Instructions to use GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("K-intelligence/Midm-2.0-Base-Instruct")
model = PeftModel.from_pretrained(base_model, "GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA")

Transformers

How to use GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA with Transformers:

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

pipe = pipeline("text-generation", model="GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA

SGLang

How to use GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA 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 "GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA with Docker Model Runner:
```
docker model run hf.co/GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA
```

midm-base-GRPO-lora-tuning-KoreanCultureQA / README.md

GyunYeop

Update README.md

1e46302 verified 11 months ago

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	---
	base_model: K-intelligence/Midm-2.0-Base-Instruct
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:K-intelligence/Midm-2.0-Base-Instruct
	- lora
	- transformers
	---

	# Model Card for Model ID

	<!-- Provide a quick summary of what the model is/does. -->



	## Model Details

	본 모델은 국립국어원 주최 2025년 인공지능의 한국어 능력 평가 경진대회 [2025]한국문화 질의응답(가 유형) ISNLP팀 최종 제출물이다.

	## Model Description

	<!-- Provide a longer summary of what this model is. -->

	학습데이터는 2025년 인공지능의 한국어 능력 평가 경진대회 [2025]한국문화 질의응답(가 유형)에서 주어진 train dataset을 이용하여 학습하였다.

	data link(현재 다운로드 불가): http://kli.korean.go.kr/taskOrdtm/taskList.do?taskOrdtmId=180&clCd=END_TASK&subMenuId=sub01

	해당 과제에서는 Midm-base+QLoRA모델을 기반으로 GRPO + Descriptive Answer Candidate로 학습하였다.

	자세한 학습 방법론 및 학습 코드는 https://github.com/KimGyunYeop/2025_MalPyeong_QA_ISNLP_RLVR_WTA 에서 확인할 수 있다.

	## Model Usage

	```
	from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, GenerationConfig
	from peft import LoraConfig, PeftModelForCausalLM
	import torch

	adapter_path = "GyunYeop/midm-base-GRPO-lora-tuning-KoreanCultureQA"
	lora_config = LoraConfig.from_pretrained(adapter_path)
	model_name = lora_config.base_model_name_or_path
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	generation_config= GenerationConfig.from_pretrained(model_name)

	bnb_config = BitsAndBytesConfig(
	load_in_4bit=True, # 4‑bit 가중치
	bnb_4bit_quant_type="nf4", # Normal‑Float 4
	bnb_4bit_use_double_quant=True, # double‑quant
	bnb_4bit_compute_dtype=torch.bfloat16, # Ada, Hopper, MI300 등
	llm_int8_skip_modules=["lm_head"] # 출력층은 FP16
	)

	base_model = AutoModelForCausalLM.from_pretrained(model_name,
	device_map="cuda:0",
	trust_remote_code=True,
	quantization_config=bnb_config,
	)

	model = PeftModelForCausalLM.from_pretrained(base_model, adapter_path)
	model.load_adapter(adapter_path, subfolder="selection", adapter_name="selection")

	prompt = '단답형 문제에서 정답을 맞추기위해 반드시 충분히 생각해보고 "생각 과정:" 이후에 정답 근거 및 생각과정을 작성 한 다음 이후 최종 답변을 생성할 것 (문제 유형: 단답형 생각과정: {생각과정} 답변: {최종답변} 형태)'
	question = '문제 유형: 단답형 \n 질문: 2005년에 개관하였으며, 교육 및 문화적 목적으로 영화를 상영하는 서울의 유일한 비영리 민간 시네마테크 전용관은 어디인가요?'
	inputs = tokenizer.apply_chat_template(
	[
	{"role": "system", "content": prompt},
	{"role": "user", "content": question}
	],
	tokenize=True,
	add_generation_prompt=True,
	return_tensors="pt"
	)

	answer = model.generate(
	inputs.to("cuda:0"),
	generation_config=generation_config,
	max_new_tokens=1024,
	do_sample=True
	)

	answer_text = tokenizer.decode(answer[0][inputs.shape[-1]:], skip_special_tokens=True)
	print(answer_text)

	```

	<!--
	- Developed by: [More Information Needed]
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	## Uses

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	### Direct Use

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	Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

	## How to Get Started with the Model

	Use the code below to get started with the model.

	[More Information Needed]

	## Training Details

	### Training Data

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	### Training Procedure

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	#### Preprocessing [optional]

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	#### Training Hyperparameters

	- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

	#### Speeds, Sizes, Times [optional]

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	[More Information Needed]

	#### Factors

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	#### Summary



	## Model Examination [optional]

	<!-- Relevant interpretability work for the model goes here -->

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	## Environmental Impact

	<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

	Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

	- Hardware Type: [More Information Needed]
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	- Cloud Provider: [More Information Needed]
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	### Model Architecture and Objective

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	### Framework versions

	- PEFT 0.16.0 -->