Instructions to use nlpai-lab/KULLM3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nlpai-lab/KULLM3 with Transformers:

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

pipe = pipeline("text-generation", model="nlpai-lab/KULLM3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nlpai-lab/KULLM3")
model = AutoModelForCausalLM.from_pretrained("nlpai-lab/KULLM3")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nlpai-lab/KULLM3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nlpai-lab/KULLM3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nlpai-lab/KULLM3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nlpai-lab/KULLM3

SGLang

How to use nlpai-lab/KULLM3 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 "nlpai-lab/KULLM3" \
    --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": "nlpai-lab/KULLM3",
		"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 "nlpai-lab/KULLM3" \
        --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": "nlpai-lab/KULLM3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nlpai-lab/KULLM3 with Docker Model Runner:
```
docker model run hf.co/nlpai-lab/KULLM3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

KULLM3

Introducing KULLM3, a model with advanced instruction-following and fluent chat abilities. It has shown remarkable performance in instruction-following, speficially by closely following gpt-3.5-turbo.
To our knowledge, It is one of the best publicly opened Korean-speaking language models.

For details, visit the KULLM repository

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub.

Developed by: NLP&AI Lab
Language(s) (NLP): Korean, English
License: Apache 2.0
Finetuned from model: upstage/SOLAR-10.7B-v1.0

Example code

Install Dependencies

pip install torch transformers==4.38.2 accelerate

In transformers>=4.39.0, generate() does not work well. (as of 2024.4.4.)

Python code

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

MODEL_DIR = "nlpai-lab/KULLM3"
model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, torch_dtype=torch.float16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

s = "고려대학교에 대해서 알고 있니?"
conversation = [{'role': 'user', 'content': s}]
inputs = tokenizer.apply_chat_template(
    conversation,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors='pt').to("cuda")
_ = model.generate(inputs, streamer=streamer, max_new_tokens=1024)

# 네, 고려대학교에 대해 알고 있습니다. 고려대학교는 대한민국 서울에 위치한 사립 대학교로, 1905년에 설립되었습니다. 이 대학교는 한국에서 가장 오래된 대학 중 하나로, 다양한 학부 및 대학원 프로그램을 제공합니다. 고려대학교는 특히 법학, 경제학, 정치학, 사회학, 문학, 과학 분야에서 높은 명성을 가지고 있습니다. 또한, 스포츠 분야에서도 활발한 활동을 보이며, 대한민국 대학 스포츠에서 중요한 역할을 하고 있습니다. 고려대학교는 국제적인 교류와 협력에도 적극적이며, 전 세계 다양한 대학과의 협력을 통해 글로벌 경쟁력을 강화하고 있습니다.

Training Details

Training Data

vicgalle/alpaca-gpt4
Mixed Korean instruction data (gpt-generated, hand-crafted, etc)
About 66000+ examples used totally

Training Procedure

Trained with fixed system prompt below.

당신은 고려대학교 NLP&AI 연구실에서 만든 AI 챗봇입니다.
당신의 이름은 'KULLM'으로, 한국어로는 '구름'을 뜻합니다.
당신은 비도덕적이거나, 성적이거나, 불법적이거나 또는 사회 통념적으로 허용되지 않는 발언은 하지 않습니다.
사용자와 즐겁게 대화하며, 사용자의 응답에 가능한 정확하고 친절하게 응답함으로써 최대한 도와주려고 노력합니다.
질문이 이상하다면, 어떤 부분이 이상한지 설명합니다. 거짓 정보를 발언하지 않도록 주의합니다.

Evaluation

Evaluation details such as testing data, metrics are written in github.
Without system prompt used in training phase, KULLM would show lower performance than expect.

Results

Citation

@misc{kullm,
  author = {NLP & AI Lab and Human-Inspired AI research},
  title = {KULLM: Korea University Large Language Model Project},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/nlpai-lab/kullm}},
}

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Safetensors

Model size

11B params

Tensor type

F16

Model tree for nlpai-lab/KULLM3

Base model

upstage/SOLAR-10.7B-v1.0

Finetuned

(42)

this model

Adapters

3 models

Finetunes

2 models

Quantizations

7 models

nlpai-lab
/

KULLM3