Instructions to use deepseek-ai/DeepSeek-V4.1-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/DeepSeek-V4.1-Flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="deepseek-ai/DeepSeek-V4.1-Flash")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V4.1-Flash", device_map="auto")

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/DeepSeek-V4.1-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepseek-ai/DeepSeek-V4.1-Flash"
# 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-V4.1-Flash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-V4.1-Flash

SGLang

How to use deepseek-ai/DeepSeek-V4.1-Flash 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-V4.1-Flash" \
    --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-V4.1-Flash",
		"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-V4.1-Flash" \
        --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-V4.1-Flash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-V4.1-Flash with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-V4.1-Flash
```

Update README.md

by zjxia - opened 5 days ago

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

+18

-0

Files changed (1) hide show

README.md +18 -0

README.md CHANGED Viewed

@@ -39,6 +39,24 @@ examples under `inference/examples/` encode to identical prompts and token IDs.
 See [`inference/README.md`](inference/README.md) for dependency installation,
 checkpoint conversion, and TXT/JSON inference commands.
 ## License
 This repository is licensed under the [MIT License](LICENSE).

 See [`inference/README.md`](inference/README.md) for dependency installation,
 checkpoint conversion, and TXT/JSON inference commands.
+## How to Run with SGLang
+Enable DSpark with `--speculative-algorithm DSPARK` and do not set a separate `--speculative-draft-model-path` as the target and draft weights therefore come from the same checkpoint. See the [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4_1) for detailed instructions, benchmarks and other hardwares configurations.
+```sh
+sglang serve \
+  --trust-remote-code \
+  --model-path deepseek-ai/DeepSeek-V4.1-Flash \
+  --tp 4 \
+  --ep-size 4 \
+  --mem-fraction-static 0.8 \
+  --speculative-algorithm DSPARK \
+  --speculative-dspark-block-size 5 \
+  --reasoning-parser auto \
+  --tool-call-parser auto \
+  --host 0.0.0.0 \
+  --port 30000
+```
 ## License
 This repository is licensed under the [MIT License](LICENSE).