Instructions to use Deepdive404/DeepSeek-V4-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Deepdive404/DeepSeek-V4-Pro with Transformers:

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

pipe = pipeline("text-generation", model="Deepdive404/DeepSeek-V4-Pro")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Deepdive404/DeepSeek-V4-Pro")
model = AutoModelForCausalLM.from_pretrained("Deepdive404/DeepSeek-V4-Pro")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Deepdive404/DeepSeek-V4-Pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Deepdive404/DeepSeek-V4-Pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Deepdive404/DeepSeek-V4-Pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Deepdive404/DeepSeek-V4-Pro

SGLang

How to use Deepdive404/DeepSeek-V4-Pro 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 "Deepdive404/DeepSeek-V4-Pro" \
    --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": "Deepdive404/DeepSeek-V4-Pro",
		"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 "Deepdive404/DeepSeek-V4-Pro" \
        --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": "Deepdive404/DeepSeek-V4-Pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Deepdive404/DeepSeek-V4-Pro with Docker Model Runner:
```
docker model run hf.co/Deepdive404/DeepSeek-V4-Pro
```

DeepSeek-V4-Pro / encoding /tests /test_output_3.txt

Deepdive404

Duplicate from deepseek-ai/DeepSeek-V4-Pro

5bfd679 13 days ago

raw

history blame contribute delete

3.31 kB

	<｜begin▁of▁sentence｜>该助手为DeepSeek，由深度求索公司创造。<｜latest_reminder｜>2026-02-21,星期六,广州,App,中文<｜User｜>小柴胡冲剂和布洛芬能一起吃吗？

	CITATION FORMAT: 【{cursor_id}†L{start_line_id}(-L{end_line_id})?】

	## Tools

	You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<｜DSML｜tool_calls>" block like the following:

	<｜DSML｜tool_calls>
	<｜DSML｜invoke name="$TOOL_NAME">
	<｜DSML｜parameter name="$PARAMETER_NAME" string="true\|false">$PARAMETER_VALUE</｜DSML｜parameter>
	...
	</｜DSML｜invoke>
	<｜DSML｜invoke name="$TOOL_NAME2">
	...
	</｜DSML｜invoke>
	</｜DSML｜tool_calls>

	String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.

	If thinking_mode is enabled (triggered by <think>), you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response.

	Otherwise, output directly after </think> with tool calls or final response.

	### Available Tool Schemas

	{"name": "search", "description": "Web search. Split multiple queries with '\|\|'.", "parameters": {"type": "object", "properties": {"queries": {"type": "string", "description": "query1\|\|query2"}}, "required": ["queries"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
	{"name": "open", "description": "Batch open IDs (format 【{id}†...】) or URLs.", "parameters": {"type": "object", "properties": {"open_list": {"type": "array", "items": {"type": "object", "properties": {"id": {"description": "ID or URL", "anyOf": [{"type": "integer"}, {"type": "string"}], "default": -1}, "cursor": {"type": "integer", "description": "", "default": -1}, "loc": {"type": "integer", "description": "Start line", "default": -1}, "num_lines": {"type": "integer", "description": "", "default": -1}, "view_source": {"type": "boolean", "description": "", "default": false}}, "additionalProperties": false}, "description": ""}}, "required": ["open_list"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
	{"name": "find", "description": "Find exact text pattern in pages.", "parameters": {"type": "object", "properties": {"find_list": {"type": "array", "items": {"type": "object", "properties": {"pattern": {"type": "string", "description": ""}, "cursor": {"type": "integer", "description": "", "default": -1}}, "required": ["pattern"], "additionalProperties": false}, "description": ""}}, "required": ["find_list"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}

	You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
	<｜Assistant｜><think>用户想知道小柴胡冲剂和布洛芬能否一起服用。</think>

	<｜DSML｜tool_calls>
	<｜DSML｜invoke name="search">
	<｜DSML｜parameter name="queries" string="true">小柴胡冲剂布洛芬相互作用一起吃</｜DSML｜parameter>
	</｜DSML｜invoke>
	</｜DSML｜tool_calls><｜end▁of▁sentence｜><｜User｜><tool_result>[0]</tool_result><｜Assistant｜><think>现在开始组织回答。</think>请及时就医。<｜end▁of▁sentence｜>