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

Libraries

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

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

pipe = pipeline("text-generation", model="meituan/DeepSeek-R1-Channel-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-Channel-INT8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("meituan/DeepSeek-R1-Channel-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 Settings

vLLM

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

Use Docker

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

SGLang

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

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

Update inference/bf16_cast_channel_int8.py

#10

by HandH1998 - opened Mar 12, 2025

base: refs/heads/main

←

from: refs/pr/10

Discussion Files changed

+15

-2

Files changed (1) hide show

inference/bf16_cast_channel_int8.py +15 -2

inference/bf16_cast_channel_int8.py CHANGED Viewed

@@ -35,8 +35,21 @@ def main(bf16_path, int8_path, model_name="deepseek-ai/DeepSeek-R1"):
         # modify config.json and save it
         config = json.load(open(config_file))
-        # delete quantization_config
-        config.pop("quantization_config", None)
         with open(config_file, "w", encoding="utf-8") as f:
             json.dump(config, f, indent=2, ensure_ascii=False, sort_keys=True)
         print(f"config.json modified and saved to {config_file}")

         # modify config.json and save it
         config = json.load(open(config_file))
+        if "quantization_config" in config:
+            quant_config = config["quantization_config"]
+            quant_config.pop("fmt", None)
+            quant_config.pop("weight_block_size", None)
+            quant_config["quant_method"] = "w8a8_int8"
+            quant_config["group_size"] = -1
+            quant_config["activation_scheme"] = "dynamic"
+            quant_config["bits"] = 8
+        else:
+            config["quantization_config"] = {
+                "activation_scheme": "dynamic",
+                "quant_method": "w8a8_int8",
+                "group_size": -1,
+                "bits": 8
+            }
         with open(config_file, "w", encoding="utf-8") as f:
             json.dump(config, f, indent=2, ensure_ascii=False, sort_keys=True)
         print(f"config.json modified and saved to {config_file}")