Instructions to use leejaymin/etri-ones-solar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leejaymin/etri-ones-solar with Transformers:

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

pipe = pipeline("text-generation", model="leejaymin/etri-ones-solar")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("leejaymin/etri-ones-solar")
model = AutoModelForCausalLM.from_pretrained("leejaymin/etri-ones-solar")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use leejaymin/etri-ones-solar with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/leejaymin/etri-ones-solar

SGLang

How to use leejaymin/etri-ones-solar 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 "leejaymin/etri-ones-solar" \
    --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": "leejaymin/etri-ones-solar",
		"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 "leejaymin/etri-ones-solar" \
        --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": "leejaymin/etri-ones-solar",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use leejaymin/etri-ones-solar with Docker Model Runner:
```
docker model run hf.co/leejaymin/etri-ones-solar
```

etri-ones-solar

Model Details

Model Developers

the model is fine-tuned by open instruction dataset

Model Architecture

this model is an auto-regressive language model based on the solar transformer architecture.

Base Model

solar https://huggingface.co/upstage/SOLAR-10.7B-v1.0

Training Dataset

Model comparisons1

comming soon

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
[...your_model_name...]	NaN	NaN	NaN	NaN	NaN	NaN

Model comparisons2

AI-Harness evaluation; link

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
[...your_model_name...]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Implementation Code

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "[...your_model_repo...]"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)

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