Instructions to use denial07/Qwen2-72B-Instruct-kor-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use denial07/Qwen2-72B-Instruct-kor-dpo with Transformers:

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

pipe = pipeline("text-generation", model="denial07/Qwen2-72B-Instruct-kor-dpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("denial07/Qwen2-72B-Instruct-kor-dpo")
model = AutoModelForCausalLM.from_pretrained("denial07/Qwen2-72B-Instruct-kor-dpo", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use denial07/Qwen2-72B-Instruct-kor-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "denial07/Qwen2-72B-Instruct-kor-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "denial07/Qwen2-72B-Instruct-kor-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/denial07/Qwen2-72B-Instruct-kor-dpo

SGLang

How to use denial07/Qwen2-72B-Instruct-kor-dpo 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 "denial07/Qwen2-72B-Instruct-kor-dpo" \
    --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": "denial07/Qwen2-72B-Instruct-kor-dpo",
		"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 "denial07/Qwen2-72B-Instruct-kor-dpo" \
        --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": "denial07/Qwen2-72B-Instruct-kor-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use denial07/Qwen2-72B-Instruct-kor-dpo with Docker Model Runner:
```
docker model run hf.co/denial07/Qwen2-72B-Instruct-kor-dpo
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is an improved version for Korean, based on the Qwen2-72B-Instruct model.

LogicKor Benchmark (24.07.31)

The following benchmark ranks are based on 1-shot evaluation.

Rank	Model	Reasoning	Math	Writing	Coding	Understanding	Grammar	Singleturn	Multiturn	Total	Parameters
1	openai/gpt-4o-2024-05-13	9.21	8.71	9.64	9.78	9.64	9.50	9.33	9.50	9.41	?
2	anthropic/claude-3-5-sonnet-20240620	8.64	8.42	9.85	9.78	9.92	9.21	9.26	9.35	9.30	?
4	mistralai/Mistral-Large-Instruct-2407	9.71	9.07	9.57	9.92	9.92	6.78	9.19	9.14	9.16	123B
8	meta-llama/Meta-Llama-3.1-405B-Instruct-FP8	8.78	7.14	9.28	9.64	9.64	8.57	8.97	8.71	8.84	405B
9	`denial07/Qwen2-72B-Instruct-kor-dpo`	8.85	8.21	9.14	9.71	9.64	7.21	8.88	8.71	8.79	72B
10	Qwen/Qwen2-72B-Instruct	8.00	8.14	9.07	9.85	9.78	7.28	8.61	8.76	8.69	72B
11	google/gemini-1.5-pro-001	7.00	8.00	9.57	8.85	9.35	8.64	8.61	8.52	8.57	?

KMMLU Benchmark

HAERAE-HUB/KMMLU benchmark accuracy score.

Category	Qwen2-72B-it-kor-dpo	Qwen2-72B-it	Mistral-Large-it-2407	Questions
HUMSS	0.63	0.63	0.62	5130
STEM	0.59	0.59	0.57	9900
Applied Science	0.56	0.56	0.54	11600
Other	0.58	0.58	0.54	8400
Overall Accuracy	0.58	0.58	0.56	35030

Downloads last month: 10

Safetensors

Model size

73B params

Tensor type

BF16

Model tree for denial07/Qwen2-72B-Instruct-kor-dpo

Quantizations

1 model