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

Libraries

How to use opensourcerelease/DeepSeek-R1-Zero-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="opensourcerelease/DeepSeek-R1-Zero-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("opensourcerelease/DeepSeek-R1-Zero-bf16", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("opensourcerelease/DeepSeek-R1-Zero-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 opensourcerelease/DeepSeek-R1-Zero-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "opensourcerelease/DeepSeek-R1-Zero-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": "opensourcerelease/DeepSeek-R1-Zero-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/opensourcerelease/DeepSeek-R1-Zero-bf16

SGLang

How to use opensourcerelease/DeepSeek-R1-Zero-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 "opensourcerelease/DeepSeek-R1-Zero-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": "opensourcerelease/DeepSeek-R1-Zero-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 "opensourcerelease/DeepSeek-R1-Zero-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": "opensourcerelease/DeepSeek-R1-Zero-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

OpenSourceRonin commited on Jan 21, 2025

Commit

e4dae8b

verified ·

1 Parent(s): bbee431

Upload 9 files

Browse files

Files changed (1) hide show

config.json +0 -9

config.json CHANGED Viewed

@@ -36,15 +36,6 @@
   "q_lora_rank": 1536,
   "qk_nope_head_dim": 128,
   "qk_rope_head_dim": 64,
-  "quantization_config": {
-    "activation_scheme": "dynamic",
-    "fmt": "e4m3",
-    "quant_method": "fp8",
-    "weight_block_size": [
-      128,
-      128
-    ]
-  },
   "rms_norm_eps": 1e-06,
   "rope_scaling": {
     "beta_fast": 32,

   "q_lora_rank": 1536,
   "qk_nope_head_dim": 128,
   "qk_rope_head_dim": 64,
   "rms_norm_eps": 1e-06,
   "rope_scaling": {
     "beta_fast": 32,