kosaul_v0.2

Model Details

KoSaul-8B

KoSaul-8B 모델은 Open-ko-llama3-8B 모델을 바탕으로 Continue learning 을 통해 만들어진 모델입니다.

A6000 * 3, Deepspeed off-load를 이용해 batch size를 극대화 시켰습니다.

Model developers Ingeol Baek

Datasets

• 국가법령포털 Open API 크롤링 데이터
• AI-hub 법률지식베이스
• AI-hub 의료,법률 전문 서적 말뭉치

Hyperparameters

• Batch size 96
• context length 1024
• Opotimizer Adamw
• LR 5e-5
• Warmup min LR 1e-6
• Zero Stage3 off-load

Perplexity 법령 데이터를 바탕으로 평가를 진행했습니다.

• KoSaul-8B - 2.649
• Open-Llama3-8B (beomi/Llama-3-Open-Ko-8B) - 3.529
• Open-Llama2-7B (beomi/llama-2-ko-7b) - 3.393
• Solar-10.7B (chihoonlee10/T3Q-ko-solar-dpo-v1.0) - 3.161
• EEVE-10.8B (yanolja/EEVE-Korean-Instruct-10.8B-v1.0) - 3.505
• KULLM3 (nlpai-lab/KULLM3) - 2.903
• MLP-KTLim (MLP-KTLim/Bllossom) - 4.385

Model Architecture Llama 3 is an auto-regressive language model

Model Release Date 2024.05.08.

License Llama3 License: https://llama.meta.com/llama3/license

Responsibility & Safety

We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.

Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.

Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.

As part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.

Responsible release

In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.

Misuse

If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at https://llama.meta.com/llama3/use-policy/.

Ethical Considerations and Limitations

The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.

But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.

Please see the Responsible Use Guide available at http://llama.meta.com/responsible-use-guide