Instructions to use PracticeLLM/Custom-KoLLM-13B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PracticeLLM/Custom-KoLLM-13B-v2 with Transformers:

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

pipe = pipeline("text-generation", model="PracticeLLM/Custom-KoLLM-13B-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v2")
model = AutoModelForCausalLM.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PracticeLLM/Custom-KoLLM-13B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PracticeLLM/Custom-KoLLM-13B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PracticeLLM/Custom-KoLLM-13B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v2

SGLang

How to use PracticeLLM/Custom-KoLLM-13B-v2 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 "PracticeLLM/Custom-KoLLM-13B-v2" \
    --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": "PracticeLLM/Custom-KoLLM-13B-v2",
		"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 "PracticeLLM/Custom-KoLLM-13B-v2" \
        --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": "PracticeLLM/Custom-KoLLM-13B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PracticeLLM/Custom-KoLLM-13B-v2 with Docker Model Runner:
```
docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v2
```

Custom-KoLLM-13B-v2 / README.md

kyujinpy

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	---
	language:
	- ko
	datasets:
	- kyujinpy/KOR-Orca-Platypus-kiwi
	library_name: transformers
	pipeline_tag: text-generation
	license: cc-by-nc-sa-4.0
	---

	# ⭐My custom LLM 13B⭐

	## Model Details
	Model Developers
	- Kyujin Han (kyujinpy)

	Model Architecture
	- My custom LLM 13B is an auto-regressive language model based on the LLaMA2 transformer architecture.

	Base Model
	- [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b)

	Training Dataset
	- [kyujinpy/KOR-Orca-Platypus-kiwi](https://huggingface.co/datasets/kyujinpy/KOR-Orca-Platypus-kiwi).

	---
	# Model comparisons
	> Ko-LLM leaderboard(11/25; [link](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard))

	\| Model \| Average \| Ko-ARC \| Ko-HellaSwag \| Ko-MMLU \| Ko-TruthfulQA \| Ko-CommonGen V2 \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| ⭐My custom LLM 13B-v1⭐ \| 50.19 \| 45.99 \| 56.93 \| 41.78 \| 41.66 \| 64.58 \|
	\| ⭐My custom LLM 13B-v2⭐ \| 48.28 \| 45.73 \| 56.97 \| 38.77 \| 38.75 \| 61.16 \|

	---
	# Model comparisons2
	> AI-Harness evaluation; [link](https://github.com/Beomi/ko-lm-evaluation-harness)

	\| Model \| Copa \| Copa \| HellaSwag \| HellaSwag \| BoolQ \| BoolQ \| Sentineg \| Sentineg \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| \| 0-shot \| 5-shot \| 0-shot \| 5-shot \| 0-shot \| 5-shot \| 0-shot \| 5-shot \|
	\| ⭐My custom LLM 13B-v1⭐ \| 0.7987 \| 0.8269 \| 0.4994 \| 0.5660 \| 0.3343 \| 0.5060 \| 0.6984 \| 0.9723 \|
	\| ⭐My custom LLM 13B-v2⭐ \| 0.7938 \| 0.8209 \| 0.4978 \| 0.4893 \| 0.3343 \| 0.5614 \| 0.6283 \| 0.9773 \|
	\| [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b) \| 0.7768 \| 0.8128 \| 0.4999 \| 0.5127 \| 0.3988 \| 0.7038 \| 0.5870 \| 0.9748 \|

	---
	# Implementation Code
	```python
	### KO-Platypus
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	repo = "PracticeLLM/Custom-KoLLM-13B-v2"
	OpenOrca = AutoModelForCausalLM.from_pretrained(
	repo,
	return_dict=True,
	torch_dtype=torch.float16,
	device_map='auto'
	)
	OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
	```

	---