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")

# pip install -U transformers accelerate
# 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", device_map="auto")

Inference
HuggingChat
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 CHI-Bench eval results — agent harness: OpenAI Agents SDK

#197

by hlnchen - opened Jun 2

base: refs/heads/main

←

from: refs/pr/197

Discussion Files changed

+40

-0

Files changed (1) hide show

.eval_results/chi-bench.yaml +40 -0

.eval_results/chi-bench.yaml ADDED Viewed

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+# Place at .eval_results/chi-bench.yaml in the DeepSeek V4 Pro model repo.
+# CONFIRM the exact repo id before submitting (org is deepseek-ai).
+# Submit via the model's Community tab as a PR; shows "community-provided" until merged.
+# Values are pass@1 (%) for the best-performing harness for this model: OpenAI Agents SDK.
+- dataset:
+    id: actava/chi-bench
+    task_id: chi_bench
+  value: 14.2
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: OpenAI Agents SDK; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"
+- dataset:
+    id: actava/chi-bench
+    task_id: prior_authorization
+  value: 10.7
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: OpenAI Agents SDK; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"
+- dataset:
+    id: actava/chi-bench
+    task_id: utilization_management
+  value: 28.0
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: OpenAI Agents SDK; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"
+- dataset:
+    id: actava/chi-bench
+    task_id: care_management
+  value: 4.0
+  date: "2026-05-08"
+  source:
+    url: https://arxiv.org/abs/2605.16679
+    name: CHI-Bench
+  notes: "Harness: OpenAI Agents SDK; Protocol: 75 tasks x 3 trials; Metric: pass@1 (%)"