Instructions to use meituan/DeepSeek-R1-Block-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meituan/DeepSeek-R1-Block-INT8 with Transformers:

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

pipe = pipeline("text-generation", model="meituan/DeepSeek-R1-Block-INT8", 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("meituan/DeepSeek-R1-Block-INT8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("meituan/DeepSeek-R1-Block-INT8", 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 meituan/DeepSeek-R1-Block-INT8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meituan/DeepSeek-R1-Block-INT8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meituan/DeepSeek-R1-Block-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meituan/DeepSeek-R1-Block-INT8

SGLang

How to use meituan/DeepSeek-R1-Block-INT8 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 "meituan/DeepSeek-R1-Block-INT8" \
    --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": "meituan/DeepSeek-R1-Block-INT8",
		"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 "meituan/DeepSeek-R1-Block-INT8" \
        --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": "meituan/DeepSeek-R1-Block-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meituan/DeepSeek-R1-Block-INT8 with Docker Model Runner:
```
docker model run hf.co/meituan/DeepSeek-R1-Block-INT8
```

pkumc

yuanzu commited on Feb 24, 2025

Commit

160722a

verified ·

1 Parent(s): d4440e1

Update README.md (#5)

Browse files

- Update README.md (1f37595e7f0ca7d27fbf9224ae97095afaedf72e)

Co-authored-by: laixinn <yuanzu@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -16,14 +16,14 @@ In benchmarking, we observe **no accuracy loss** and up to **30\%** performance
 ## 1. Benchmarking Result (detailed in [PULL REQUEST](https://github.com/sgl-project/sglang/pull/3730)):
 | Model  | Config | Accuracy (GSM8K) | Accuracy (MMLU) | Output Throughput(qps=128) | Output Throughput(bs=1) |
 |--------|--------|-------------------|----------------|------------------------------|--------------------------|
-| BF16 R1 | (A100\*16)x2 | 95.8              | 87.1           | 4450.02 (+33%)                | 44.18 (+18%)             |
-| INT8 R1 | A100\*32  | 95.5              | 87.1           | 3342.29                       | 37.20                     |
 ## 2. Quantization Process
 We apply INT8 quantization to the BF16 checkpoints.
-The weight scales are determined by dividing the block-wise maximum of element values by the INT8 type maximum.
 To generate this weight, run the provided script in the ``./inference`` directory:

 ## 1. Benchmarking Result (detailed in [PULL REQUEST](https://github.com/sgl-project/sglang/pull/3730)):
 | Model  | Config | Accuracy (GSM8K) | Accuracy (MMLU) | Output Throughput(qps=128) | Output Throughput(bs=1) |
 |--------|--------|-------------------|----------------|------------------------------|--------------------------|
+| BF16 R1 | A100\*32  | 95.5              | 87.1           | 3342.29                       | 37.20                     |
+| INT8 R1 | (A100\*16)x2 | **95.8**              | **87.1**           | 4450.02 **(+33%)**                | 44.18 **(+18%)**             |
 ## 2. Quantization Process
 We apply INT8 quantization to the BF16 checkpoints.
+The quantization scales are determined by dividing the block-wise maximum of element values by the INT8 type maximum.
 To generate this weight, run the provided script in the ``./inference`` directory: