Instructions to use Changgil/K2S3-SOLAR-11b-v3.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Changgil/K2S3-SOLAR-11b-v3.0 with Transformers:

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

pipe = pipeline("text-generation", model="Changgil/K2S3-SOLAR-11b-v3.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Changgil/K2S3-SOLAR-11b-v3.0")
model = AutoModelForMultimodalLM.from_pretrained("Changgil/K2S3-SOLAR-11b-v3.0")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Changgil/K2S3-SOLAR-11b-v3.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Changgil/K2S3-SOLAR-11b-v3.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Changgil/K2S3-SOLAR-11b-v3.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Changgil/K2S3-SOLAR-11b-v3.0

SGLang

How to use Changgil/K2S3-SOLAR-11b-v3.0 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 "Changgil/K2S3-SOLAR-11b-v3.0" \
    --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": "Changgil/K2S3-SOLAR-11b-v3.0",
		"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 "Changgil/K2S3-SOLAR-11b-v3.0" \
        --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": "Changgil/K2S3-SOLAR-11b-v3.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Changgil/K2S3-SOLAR-11b-v3.0 with Docker Model Runner:
```
docker model run hf.co/Changgil/K2S3-SOLAR-11b-v3.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Developed by :

K2S3

Model Number:

K2S3-SOLAR-11b-v3.0

Base Model :

upstage/SOLAR-10.7B-v1.0

Training Data

The training data for this model includes the Standard Korean Dictionary, training data from KULLM at Korea University, abstracts of master's and doctoral theses, Korean language samples from AI Hub, alpaca-gpt4-data, and samples from The OpenOrca Dataset.
이 모델의 훈련 데이터에는 표준국어대사전, 고려대학교 KULLM에서 제공한 훈련 데이터, 석사 및 박사학위 논문의 초록, AI Hub에서 제공한 한국어 데이터 샘플, alpaca-gpt4-data, 그리고 OpenOrca Dataset에서 제공한 샘플들이 포함됩니다.

Training Method

This model was fine-tuned on the "upstage/SOLAR-10.7B-v1.0" base model using a full parameter tuning method with SFT (Supervised Fine-Tuning).
이 모델은 "upstage/SOLAR-10.7B-v1.0" 기반 모델을 SFT를 사용하여 전체 파라미터 조정 방법으로 미세조정되었습니다.

Hardware

Hardware: Utilized two A100 (80G*2EA) GPUs for training.
Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp.
이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다.

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Safetensors

Model size

11B params

Tensor type

F16

Model tree for Changgil/K2S3-SOLAR-11b-v3.0

Quantizations

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