Instructions to use dddsaty/SOLAR_Merge_Adapter_DPO_Orca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dddsaty/SOLAR_Merge_Adapter_DPO_Orca with Transformers:

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

pipe = pipeline("text-generation", model="dddsaty/SOLAR_Merge_Adapter_DPO_Orca")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dddsaty/SOLAR_Merge_Adapter_DPO_Orca")
model = AutoModelForCausalLM.from_pretrained("dddsaty/SOLAR_Merge_Adapter_DPO_Orca")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dddsaty/SOLAR_Merge_Adapter_DPO_Orca with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/dddsaty/SOLAR_Merge_Adapter_DPO_Orca

SGLang

How to use dddsaty/SOLAR_Merge_Adapter_DPO_Orca 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 "dddsaty/SOLAR_Merge_Adapter_DPO_Orca" \
    --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": "dddsaty/SOLAR_Merge_Adapter_DPO_Orca",
		"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 "dddsaty/SOLAR_Merge_Adapter_DPO_Orca" \
        --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": "dddsaty/SOLAR_Merge_Adapter_DPO_Orca",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dddsaty/SOLAR_Merge_Adapter_DPO_Orca with Docker Model Runner:
```
docker model run hf.co/dddsaty/SOLAR_Merge_Adapter_DPO_Orca
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Explanation

Merge two base models using mergekit (slerp)
Apply DPO to the merged model, just an adapter part is saved
merge the adpater and the merged model

Base Model

Training Corpus

Intel/orca_dpo_pairs

Score

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
65.96	63.91	84.58	63.18	51.49	82	50.57

Log

2024.02.05: Initial version Upload
2024.02.10: Readme update

LICENSE
Following the upstage/SOLAR-10.7B-Instruct-v1.0 License

cc-by-nc-4.0

Citation

beomi/OPEN-SOLAR-KO-10.7B

@misc {solar_ko_junbum_2023,
    author       = { {L. Junbum} },
    title        = { Solar-Ko-10.7b },
    year         = 2024,
    url          = { https://huggingface.co/beomi/SOLAR-KO-10.7B },
    publisher    = { Hugging Face }
}

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

@misc{kim2023solar,
      title={SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling}, 
      author={Dahyun Kim and Chanjun Park and Sanghoon Kim and Wonsung Lee and Wonho Song and Yunsu Kim and Hyeonwoo Kim and Yungi Kim and Hyeonju Lee and Jihoo Kim and Changbae Ahn and Seonghoon Yang and Sukyung Lee and Hyunbyung Park and Gyoungjin Gim and Mikyoung Cha and Hwalsuk Lee and Sunghun Kim},
      year={2023},
      eprint={2312.15166},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Downloads last month: 82

Safetensors

Model size

11B params

Tensor type

F16

Model tree for dddsaty/SOLAR_Merge_Adapter_DPO_Orca

Quantizations

1 model

Dataset used to train dddsaty/SOLAR_Merge_Adapter_DPO_Orca

Spaces using dddsaty/SOLAR_Merge_Adapter_DPO_Orca 9

Paper for dddsaty/SOLAR_Merge_Adapter_DPO_Orca

SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

Paper • 2312.15166 • Published Dec 23, 2023 • 61