Instructions to use JetLM/SDAR-1.7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JetLM/SDAR-1.7B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="JetLM/SDAR-1.7B-Chat", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("JetLM/SDAR-1.7B-Chat", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JetLM/SDAR-1.7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JetLM/SDAR-1.7B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetLM/SDAR-1.7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JetLM/SDAR-1.7B-Chat

SGLang

How to use JetLM/SDAR-1.7B-Chat 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 "JetLM/SDAR-1.7B-Chat" \
    --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": "JetLM/SDAR-1.7B-Chat",
		"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 "JetLM/SDAR-1.7B-Chat" \
        --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": "JetLM/SDAR-1.7B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JetLM/SDAR-1.7B-Chat with Docker Model Runner:
```
docker model run hf.co/JetLM/SDAR-1.7B-Chat
```

chengs18 commited on Jul 20, 2025

Commit

a1f43ea

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@@ -6,7 +6,6 @@ library_name: transformers
 **SDAR**(**S**ynergy of **D**iffusion and **A**uto**R**egression)-model is a new large language model that integrates autoregressive (AR) and discrete diffusion modeling strategies. It combines the efficient training paradigm of AR models with the highly parallel inference capability of diffusion models, while delivering performance fully on par with SOTA opensource AR models. At the same time, SDAR sets a new benchmark as the most powerful diffusion language model to date.
----
 # Performance of SDAR-1.7B-Chat on various benchmarks
@@ -31,7 +30,6 @@ evaluation settings:
 **Note**: The 4B, 8B, and 30B models are coming soon. Performance results for these models will be released in the near future.
 ## Inference
 The inference code will come soon

 **SDAR**(**S**ynergy of **D**iffusion and **A**uto**R**egression)-model is a new large language model that integrates autoregressive (AR) and discrete diffusion modeling strategies. It combines the efficient training paradigm of AR models with the highly parallel inference capability of diffusion models, while delivering performance fully on par with SOTA opensource AR models. At the same time, SDAR sets a new benchmark as the most powerful diffusion language model to date.
 # Performance of SDAR-1.7B-Chat on various benchmarks
 **Note**: The 4B, 8B, and 30B models are coming soon. Performance results for these models will be released in the near future.
 ## Inference
 The inference code will come soon