Instructions to use ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g with Transformers:

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

pipe = pipeline("text-generation", model="ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g", 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("ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g", 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 ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g

SGLang

How to use ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g 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 "ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g" \
    --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": "ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g",
		"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 "ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g" \
        --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": "ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g
```

SpiridonSunRotator commited on Apr 8, 2025

Commit

dcd41a6

verified ·

1 Parent(s): 72c64b3

Update README.md

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@@ -49,7 +49,7 @@ The results were obtained using the following commands:
 `OpenLLM v1`
 ```bash
-MODEL=daslab-testing/DeepSeek-R1-GPTQ-4b-128g-act_order-mse_scale
 MODEL_ARGS="pretrained=$MODEL,dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True"
 lm_eval \
@@ -63,7 +63,7 @@ For reasoning evals we adopted the protocol from the [open-r1 repository](https:
 `Reasoning tasks`
 ```bash
-MODEL=daslab-testing/DeepSeek-R1-GPTQ-4b-128g-act_order-mse_scale
 MODEL_ARGS="pretrained=$MODEL,dtype=bfloat16,max_model_length=38768,gpu_memory_utilization=0.8,tensor_parallel_size=8,add_special_tokens=false,generation_parameters={\"max_new_tokens\":32768,\"temperature\":0.6,\"top_p\":0.95,\"seed\":7686}"
 export VLLM_WORKER_MULTIPROC_METHOD=spawn

 `OpenLLM v1`
 ```bash
+MODEL=ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g-act_order-mse_scale
 MODEL_ARGS="pretrained=$MODEL,dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True"
 lm_eval \
 `Reasoning tasks`
 ```bash
+MODEL=ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g-act_order-mse_scale
 MODEL_ARGS="pretrained=$MODEL,dtype=bfloat16,max_model_length=38768,gpu_memory_utilization=0.8,tensor_parallel_size=8,add_special_tokens=false,generation_parameters={\"max_new_tokens\":32768,\"temperature\":0.6,\"top_p\":0.95,\"seed\":7686}"
 export VLLM_WORKER_MULTIPROC_METHOD=spawn