Instructions to use AIdenU/SOLAR-10.7b-ko-Y24_v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIdenU/SOLAR-10.7b-ko-Y24_v1.0 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_v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("AIdenU/SOLAR-10.7b-ko-Y24_v1.0")
model = AutoModelForCausalLM.from_pretrained("AIdenU/SOLAR-10.7b-ko-Y24_v1.0", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIdenU/SOLAR-10.7b-ko-Y24_v1.0 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_v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIdenU/SOLAR-10.7b-ko-Y24_v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIdenU/SOLAR-10.7b-ko-Y24_v1.0

SGLang

How to use AIdenU/SOLAR-10.7b-ko-Y24_v1.0 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 "AIdenU/SOLAR-10.7b-ko-Y24_v1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIdenU/SOLAR-10.7b-ko-Y24_v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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_v1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIdenU/SOLAR-10.7b-ko-Y24_v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIdenU/SOLAR-10.7b-ko-Y24_v1.0 with Docker Model Runner:
```
docker model run hf.co/AIdenU/SOLAR-10.7b-ko-Y24_v1.0
```
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_v1.0", device_map="auto", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("AIdenU/SOLAR-10.7b-ko-Y24_v1.0", 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

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