Instructions to use AIdenU/SOLAR-10.7b-ko-Y24_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIdenU/SOLAR-10.7b-ko-Y24_v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="AIdenU/SOLAR-10.7b-ko-Y24_v0.1")

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

tokenizer = AutoTokenizer.from_pretrained("AIdenU/SOLAR-10.7b-ko-Y24_v0.1")
model = AutoModelForCausalLM.from_pretrained("AIdenU/SOLAR-10.7b-ko-Y24_v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIdenU/SOLAR-10.7b-ko-Y24_v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIdenU/SOLAR-10.7b-ko-Y24_v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIdenU/SOLAR-10.7b-ko-Y24_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AIdenU/SOLAR-10.7b-ko-Y24_v0.1

SGLang

How to use AIdenU/SOLAR-10.7b-ko-Y24_v0.1 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 "AIdenU/SOLAR-10.7b-ko-Y24_v0.1" \
    --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": "AIdenU/SOLAR-10.7b-ko-Y24_v0.1",
		"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 "AIdenU/SOLAR-10.7b-ko-Y24_v0.1" \
        --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": "AIdenU/SOLAR-10.7b-ko-Y24_v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AIdenU/SOLAR-10.7b-ko-Y24_v0.1 with Docker Model Runner:
```
docker model run hf.co/AIdenU/SOLAR-10.7b-ko-Y24_v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BaseModel

upstage/SOLAR-10.7B-v1.0

Model Generation

from transforemrs import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("AIdenU/SOLAR-10.7b-ko-Y24_v0.1", device_map="auto", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("AIdenU/SOLAR-10.7b-ko-Y24_v0.1", use_fast=True)

prompt = [
  {'role': 'system', 'content': '당신은 지시를 매우 잘 따르는 인공지능 비서입니다.'},
  {'role': 'user', 'content': '지렁이도 밟으면 꿈틀하나요?'}
]
outputs = model.generate(
  **tokenizer(
    tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True),
    return_tensors='pt'
  ).to('cuda'),
  max_new_tokens=256,
  temperature=0.2,
  top_p=1,
  do_sample=True
)
print(tokenizer.decode(outputs[0]))

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Safetensors

Model size

11B params

Tensor type

F16

Model tree for AIdenU/SOLAR-10.7b-ko-Y24_v0.1

Quantizations

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SOLAR-10.7b-ko-Y24_v0.1

BaseModel

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