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

Libraries

How to use deepseek-ai/DeepSeek-V4-Pro with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/DeepSeek-V4-Pro with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-V4-Pro

SGLang

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

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

Add SkillsBench v1.1 evaluation result

#205

by bingran-you - opened 2 days ago

base: refs/heads/main

←

from: refs/pr/205

Discussion Files changed

+11

-0

bingran-you

2 days ago

•

edited 2 days ago

Summary

Adds DeepSeek V4 Pro's SkillsBench v1.1 with-skills evaluation result to Hugging Face eval results.

This is a metadata-only PR adding .eval_results/skillsbench.yaml. It does not change model files, code, dependencies, or runtime behavior.

Result

Benchmark: SkillsBench v1.1
Benchmark dataset: https://huggingface.co/datasets/benchflow/skillsbench
Task id: skillsbench_v1_1
Value: 50.1
Mode: with-skills
Harness: BenchFlow
Agent: OpenHands
Coverage: 87 tasks x 3 trials, full 261/261 selected trials
Recomputed date: 2026-06-11

Source

Official export:
https://huggingface.co/datasets/benchflow/skillsbench-leaderboard/raw/main/leaderboard/skillsbench/v1.1/official.json

Trajectory/result archive:
https://huggingface.co/datasets/benchflow/skillsbench-leaderboard

Notes

SkillsBench has paired with-skills and without-skills scores. This PR submits only the with-skills score because the current Hugging Face benchmark dataset defines skillsbench_v1_1 as the default leaderboard task. A no-skills leaderboard can be added separately if/when the benchmark dataset adds a second task id.

Draft: Add SkillsBench v1.1 evaluation result4b1645a2

bingran-you changed pull request title from Draft: Add SkillsBench v1.1 evaluation result to Add SkillsBench v1.1 evaluation result 2 days ago

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