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
- PEFT
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