Instructions to use maywell/kiqu-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maywell/kiqu-70b with Transformers:

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

pipe = pipeline("text-generation", model="maywell/kiqu-70b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maywell/kiqu-70b")
model = AutoModelForCausalLM.from_pretrained("maywell/kiqu-70b")
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

vLLM

How to use maywell/kiqu-70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maywell/kiqu-70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maywell/kiqu-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maywell/kiqu-70b

SGLang

How to use maywell/kiqu-70b 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 "maywell/kiqu-70b" \
    --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": "maywell/kiqu-70b",
		"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 "maywell/kiqu-70b" \
        --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": "maywell/kiqu-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maywell/kiqu-70b with Docker Model Runner:
```
docker model run hf.co/maywell/kiqu-70b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

kiqu-70b (Arena Leaderboard)

kiqu-70b is a SFT+DPO trained model based on Miqu-70B-Alpaca-DPO using Korean datasets.

Since this model is finetune of miqu-1-70b using it on commercial purposes is at your own risk. — leaked early version Mistral-Medium

본 모델 kiqu-70b는 Miqu-70B-Alpaca-DPO 모델을 기반으로 한국어 데이터셋을 사용하여 SFT+DPO 훈련을 진행하여 제작되었습니다.

베이스 모델인 miqu-1-70b 모델이 미스트랄-미디움의 초기 유출 버전이기에 상업적 사용에 대한 risk는 본인에게 있습니다.

Beside that this model follows cc-by-sa-4.0

본 모델 자체로서는 cc-by-sa-4.0을 따릅니다.

Model Details

Base Model
miqu-1-70b (Early Mistral-Medium)

Instruction format

It follows Mistral format. Giving few-shots to model is highly recommended

본 모델은 미스트랄 포맷을 따릅니다. few-shot 사용을 적극 권장합니다.

[INST] {instruction}
[/INST] {output}

Multi-shot

[INST] {instruction}
[/INST] {output}

[INST] {instruction}
[/INST] {output}

[INST] {instruction}
[/INST] {output}
.
.
.

Recommended Template - 1-shot with system prompt

너는 kiqu-70B라는 한국어에 특화된 언어모델이야. 깔끔하고 자연스럽게 대답해줘!
[INST] 안녕?
[/INST] 안녕하세요! 무엇을 도와드릴까요? 질문이나 궁금한 점이 있다면 언제든지 말씀해주세요.

[INST] {instruction}
[/INST]

Trailing space after [/INST] can affect models performance in significant margin. So, when doing inference it is recommended to not include trailing space in chat template.

[/INST] 뒤에 띄어쓰기는 모델 성능에 유의미한 영향을 미칩니다. 따라서, 인퍼런스(추론)과정에서는 챗 템플릿에 띄어쓰기를 제외하는 것을 적극 권장합니다.