Instructions to use sonselfa/kli205-qwen35-9b-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sonselfa/kli205-qwen35-9b-tuned with Transformers:

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

pipe = pipeline("text-generation", model="sonselfa/kli205-qwen35-9b-tuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sonselfa/kli205-qwen35-9b-tuned")
model = AutoModelForCausalLM.from_pretrained("sonselfa/kli205-qwen35-9b-tuned", 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 sonselfa/kli205-qwen35-9b-tuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sonselfa/kli205-qwen35-9b-tuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sonselfa/kli205-qwen35-9b-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sonselfa/kli205-qwen35-9b-tuned

SGLang

How to use sonselfa/kli205-qwen35-9b-tuned 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 "sonselfa/kli205-qwen35-9b-tuned" \
    --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": "sonselfa/kli205-qwen35-9b-tuned",
		"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 "sonselfa/kli205-qwen35-9b-tuned" \
        --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": "sonselfa/kli205-qwen35-9b-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sonselfa/kli205-qwen35-9b-tuned with Docker Model Runner:
```
docker model run hf.co/sonselfa/kli205-qwen35-9b-tuned
```

KLI205 Qwen3.5-9B Tuned

Qwen/Qwen3.5-9B에 한국어 논증적 글 채점용 LoRA를 병합한 모델입니다.

Artifact

Base model: Qwen/Qwen3.5-9B
Adapter: LoRA rank 32, alpha 64
Export dtype: BF16
Format: merged SafeTensors (표준 Transformers/vLLM 로드용)
License: Apache-2.0

이 저장소에는 모델 실행 파일만 포함되며 학습 원문, 평가 정답, RAG 자료, 개인정보 및 optimizer state는 포함하지 않습니다.

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "sonselfa/kli205-qwen35-9b-tuned"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")

Status

This is the first public merged checkpoint. Application-specific output-contract validation and score calibration may still be performed separately before final competition submission.

Downloads last month: 177

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for sonselfa/kli205-qwen35-9b-tuned

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Adapter

(556)

this model