score_func": "sqrtsoftplus",
	"route_scale": 1.5,
	"norm_eps": 1e-20,
	"swiglu_limit": 10.0,
	"q_lora_rank": 1024,
	"head_dim": 512,
	"rope_head_dim": 64,
	"o_groups": 8,
	"o_lora_rank": 1024,
	"window_size": 128,
	"original_seq_len": 65536,
	"rope_theta": 10000,
	"rope_factor": 16,
	"beta_fast": 32,
	"beta_slow": 1,
	"index_n_heads": 64,
	"index_head_dim": 128,
	"index_topk": 512,
	"hc_mult": 4,
	"hc_sinkhorn_iters": 20,
	"dtype": "fp8",
	"scale_fmt": "ue8m0",
	"expert_dtype": "fp4",
	"compress_rope_theta": 160000,
	"compress_ratios": [0, 0, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 0, 0, 0],
	"vision_n_layers": 32,
	"vision_dim": 1024,
	"vision_n_heads": 16,
	"vision_inter_dim": 2816,
	"vision_patch_size": 14,
	"vision_downsample_ratio": 3,
	"vision_max_n_token": 384,
	"vision_min_pixels": 147456,
	"vision_max_wh_ratio": 8
	}