Instructions to use TwinDoc/RedWhale-tv-10.8B-sft-g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TwinDoc/RedWhale-tv-10.8B-sft-g with Transformers:

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

pipe = pipeline("text-generation", model="TwinDoc/RedWhale-tv-10.8B-sft-g")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TwinDoc/RedWhale-tv-10.8B-sft-g")
model = AutoModelForCausalLM.from_pretrained("TwinDoc/RedWhale-tv-10.8B-sft-g")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TwinDoc/RedWhale-tv-10.8B-sft-g with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TwinDoc/RedWhale-tv-10.8B-sft-g"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TwinDoc/RedWhale-tv-10.8B-sft-g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TwinDoc/RedWhale-tv-10.8B-sft-g

SGLang

How to use TwinDoc/RedWhale-tv-10.8B-sft-g 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 "TwinDoc/RedWhale-tv-10.8B-sft-g" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TwinDoc/RedWhale-tv-10.8B-sft-g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "TwinDoc/RedWhale-tv-10.8B-sft-g" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TwinDoc/RedWhale-tv-10.8B-sft-g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TwinDoc/RedWhale-tv-10.8B-sft-g with Docker Model Runner:
```
docker model run hf.co/TwinDoc/RedWhale-tv-10.8B-sft-g
```

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Model Description

한국어 LLM 평가 데이터셋인 kollm 을 활용하여 Supervised Fine-Tuning(a.k.a SFT) 학습한 모델입니다. 학습 데이터셋은 KoAlpaca-v1.1, kollm_kmmlu, korean-parallel-corpora, kobest_sentineg 와 같은 오픈 데이터셋으로 구성되어 있습니다. 데이터에 대한 상세한 설명은 Train Datasets 링크를 참고해주세요.

About the Model

Name: TwinDoc/RedWhale-tv-10.8B-sft-g
Finetuned from model: TwinDoc/RedWhale-tv-10.8B-v1.0
Train Datasets: davidkim205/kollm-converations
Developed by: 애자일소다 (AGILESODA)
Model type: llama
Language(s) (NLP): 한국어, 영어
License: cc-by-nc-sa-4.0
train setting
- Lora r, alpha : 64, 16
- Dtype : bf16
- Epoch : 1
- Learning rate : 2e-4
- Global batch : 128
- Context length : 1024
inference setting
- BOS id : 1
- EOS id : 2
- Top-p : 0.95
- Temperature : 0.01

prompt template

A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: {input}
Assistant: {output}

License

The content of this project, created by AGILESODA, is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Citation

@misc{vo2024redwhaleadaptedkoreanllm,
      title={RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining}, 
      author={Anh-Dung Vo and Minseong Jung and Wonbeen Lee and Daewoo Choi},
      year={2024},
      eprint={2408.11294},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.11294}, 
}

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