Instructions to use huihui-ai/DeepSeek-R1-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use huihui-ai/DeepSeek-R1-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="huihui-ai/DeepSeek-R1-bf16", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("huihui-ai/DeepSeek-R1-bf16", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("huihui-ai/DeepSeek-R1-bf16", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use huihui-ai/DeepSeek-R1-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "huihui-ai/DeepSeek-R1-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huihui-ai/DeepSeek-R1-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/huihui-ai/DeepSeek-R1-bf16

SGLang

How to use huihui-ai/DeepSeek-R1-bf16 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 "huihui-ai/DeepSeek-R1-bf16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huihui-ai/DeepSeek-R1-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "huihui-ai/DeepSeek-R1-bf16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "huihui-ai/DeepSeek-R1-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use huihui-ai/DeepSeek-R1-bf16 with Docker Model Runner:
```
docker model run hf.co/huihui-ai/DeepSeek-R1-bf16
```

huihui-ai/DeepSeek-R1

This model converted from DeepSeek-R1 to BF16.
Here we simply provide the conversion command and related information about ollama.
If needed, we can upload the bf16 version.

FP8 to BF16

Download deepseek-ai/DeepSeek-R1 model, requires approximately 641GB of space.

cd /home/admin/models
huggingface-cli download deepseek-ai/DeepSeek-R1 --local-dir ./deepseek-ai/DeepSeek-R1

Create the environment.

conda create -yn DeepSeek-V3 python=3.12
conda activate DeepSeek-V3
pip install -r requirements.txt

Convert to BF16, requires an additional approximately 1.3 TB of space.
Here, you need to download the transformation code from the "inference" folder of deepseek-ai/DeepSeek-V3

cd deepseek-ai/DeepSeek-V3/inference
python fp8_cast_bf16.py --input-fp8-hf-path /home/admin/models/deepseek-ai/DeepSeek-R1/ --output-bf16-hf-path /home/admin/models/deepseek-ai/DeepSeek-R1-bf16

BF16 to f16.gguf

Use the llama.cpp conversion program to convert DeepSeek-R1-bf16 to gguf format, requires an additional approximately 1.3 TB of space.

python convert_hf_to_gguf.py /home/admin/models/deepseek-ai/DeepSeek-R1-bf16 --outfile /home/admin/models/deepseek-ai/DeepSeek-R1-bf16/ggml-model-f16.gguf --outtype f16

Use the llama.cpp quantitative program to quantitative model (llama-quantize needs to be compiled.), other quant option. Convert first Q2_K, requires an additional approximately 227 GB of space.

llama-quantize /home/admin/models/deepseek-ai/DeepSeek-R1-bf16/ggml-model-f16.gguf  /home/admin/models/deepseek-ai/DeepSeek-R1-bf16/ggml-model-Q2_K.gguf Q2_K

Use llama-cli to test.

llama-cli -m /home/admin/models/deepseek-ai/DeepSeek-R1-bf16/ggml-model-Q2_K.gguf -n 2048

Use with ollama

Note: this model requires Ollama 0.5.5

You can use huihui_ai/deepseek-r1:671b-q2_K directly

ollama run huihui_ai/deepseek-r1:671b-q2_K

or huihui_ai/deepseek-r1:671b-q3_K

ollama run huihui_ai/deepseek-r1:671b-q3_K

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