Instructions to use WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp with Transformers:

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

pipe = pipeline("text-generation", model="WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp")
model = AutoModelForCausalLM.from_pretrained("WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp

SGLang

How to use WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp 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 "WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp" \
    --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": "WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp",
		"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 "WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp" \
        --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": "WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp with Docker Model Runner:
```
docker model run hf.co/WaveCut/DeepSeek-V4-Flash-0731-REAM96-111B-exp
```

DeepSeek-V4-Flash-0731-REAM96-111B-exp / model-00029-of-00044.safetensors

WaveCut

Publish model-00029-of-00044.safetensors

2286b4d verified 24 days ago

Download

History Blame Contribute Delete

1.43 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: d9ac73948cfee7372283d949f3d1c06fb316503756cd782a9ec53826f93a9542
Size of remote file:: 1.43 GB
SHA256:: b535d1ee83079b55638cc5ee7821bcb01a0d1ac6af29f06286aeb4ab0a3513b7

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.