Instructions to use jingyeom/SOLAR_KO_1.3_deup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jingyeom/SOLAR_KO_1.3_deup with Transformers:

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

pipe = pipeline("text-generation", model="jingyeom/SOLAR_KO_1.3_deup")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jingyeom/SOLAR_KO_1.3_deup")
model = AutoModelForCausalLM.from_pretrained("jingyeom/SOLAR_KO_1.3_deup")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jingyeom/SOLAR_KO_1.3_deup with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jingyeom/SOLAR_KO_1.3_deup"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jingyeom/SOLAR_KO_1.3_deup",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jingyeom/SOLAR_KO_1.3_deup

SGLang

How to use jingyeom/SOLAR_KO_1.3_deup 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 "jingyeom/SOLAR_KO_1.3_deup" \
    --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": "jingyeom/SOLAR_KO_1.3_deup",
		"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 "jingyeom/SOLAR_KO_1.3_deup" \
        --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": "jingyeom/SOLAR_KO_1.3_deup",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jingyeom/SOLAR_KO_1.3_deup with Docker Model Runner:
```
docker model run hf.co/jingyeom/SOLAR_KO_1.3_deup
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model

base_model : beomi/OPEN-SOLAR-KO-10.7B

Dataset

공개 데이터 수집
Deduplicating Training Data Makes Language Models Better 알고리즘 활용

Code

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "jingyeom/SOLAR_KO_1.3_deup"
model = AutoModelForCausalLM.from_pretrained(
        model_name,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

Benchmark

Ko-LLM-Leaderboard (24.01.29 기준 리더보드 11등)

Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
53.63	52.65	60.92	50.9	45.14	58.56

Downloads last month: 192

Safetensors

Model size

11B params

Tensor type

F32

Model tree for jingyeom/SOLAR_KO_1.3_deup

Quantizations

1 model