Instructions to use jaeyong2/Qwen3-1.7B-DPO-Peft-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaeyong2/Qwen3-1.7B-DPO-Peft-Preview with Transformers:

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

pipe = pipeline("text-generation", model="jaeyong2/Qwen3-1.7B-DPO-Peft-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaeyong2/Qwen3-1.7B-DPO-Peft-Preview")
model = AutoModelForCausalLM.from_pretrained("jaeyong2/Qwen3-1.7B-DPO-Peft-Preview", device_map="auto")
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 Settings

vLLM

How to use jaeyong2/Qwen3-1.7B-DPO-Peft-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaeyong2/Qwen3-1.7B-DPO-Peft-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaeyong2/Qwen3-1.7B-DPO-Peft-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaeyong2/Qwen3-1.7B-DPO-Peft-Preview

SGLang

How to use jaeyong2/Qwen3-1.7B-DPO-Peft-Preview 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 "jaeyong2/Qwen3-1.7B-DPO-Peft-Preview" \
    --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": "jaeyong2/Qwen3-1.7B-DPO-Peft-Preview",
		"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 "jaeyong2/Qwen3-1.7B-DPO-Peft-Preview" \
        --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": "jaeyong2/Qwen3-1.7B-DPO-Peft-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jaeyong2/Qwen3-1.7B-DPO-Peft-Preview with Docker Model Runner:
```
docker model run hf.co/jaeyong2/Qwen3-1.7B-DPO-Peft-Preview
```

Evaluation

!lm_eval --model hf \
    --model_args pretrained=jaeyong2/Qwen3-0.6B-DPO-Ja-Peft \
    --tasks kmmlu,mmlu,japanese_leaderboard,gsm8k \
    --device cuda:0 \
    --batch_size 1

	Qwen3-1.7B-DPO-peft	Qwen3-1.7B
MMLU	0.55	0.55
ja_leaderboard_jaqket_v2	0.35	0.34
ja_leaderboard_jcommonsenseqa	0.48	0.46
ja_leaderboard_jnli	0.28	0.23
ja_leaderboard_jsquad	0.25	0.22
ja_leaderboard_marc_ja	0.65	0.74
ja_leaderboard_mgsm	0.42	0.40
ja_leaderboard_xlsum	0.10	0.11
ja_leaderboard_xwinograd	0.58	0.58
GSM8K	0.70	0.69
KMMLU	0.36	0.36

License

Qwen/Qwen3-0.6B : https://choosealicense.com/licenses/apache-2.0/

Acknowledgement

This research is supported by TPU Research Cloud program.

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Safetensors

Model size

2B params

Tensor type

BF16