Instructions to use davidkim205/komt-solar-10.7b-sft-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidkim205/komt-solar-10.7b-sft-v5 with Transformers:

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

pipe = pipeline("text-generation", model="davidkim205/komt-solar-10.7b-sft-v5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("davidkim205/komt-solar-10.7b-sft-v5")
model = AutoModelForCausalLM.from_pretrained("davidkim205/komt-solar-10.7b-sft-v5", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davidkim205/komt-solar-10.7b-sft-v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davidkim205/komt-solar-10.7b-sft-v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidkim205/komt-solar-10.7b-sft-v5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davidkim205/komt-solar-10.7b-sft-v5

SGLang

How to use davidkim205/komt-solar-10.7b-sft-v5 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 "davidkim205/komt-solar-10.7b-sft-v5" \
    --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": "davidkim205/komt-solar-10.7b-sft-v5",
		"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 "davidkim205/komt-solar-10.7b-sft-v5" \
        --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": "davidkim205/komt-solar-10.7b-sft-v5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davidkim205/komt-solar-10.7b-sft-v5 with Docker Model Runner:
```
docker model run hf.co/davidkim205/komt-solar-10.7b-sft-v5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

komt : korean multi task instruction tuning model

Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities. However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively. This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs).

Model Details

Model Developers : davidkim(changyeon kim)
Repository : https://github.com/davidkim205/komt(will be updated soon.)
base mode : Edentns/DataVortexS-10.7B-dpo-v1.11
dataset : comp-341k(will be updated soon.)

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Safetensors

Model size

11B params

Tensor type

BF16

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