Instructions to use UT-IFML/dQwen3-1.7B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UT-IFML/dQwen3-1.7B-Base with Transformers:

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

pipe = pipeline("text-generation", model="UT-IFML/dQwen3-1.7B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("UT-IFML/dQwen3-1.7B-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UT-IFML/dQwen3-1.7B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UT-IFML/dQwen3-1.7B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UT-IFML/dQwen3-1.7B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UT-IFML/dQwen3-1.7B-Base

SGLang

How to use UT-IFML/dQwen3-1.7B-Base 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 "UT-IFML/dQwen3-1.7B-Base" \
    --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": "UT-IFML/dQwen3-1.7B-Base",
		"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 "UT-IFML/dQwen3-1.7B-Base" \
        --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": "UT-IFML/dQwen3-1.7B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UT-IFML/dQwen3-1.7B-Base with Docker Model Runner:
```
docker model run hf.co/UT-IFML/dQwen3-1.7B-Base
```

dQwen3-1.7B-Base

File size: 870 Bytes

{
  "output_dir": "/scratch/11079/antonxue/cache/ADLMC/diffuqwen35_yolo25k_17q3_20260807_131436_50k",
  "base_model": "/scratch/11079/antonxue/cache/huggingface/hub/models--Qwen--Qwen3-1.7B/snapshots/70d244cc86ccca08cf5af4e1e306ecf908b1ad5e",
  "data_dir": "/scratch/11079/antonxue/adlmc_pretrain",
  "mixture": "v3",
  "max_length": 4096,
  "stable_steps": 44000,
  "decay_steps": 5000,
  "short_batch_probability": 0.0,
  "truncate_probability": 0.1,
  "truncate_min_frac": 0.5,
  "batch_size": 8,
  "gradient_accumulation_steps": 2,
  "learning_rate": 2e-05,
  "warmup_steps": 1000,
  "weight_decay": 0.01,
  "logging_steps": 5,
  "save_steps": 1000,
  "save_total_limit": 7,
  "seed": 42,
  "dataloader_num_workers": 8,
  "gradient_checkpointing": true,
  "resume_from_checkpoint": "/scratch/11079/antonxue/cache/ADLMC/diffuqwen35_yolo25k_17q3_20260807_131436_50k"
}