Instructions to use QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact with Transformers:

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

pipe = pipeline("text-generation", model="QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact", 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("QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact", 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 QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact

SGLang

How to use QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact 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 "QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact" \
    --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": "QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact",
		"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 "QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact" \
        --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": "QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact with Docker Model Runner:
```
docker model run hf.co/QuantTrio/DeepSeek-R1-0528-GPTQ-Int4-Int8Mix-Compact
```

tclf90 commited on Jun 4, 2025

Commit

a384f67

verified ·

1 Parent(s): 5b9a8f6

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@@ -26,10 +26,11 @@ Preliminary trials show that converting the entire model to pure Int4 (AWQ/GPTQ)
 Variant Overview
-| Variant     | Characteristics                                                         | File Size | Recommended Scenario                                     |
-|-------------|-------------------------------------------------------------------------|-----------|----------------------------------------------------------|
-| **Compact** | More Int8 layers, higher fidelity                                       | 414 GB    | Ample GPU memory & strict quality needs (e.g., 8 × A100) |
-| **Lite**    | Only the most critical layers upgraded to Int8; size close to pure Int4 | 355 GB    | Resource-constrained, lightweight server deployments     |
 Choose the variant that best matches your hardware and quality requirements.

 Variant Overview
+| Variant     | Characteristics                                                                       | File Size | Recommended Scenario                                                                       |
+|-------------|---------------------------------------------------------------------------------------|-----------|--------------------------------------------------------------------------------------------|
+| **Lite**    | Only the most critical layers upgraded to Int8; size close to pure Int4               | 355 GB    | Resource-constrained, lightweight server deployments                                       |
+| **Compact** | More Int8 layers, relatively higher output quality                                    | 414 GB    | VRAM-sufficient deployments focused on answer quality (e.g., 8 × A100)                     |
+| **Medium**  | Compact plus fully-Int8 attention layers; high quality with reduced long-context loss | 445 GB    | VRAM-rich deployments needing both top answer quality and high concurrency (e.g., 8 × H20) |
 Choose the variant that best matches your hardware and quality requirements.