Instructions to use Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128 with Transformers:

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

pipe = pipeline("text-generation", model="Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128")
model = AutoModelForCausalLM.from_pretrained("Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128")
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 Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128

SGLang

How to use Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128 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 "Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128" \
    --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": "Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128",
		"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 "Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128" \
        --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": "Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128 with Docker Model Runner:
```
docker model run hf.co/Hyun9junn/K-EXAONE-236B-A23B-W4A16-G128
```

Hyun9junn commited on 6 days ago

Commit

f920227

verified ·

1 Parent(s): e015684

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@@ -16,6 +16,11 @@ base_model: LGAI-EXAONE/K-EXAONE-236B-A23B
 ---
 # K-EXAONE-236B-A23B-W4A16-G128
 ### 🔄 (2026-05-04) Change Calibration Dataset
 * use [`beomi/KoAlpaca-RealQA`](https://huggingface.co/datasets/beomi/KoAlpaca-RealQA) (512 samples, sequence length 2048)
 ### 🔄 (2026-04-15) Improved Quantization - scale-up calibration dataset

 ---
 # K-EXAONE-236B-A23B-W4A16-G128
+### 🔄 (2026-06-04) Change Calibration Dataset
+* use mixed calibration datasets (2,048 samples, sequence length 8,192)
+  * [`beomi/KoAlpaca-RealQA`](https://huggingface.co/datasets/beomi/KoAlpaca-RealQA): 512 samples
+  * [`ChuGyouk/HFH4_ultrachat_200k_ko`](https://huggingface.co/datasets/ChuGyouk/HFH4_ultrachat_200k_ko): 512 samples
+  * [`HuggingFaceH4/ultrachat_200k`](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k): 1,024 samples
 ### 🔄 (2026-05-04) Change Calibration Dataset
 * use [`beomi/KoAlpaca-RealQA`](https://huggingface.co/datasets/beomi/KoAlpaca-RealQA) (512 samples, sequence length 2048)
 ### 🔄 (2026-04-15) Improved Quantization - scale-up calibration dataset