Instructions to use ifuseok/sft-solar-10.7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ifuseok/sft-solar-10.7b-v1 with Transformers:

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

pipe = pipeline("text-generation", model="ifuseok/sft-solar-10.7b-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ifuseok/sft-solar-10.7b-v1")
model = AutoModelForCausalLM.from_pretrained("ifuseok/sft-solar-10.7b-v1", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ifuseok/sft-solar-10.7b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ifuseok/sft-solar-10.7b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ifuseok/sft-solar-10.7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ifuseok/sft-solar-10.7b-v1

SGLang

How to use ifuseok/sft-solar-10.7b-v1 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 "ifuseok/sft-solar-10.7b-v1" \
    --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": "ifuseok/sft-solar-10.7b-v1",
		"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 "ifuseok/sft-solar-10.7b-v1" \
        --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": "ifuseok/sft-solar-10.7b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ifuseok/sft-solar-10.7b-v1 with Docker Model Runner:
```
docker model run hf.co/ifuseok/sft-solar-10.7b-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Input Models input text only.

Output Models generate text only.

Base Model upstage/SOLAR-10.7B-Instruct-v1.0

Training Dataset

Implementation Code

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "ifuseok/sft-solar-10.7b-v1"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)

Prompt Example

### System:
시스템 메시지 입니다. 
### User:
유저  입니다.
### Assistant
어시스턴트 입니다.

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Safetensors

Model size

11B params

Tensor type

BF16

Model tree for ifuseok/sft-solar-10.7b-v1

Quantizations

1 model

ifuseok
/

sft-solar-10.7b-v1

Implementation Code

Prompt Example

Model tree for ifuseok/sft-solar-10.7b-v1

Datasets used to train ifuseok/sft-solar-10.7b-v1

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