Instructions to use KORMo-Team/KORMo-10B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KORMo-Team/KORMo-10B-base with Transformers:

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

pipe = pipeline("text-generation", model="KORMo-Team/KORMo-10B-base", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("KORMo-Team/KORMo-10B-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KORMo-Team/KORMo-10B-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KORMo-Team/KORMo-10B-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KORMo-Team/KORMo-10B-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KORMo-Team/KORMo-10B-base

SGLang

How to use KORMo-Team/KORMo-10B-base 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 "KORMo-Team/KORMo-10B-base" \
    --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": "KORMo-Team/KORMo-10B-base",
		"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 "KORMo-Team/KORMo-10B-base" \
        --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": "KORMo-Team/KORMo-10B-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KORMo-Team/KORMo-10B-base with Docker Model Runner:
```
docker model run hf.co/KORMo-Team/KORMo-10B-base
```

mjkmain commited on Oct 13, 2025

Commit

0a7cdae

verified ·

1 Parent(s): 64eb221

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -50,7 +50,7 @@ By releasing every intermediate model and post-training dataset, we give users t
 ## 🔗 Links
-- 📖 **Technical Report**: [👉 Archive paper](https://arxiv.org/pdf/2510.09426)
 - 🤗 **Hugging Face**: [👉 Model Download](https://huggingface.co/KORMo-Team)
 - 💻 **GitHub Repository**: [👉 Training and Inference Code](https://github.com/MLP-Lab/KORMo-tutorial)
 - 🔉 **Tutorial**: [👉 Instruction Tuning over google colab](https://colab.research.google.com/github/MLP-Lab/KORMo-tutorial/blob/main/tutorial/02.sft_qlora.ipynb) [👉 Youtube Tutorial](https://www.youtube.com/@MLPLab)

 ## 🔗 Links
+- 📖 **Technical Report**: [👉 Arxive](https://arxiv.org/pdf/2510.09426)
 - 🤗 **Hugging Face**: [👉 Model Download](https://huggingface.co/KORMo-Team)
 - 💻 **GitHub Repository**: [👉 Training and Inference Code](https://github.com/MLP-Lab/KORMo-tutorial)
 - 🔉 **Tutorial**: [👉 Instruction Tuning over google colab](https://colab.research.google.com/github/MLP-Lab/KORMo-tutorial/blob/main/tutorial/02.sft_qlora.ipynb) [👉 Youtube Tutorial](https://www.youtube.com/@MLPLab)