Instructions to use HwanChang0106/qwen3-seq-sort-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HwanChang0106/qwen3-seq-sort-ko with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
model = PeftModel.from_pretrained(base_model, "HwanChang0106/qwen3-seq-sort-ko")

Transformers

How to use HwanChang0106/qwen3-seq-sort-ko with Transformers:

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

pipe = pipeline("text-generation", model="HwanChang0106/qwen3-seq-sort-ko")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("HwanChang0106/qwen3-seq-sort-ko", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HwanChang0106/qwen3-seq-sort-ko with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HwanChang0106/qwen3-seq-sort-ko"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HwanChang0106/qwen3-seq-sort-ko",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HwanChang0106/qwen3-seq-sort-ko

SGLang

How to use HwanChang0106/qwen3-seq-sort-ko 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 "HwanChang0106/qwen3-seq-sort-ko" \
    --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": "HwanChang0106/qwen3-seq-sort-ko",
		"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 "HwanChang0106/qwen3-seq-sort-ko" \
        --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": "HwanChang0106/qwen3-seq-sort-ko",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HwanChang0106/qwen3-seq-sort-ko with Docker Model Runner:
```
docker model run hf.co/HwanChang0106/qwen3-seq-sort-ko
```

Qwen3-8B LoRA: Korean Sequence Sorting

This repo contains only the LoRA adapter and tokenizer/template files for a Qwen3-8B base model. Use it by loading the base model first and then applying this adapter with PEFT.

Usage

Base model: Qwen/Qwen3-8B (replace with your exact base if different)
Adapter repo (this repo): HwanChang0106/qwen3-seq-sort-ko

Python example:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen3-8B"  # base model
lora_id = "HwanChang0106/qwen3-seq-sort-ko"  # this repo

tok = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, lora_id)

prompt = "다음 수열을 오름차순으로 정렬하세요: 3, 1, 2"
inputs = tok(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64)
print(tok.decode(outputs[0], skip_special_tokens=True))

If you prefer to merge the LoRA into the base weights for pure HF usage:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
model = PeftModel.from_pretrained(base, lora_id)
merged = model.merge_and_unload()  # returns a merged base model

Files

adapter: adapter_model.safetensors, adapter_config.json
tokenizer/template: tokenizer.json, tokenizer_config.json, special_tokens_map.json, added_tokens.json, vocab.json, merges.txt, chat_template.jinja

Optimizer or training state files are not included.

Notes

Ensure trust_remote_code=True when using Qwen models.
Match the exact base checkpoint you trained on for best results.

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