Instructions to use KISTI-KONI/KONI-Llama3-8B-Instruct-20240729 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KISTI-KONI/KONI-Llama3-8B-Instruct-20240729 with Transformers:

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

pipe = pipeline("text-generation", model="KISTI-KONI/KONI-Llama3-8B-Instruct-20240729")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KISTI-KONI/KONI-Llama3-8B-Instruct-20240729")
model = AutoModelForCausalLM.from_pretrained("KISTI-KONI/KONI-Llama3-8B-Instruct-20240729", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KISTI-KONI/KONI-Llama3-8B-Instruct-20240729 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KISTI-KONI/KONI-Llama3-8B-Instruct-20240729"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KISTI-KONI/KONI-Llama3-8B-Instruct-20240729",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KISTI-KONI/KONI-Llama3-8B-Instruct-20240729

SGLang

How to use KISTI-KONI/KONI-Llama3-8B-Instruct-20240729 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 "KISTI-KONI/KONI-Llama3-8B-Instruct-20240729" \
    --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": "KISTI-KONI/KONI-Llama3-8B-Instruct-20240729",
		"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 "KISTI-KONI/KONI-Llama3-8B-Instruct-20240729" \
        --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": "KISTI-KONI/KONI-Llama3-8B-Instruct-20240729",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KISTI-KONI/KONI-Llama3-8B-Instruct-20240729 with Docker Model Runner:
```
docker model run hf.co/KISTI-KONI/KONI-Llama3-8B-Instruct-20240729
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

1. Model Description

KONI (KISTI Open Natural Intelligence) is a specialized large language model (LLM) developed by the Korea Institute of Science and Technology Information (KISTI). This model is specifically designed for science and technology, making it highly effective for tasks in these fields.

2. Key Features

Specialized in Science and Technology: The model is explicitly trained on a vast and specialized corpus of scientific and technological data.
Enhanced Performance: This version of KONI shows significantly improved performance compared to its initial release in December, 2023.
Base Model: The base model for KONI-Llama3-8B-Instruct-20240729 is KONI-Llama3-8B-Merged-20240724, which is a merger of Meta-Llama-3-8B and KISTI-KONI/KONI-Llama3-8B-20240630
Alignment: SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization) are applied

3. Data

Approximately 11k SFT data and 7k DPO data are used.
SFT Data: The SFT data includes both internally generated data and publicly available data on Hugging Face, translated into Korean where necessary.
DPO Data: The DPO data consists of translated and curated data from argilla/dpo-mix-7k.

4. Benchmark Results

Results in LogicKor* are as follows:

Metric	Score
Reasoning	6.57
Math	8.00
Writing	8.92
Coding	8.85
Comprehension	9.85
Grammar	7.07
Single-turn	8.42
Multi-turn	8.00
Overall	8.21
Our model demonstrates the best performance among publicly available 8B models on the LogicKor leaderboard as of 2024.07.30.

5. How to use the model

import transformers
import torch

model_id = "KISTI-KONI/KONI-Llama3-8B-Instruct-20240729"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

pipeline.model.eval()

instruction = "안녕? 너는 누구야?"

messages = [
   {"role": "user", "content": f"{instruction}"}
    ]

prompt = pipeline.tokenizer.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=2048,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.7,
    top_p=0.9
)

print(outputs[0]["generated_text"][len(prompt):])

안녕하세요! 저는 KONI, 여러분의 질문에 답하고 정보를 제공하는 인공지능입니다. 저는 어떤 정보를 제공해 드릴까요?

6. Citation

Language Model

@article{KISTI-KONI/KONI-Llama3-8B-Instruct-20240729,
  title={KISTI-KONI/KONI-Llama3-8B-Instruct-20240729},
  author={KISTI},
  year={2024},
  url={https://huggingface.co/KISTI-KONI/KONI-Llama3-8B-Instruct-20240729}
}

7. Contributors

KISTI, Large-scale AI Research Group

8. Special Thanks

@beomi
@kuotient
KyungTae Lim

8. Acknowledgement

This research was supported by Korea Institute of Science and Technology Information(KISTI).
This work was supported by the National Supercomputing Center with supercomputing resources including technical support (KISTI).

9. References

Downloads last month: 41

Safetensors

Model size

8B params

Tensor type

F32

Model tree for KISTI-KONI/KONI-Llama3-8B-Instruct-20240729

Merges

27 models

Quantizations

4 models

Collection including KISTI-KONI/KONI-Llama3-8B-Instruct-20240729

KONI

Collection

Text-only LLMs • 9 items • Updated Sep 3, 2025