Instructions to use RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8

SGLang

How to use RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8 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 "RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8" \
    --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": "RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8",
		"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 "RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8" \
        --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": "RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8
```

DeepSeek-V4-Pro-NVFP4-FP8 / README.md

kylesayrs

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	---
	license: mit
	base_model:
	- deepseek-ai/DeepSeek-V4-Pro
	library_name: transformers
	tags:
	- compressed-tensors
	- vLLM
	---

	# RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8

	This is a quantized version of `deepseek-ai/DeepSeek-V4-Pro` with MoE layers quantized to NVFP4 and attention layers quantized to FP8 block

	## Usage

	This model is intended for deployment with vLLM and requires the following branch: https://github.com/vllm-project/vllm/pull/41276.
	You can serve the model using

	```bash
	vllm serve RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8-BLOCK --tensor_parallel_size 8 --kv_cache_dtype=fp8
	```

	## Creation Process

	This model was created using [LLM Compressor](https://github.com/vllm-project/llm-compressor). The example script can be found in `examples/quantizing_moe/deepseek_v4_pro_example.py` [[DSV4] DeepSeekV4 Pro](https://github.com/vllm-project/llm-compressor/pull/2858). Quantizing the model with data parallelism and 6xA100 takes about 3 hours.


	## Evaluation ##
	\| Benchmark \| `deepseek-ai/DeepSeek-V4-Pro-Base` \| `deepseek-ai/DeepSeek-V4-Pro` \| `RedHatAI/DeepSeek-V4-Pro-NVFP4-FP8` \|
	\| - \| - \| -\| - \|
	\| GPQA \| \| 90.1 \| 0.93 (380/792 samples) \|
	\| GSM8K \| 91.1 \| 92.6 \| 91.0 \|