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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "asdfjsdfd/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": "asdfjsdfd/DeepSeek-V4-Pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

DeepSeek-V4-Pro / encoding /tests /test_output_1.txt

asdfjsdfd

Duplicate from deepseek-ai/DeepSeek-V4-Pro

feeb8be 3 days ago

raw

history blame contribute delete

2.39 kB

	<｜begin▁of▁sentence｜>You are a helpful assistant.

	## 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": "get_weather", "description": "Get the weather for a specific location", "parameters": {"type": "object", "properties": {"location": {"type": "string", "description": "The city name"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "description": "Temperature unit"}}, "required": ["location"]}}
	{"name": "search", "description": "Search the web for information", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "Search query"}, "num_results": {"type": "integer", "description": "Number of results to return"}}, "required": ["query"]}}

	You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
	<｜User｜>What's the weather in Beijing?<｜Assistant｜><think>The user wants to know the weather in Beijing. I should use the get_weather tool.</think>

	<｜DSML｜tool_calls>
	<｜DSML｜invoke name="get_weather">
	<｜DSML｜parameter name="location" string="true">Beijing</｜DSML｜parameter>
	<｜DSML｜parameter name="unit" string="true">celsius</｜DSML｜parameter>
	</｜DSML｜invoke>
	</｜DSML｜tool_calls><｜end▁of▁sentence｜><｜User｜><tool_result>{"temperature": 22, "condition": "sunny", "humidity": 45}</tool_result><｜Assistant｜><think>Got the weather data. Let me format a nice response.</think>The weather in Beijing is currently sunny with a temperature of 22°C and 45% humidity.<｜end▁of▁sentence｜>