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
```

ekurtic commited on Apr 8, 2025

Commit

da7c4a6

verified ·

1 Parent(s): dcd41a6

Update README.md

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README.md CHANGED Viewed

@@ -29,19 +29,23 @@ Model outputs were generated with the vLLM engine.
 For reasoning tasks we estimate pass@1 based on 10 runs with different seeds and `temperature=0.6`, `top_p=0.95` and `max_new_tokens=32768`.
-`OpenLLM v1`
-|      Model                         | ArcC | GSM8k | Hellaswag | MMLU | TruthfulQA-mc2 | Winogrande | Average | Recovery |
-|-------------------------------|---------------|-------|-----------|------|------------|------------|---------------|----------|
-| deepseek-ai/DeepSeek-R1 |     72.53      | 95.91 |   89.83    | 87.22 |   59.28    |     82.00     |     81.04      |   100.00    |
-| cognitivecomputations/DeepSeek-R1-AWQ    |     73.12     | 95.15 |   89.07   | 86.86|   60.09    |   82.32    |      81.10     |  100.07  |
-| daslab-testing/DeepSeek-R1-GPTQ-4b-128g-act_order-mse_scale (this) |      72.70      | 95.68 |   89.25   | 86.83|   58.77    |   82.32    |     80.93     | 99.86    |
-`Reasoning tasks`
-|      Model                                   | AIME-2024 pass@1 | MATH-500 pass@1 | GPQA-Diamond pass@1 | Average | Recovery |
-|-----------------------------------------|------------------|-----------------|---------------------|---------|----------|
-| deepseek-ai/DeepSeek-R1                       |     78.34       |       97.24     |        73.383       |  82.99  |   100.00    |
-| cognitivecomputations/DeepSeek-R1-AWQ |     70.67      |       93.64     |        70.456       |  78.25  |  94.29   |
-| daslab-testing/DeepSeek-R1-GPTQ-4b-128g-act_order-mse_scale (this)            |  72.96    |    97.08  |        70.26        |  80.10   |  96.52   |
 ## Reproduction

 For reasoning tasks we estimate pass@1 based on 10 runs with different seeds and `temperature=0.6`, `top_p=0.95` and `max_new_tokens=32768`.
+#### OpenLLM Leaderboard V1 tasks
+|                                              | Recovery (%) | Average Score | ARC-Challenge<br>acc_norm, 25-shot | GSM8k<br>exact_match, 5-shot | HellaSwag<br>acc_norm, 10-shot | MMLU<br>acc, 5-shot | TruthfulQA<br>mc2, 0-shot | WinoGrande<br>acc, 5-shot |
+| :------------------------------------------: | :----------: | :-----------: | :--------------------------------: | :--------------------------: | :----------------------------: | :-----------------: | :-----------------------: | :-----------------------: |
+| deepseek/DeepSeek-R1                         | 100.00       | 81.04         | 72.53                              | 95.91                        | 89.30                          | 87.22               | 59.28                     | 82.00                     |
+| cognitivecomputations/DeepSeek-R1-AWQ        | 100.07       | 81.10         | 73.12                              | 95.15                        | 89.07                          | 86.86               | 60.09                     | 82.32                     |
+| ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g <br> **(this model)**         | 99.86        | 80.93         | 72.70                              | 95.68                        | 89.25                          | 86.83               | 58.77                     | 82.32                     |
+| ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g-experts | 100.30       | 81.28         | 72.53                              | 95.68                        | 89.36                          | 86.99               | 59.77                     | 83.35                     |
+#### Reasoning tasks (AIME-24, GPQA-Diamond, MATH-500)
+|                                              | Recovery (%) | Average Score | AIME 2024<br>pass@1 | MATH-500<br>pass@1 | GPQA Diamond<br>pass@1 |
+| -------------------------------------------- | :----------: | :-----------: | :-----------------: | :----------------: | :--------------------: |
+| deepseek/DeepSeek-R1                         | 100.00       | 82.99         | 78.33               | 97.24              | 73.38                  |
+| cognitivecomputations/DeepSeek-R1-AWQ        | 94.29        | 78.25         | 70.67               | 93.64              | 70.46                  |
+| ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g <br> **(this model)**        | 96.52        | 80.10         | 72.96               | 97.09              | 70.26                  |
+| ISTA-DASLab/DeepSeek-R1-GPTQ-4b-128g-experts | 98.81        | 82.00         | 77.00               | 97.08              | 71.92                  |
 ## Reproduction